Case study
… title: “ Case Study 1 - Analysis of restaurent revies based on Yelp Dataset “ date : 2019-11-15 tags : [NLP,Text Analysis,Machine learning] header: image: “/images/light.jpg” excerpt: “Data cleaning,Schema creation,Machine learning” mathjax: “true” …
# import data.
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import re
df= pd.read_csv('/Users/ali/review.csv',header=0, encoding='latin1')
Topics Classification.
from sklearn.metrics import roc_curve, auc,precision_recall_curve
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
import seaborn as sns
import numpy as np
Topic Classification without Gridsearch
Multinomial Naive Bayes
# define a function to evaluate NB model.
def NB_topic_classification(train_file_name):
# import dataset.
categ_data = pd.read_csv(train_file_name , header = 0)
classes = categ_data.label.values
classes = [i.split(",") for i in classes]
# one hot encoding
mlb = MultiLabelBinarizer()
Y = mlb.fit_transform(classes)
# split dataset into traning and testing.
X_train, X_test, Y_train, Y_test = train_test_split(\
categ_data.review, Y, test_size=0.3, random_state=0)
# create a pipeline to vectorize and classify.
classifier = Pipeline([
('tfidf', TfidfVectorizer(stop_words="english",\
min_df=2)),
('clf', OneVsRestClassifier(MultinomialNB(alpha = 0.5 )))])
# train model with training part of the datastet.
classifier.fit(X_train, Y_train)
# predict testing data and store them
predicted = classifier.predict(X_test)
predicted.shape
# compare predicted with the ground truth and report performance.
print(classification_report\
(Y_test, predicted, target_names=mlb.classes_))
NB_topic_classification("label restaurant data.csv" )
precision recall f1-score support
ambience 0.95 0.29 0.44 140
anecdotes/miscellaneous 0.74 0.56 0.64 342
food 0.83 0.72 0.77 366
price 0.93 0.36 0.52 102
service 0.86 0.50 0.64 173
micro avg 0.82 0.55 0.66 1123
macro avg 0.86 0.49 0.60 1123
weighted avg 0.83 0.55 0.65 1123
samples avg 0.62 0.58 0.59 1123
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels.
'precision', 'predicted', average, warn_for)
Support Vector Machine
# This function is to evaluate SVM model.
# The code is referenced from the NB model eveluation above;
# however, the model has changed to SVM in the pipeline definition.
def SVM_topic_classification(train_file_name):
categ_data = pd.read_csv(train_file_name , header = 0)
classes = categ_data.label.values
classes = [i.split(",") for i in classes]
mlb = MultiLabelBinarizer()
Y = mlb.fit_transform(classes)
X_train, X_test, Y_train, Y_test = train_test_split(\
categ_data.review, Y, test_size=0.3, random_state=0)
classifier = Pipeline([
('tfidf', TfidfVectorizer(stop_words="english",\
min_df=2)),
('clf', OneVsRestClassifier(LinearSVC(C = 1)))])
classifier.fit(X_train, Y_train)
predicted = classifier.predict(X_test)
predicted.shape
print(classification_report\
(Y_test, predicted, target_names=mlb.classes_))
SVM_topic_classification("label restaurant data.csv")
precision recall f1-score support
ambience 0.87 0.54 0.66 140
anecdotes/miscellaneous 0.71 0.73 0.72 342
food 0.85 0.80 0.82 366
price 0.88 0.74 0.80 102
service 0.85 0.70 0.77 173
micro avg 0.80 0.73 0.76 1123
macro avg 0.83 0.70 0.76 1123
weighted avg 0.81 0.73 0.76 1123
samples avg 0.74 0.73 0.72 1123
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels.
'precision', 'predicted', average, warn_for)
K-nearest neighbour
# This function is to evaluate KNN model.
# The code is referenced from the NB model eveluation above;
# however, the model has changed to KNN in the pipeline definition.
def KNN_topic_classification(train_file_name):
categ_data = pd.read_csv(train_file_name , header = 0)
classes = categ_data.label.values
classes = [i.split(",") for i in classes]
mlb = MultiLabelBinarizer()
Y = mlb.fit_transform(classes)
X_train, X_test, Y_train, Y_test = train_test_split(\
categ_data.review, Y, test_size=0.3, random_state=0)
classifier = Pipeline([
('tfidf', TfidfVectorizer(stop_words="english",\
min_df=2)),
('clf', OneVsRestClassifier(KNeighborsClassifier(n_neighbors=1)))])
classifier.fit(X_train, Y_train)
predicted = classifier.predict(X_test)
predicted.shape
print(classification_report\
(Y_test, predicted, target_names=mlb.classes_))
KNN_topic_classification("label restaurant data.csv" )
precision recall f1-score support
ambience 0.67 0.11 0.20 140
anecdotes/miscellaneous 0.42 0.87 0.57 342
food 0.73 0.25 0.37 366
price 0.75 0.29 0.42 102
service 0.56 0.23 0.33 173
micro avg 0.49 0.42 0.46 1123
macro avg 0.63 0.35 0.38 1123
weighted avg 0.60 0.42 0.41 1123
samples avg 0.49 0.46 0.46 1123
Topic Classification with Gridsearch
categ_data = pd.read_csv("label restaurant data.csv" , header = 0)
categ_data.head()
classes = categ_data.label.values
classes = [i.split(",") for i in classes]
# implement one hot encoding.
mlb = MultiLabelBinarizer()
Y=mlb.fit_transform(classes)
classes_label = mlb.classes_
ambience = Y[:,0]
food = Y[:,2]
miscellaneous = Y[:,1]
service = Y[:,4]
price = Y[:,3]
Topic Classification using Multinomial Naive Bayes.
def NB_topic_classification(review_data, feature):
X_train, X_test, y_train, y_test = train_test_split(review_data, feature , \
test_size=0.25, random_state=0)
text_clf = Pipeline([('tfidf', TfidfVectorizer()), ('clf', MultinomialNB())])
parameters = {'tfidf__min_df':[1, 2, 3],
'tfidf__max_df': [0.995 , 0.999, 1.0],
'tfidf__stop_words':[None,"english"],
'clf__alpha': [0.5, 1.0, 2.0 , 5.0]}
metric = "f1_macro"
gs_clf = GridSearchCV(text_clf, param_grid=parameters, scoring=metric, cv=5)
gs_clf = gs_clf.fit(X_train, y_train)
for param_name in gs_clf.best_params_:
print("{} : {}".format(param_name , gs_clf.best_params_[param_name]))
print("best f1 score:", gs_clf.best_score_)
clf_alpha = gs_clf.best_params_["clf__alpha"]
tfidf_min_df = gs_clf.best_params_["tfidf__min_df"]
tfidf_max_df = gs_clf.best_params_["tfidf__max_df"]
tfidf_stop_words = gs_clf.best_params_["tfidf__stop_words"]
classifier = Pipeline([
('tfidf', TfidfVectorizer(stop_words=tfidf_stop_words,\
min_df=tfidf_min_df, max_df = tfidf_max_df)),
('clf', MultinomialNB(alpha = clf_alpha ))])
clf = classifier.fit(X_train, y_train)
labels=sorted(np.unique(feature))
labels = list(map(str, labels))
predicted = classifier.predict(X_test)
return predicted
predicted_ambience = NB_topic_classification(categ_data.review, ambience)
predicted_miscellaneous= NB_topic_classification(categ_data.review, miscellaneous)
predicted_food = NB_topic_classification(categ_data.review, food)
predicted_price = NB_topic_classification(categ_data.review, price)
predicted_service = NB_topic_classification(categ_data.review, service)
ification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
clf__alpha : 0.5
tfidf__max_df : 0.995
tfidf__min_df : 3
tfidf__stop_words : english
best f1 score: 0.668024906667693
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
clf__alpha : 0.5
tfidf__max_df : 0.995
tfidf__min_df : 3
tfidf__stop_words : english
best f1 score: 0.7959727136933766
X_train, X_test, y_train, y_test = train_test_split(categ_data.review, Y , \
test_size=0.25, random_state=0)
zip_all = list(zip(predicted_ambience, predicted_miscellaneous, predicted_food, predicted_price, predicted_service))
print(classification_report\
(y_test, np.array(list(zip_all)), target_names=mlb.classes_))
precision recall f1-score support
ambience 0.95 0.30 0.45 121
anecdotes/miscellaneous 0.78 0.56 0.65 289
food 0.86 0.70 0.77 301
price 0.97 0.41 0.58 80
service 0.88 0.56 0.68 145
micro avg 0.85 0.56 0.67 936
macro avg 0.89 0.50 0.63 936
weighted avg 0.86 0.56 0.66 936
samples avg 0.62 0.59 0.60 936
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels.
'precision', 'predicted', average, warn_for)
Topic Classification using Support Vector Machine
def SVM_topic_classification(review_data, feature):
X_train, X_test, y_train, y_test = train_test_split(review_data, feature , \
test_size=0.25, random_state=0)
text_clf = Pipeline([('tfidf', TfidfVectorizer()), ('clf', svm.SVC())])
parameters = {'tfidf__min_df':[1, 2, 3],
'tfidf__max_df': [0.995 , 0.999 , 1.0],
'tfidf__stop_words':[None,"english"],
'clf__C':[ 1, 2, 5],
'clf__kernel':['linear']}
metric = "f1_macro"
gs_clf = GridSearchCV(text_clf, param_grid=parameters, scoring=metric, cv=5)
gs_clf = gs_clf.fit(X_train, y_train)
for param_name in gs_clf.best_params_:
print("{} : {}".format(param_name , gs_clf.best_params_[param_name]))
print("best f1 score:", gs_clf.best_score_)
clf_C = gs_clf.best_params_["clf__C"]
clf_kernel = gs_clf.best_params_["clf__kernel"]
tfidf_min_df = gs_clf.best_params_["tfidf__min_df"]
tfidf_max_df = gs_clf.best_params_["tfidf__max_df"]
tfidf_stop_words = gs_clf.best_params_["tfidf__stop_words"]
classifier = Pipeline([
('tfidf', TfidfVectorizer(stop_words=tfidf_stop_words,\
min_df=tfidf_min_df, max_df = tfidf_max_df)),
('clf', svm.SVC(C = clf_C, kernel = clf_kernel))])
clf = classifier.fit(X_train, y_train)
labels=sorted(np.unique(feature))
labels = list(map(str, labels))
predicted = classifier.predict(X_test)
return predicted
predicted_ambience = SVM_topic_classification(categ_data.review, ambience)
predicted_miscellaneous= SVM_topic_classification(categ_data.review, miscellaneous)
predicted_food = SVM_topic_classification(categ_data.review, food)
predicted_price = SVM_topic_classification(categ_data.review, price)
predicted_service = SVM_topic_classification(categ_data.review, service)
clf__C : 2
clf__kernel : linear
tfidf__max_df : 0.995
tfidf__min_df : 1
tfidf__stop_words : english
best f1 score: 0.8115904458156638
clf__C : 1
clf__kernel : linear
tfidf__max_df : 0.995
tfidf__min_df : 1
tfidf__stop_words : None
best f1 score: 0.7991135102581619
clf__C : 1
clf__kernel : linear
tfidf__max_df : 0.995
tfidf__min_df : 1
tfidf__stop_words : None
best f1 score: 0.8666034900184318
clf__C : 2
clf__kernel : linear
tfidf__max_df : 0.995
tfidf__min_df : 1
tfidf__stop_words : english
best f1 score: 0.8534716551437846
clf__C : 2
clf__kernel : linear
tfidf__max_df : 0.995
tfidf__min_df : 1
tfidf__stop_words : english
best f1 score: 0.8787895157563381
X_train, X_test, y_train, y_test = train_test_split(categ_data.review, Y , \
test_size=0.25, random_state=0)
zip_all = list(zip(predicted_ambience, predicted_miscellaneous, predicted_food, predicted_price, predicted_service))
print(classification_report\
(y_test, np.array(list(zip_all)), target_names=mlb.classes_))
precision recall f1-score support
ambience 0.95 0.30 0.45 121
anecdotes/miscellaneous 0.78 0.56 0.65 289
food 0.86 0.70 0.77 301
price 0.97 0.41 0.58 80
service 0.88 0.56 0.68 145
micro avg 0.85 0.56 0.67 936
macro avg 0.89 0.50 0.63 936
weighted avg 0.86 0.56 0.66 936
samples avg 0.62 0.59 0.60 936
Topic Classification using K-Nearest Neighbour
def KNN_topic_classification(review_data, feature):
X_train, X_test, y_train, y_test = train_test_split(review_data, feature , \
test_size=0.25, random_state=0)
text_clf = Pipeline([('tfidf', TfidfVectorizer()), ('clf', KNeighborsClassifier())])
k_range = list(range(1, 10))
parameters = {'tfidf__min_df':[1, 2, 3],
'tfidf__stop_words':[None,"english"],
'clf__n_neighbors': k_range}
metric = "f1_macro"
gs_clf = GridSearchCV(text_clf, param_grid=parameters, scoring=metric, cv=5)
gs_clf = gs_clf.fit(X_train, y_train)
for param_name in gs_clf.best_params_:
print("{} : {}".format(param_name , gs_clf.best_params_[param_name]))
print("best f1 score:", gs_clf.best_score_)
clf_k = gs_clf.best_params_["clf__n_neighbors"]
tfidf_min_df = gs_clf.best_params_["tfidf__min_df"]
tfidf_stop_words = gs_clf.best_params_["tfidf__stop_words"]
classifier = Pipeline([
('tfidf', TfidfVectorizer(stop_words=tfidf_stop_words,\
min_df=tfidf_min_df)),
('clf', KNeighborsClassifier(n_neighbors= clf_k))])
clf = classifier.fit(X_train, y_train)
labels=sorted(np.unique(feature))
labels = list(map(str, labels))
predicted = classifier.predict(X_test)
return predicted
predicted_ambience = KNN_topic_classification(categ_data.review, ambience)
predicted_miscellaneous= KNN_topic_classification(categ_data.review, miscellaneous)
predicted_food = KNN_topic_classification(categ_data.review, food)
predicted_price = KNN_topic_classification(categ_data.review, price)
predicted_service = KNN_topic_classification(categ_data.review, service)
cation.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
clf__n_neighbors : 1
tfidf__min_df : 1
tfidf__stop_words : None
best f1 score: 0.6425241878899145
clf__n_neighbors : 7
tfidf__min_df : 1
tfidf__stop_words : None
best f1 score: 0.7457548560703786
clf__n_neighbors : 9
tfidf__min_df : 1
tfidf__stop_words : None
best f1 score: 0.787822939909712
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
clf__n_neighbors : 3
tfidf__min_df : 1
tfidf__stop_words : None
best f1 score: 0.7020501053173683
/Users/anishumesh/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
clf__n_neighbors : 9
tfidf__min_df : 1
tfidf__stop_words : None
best f1 score: 0.755653894590898
X_train, X_test, y_train, y_test = train_test_split(categ_data.review, Y , \
test_size=0.25, random_state=0)
zip_all = list(zip(predicted_ambience, predicted_miscellaneous, predicted_food, predicted_price, predicted_service))
print(classification_report\
(y_test, np.array(list(zip_all)), target_names=mlb.classes_))
precision recall f1-score support
ambience 0.95 0.30 0.45 121
anecdotes/miscellaneous 0.78 0.56 0.65 289
food 0.86 0.70 0.77 301
price 0.97 0.41 0.58 80
service 0.88 0.56 0.68 145
micro avg 0.85 0.56 0.67 936
macro avg 0.89 0.50 0.63 936
weighted avg 0.86 0.56 0.66 936
samples avg 0.62 0.59 0.60 936
Feature classification using SVM (Best Performance)
All_review_to_classify = pd.read_csv("/Users/anishumesh/food_reviews.csv")
All_review_to_classify['review'] = All_review_to_classify['text']
All_review_to_classify = All_review_to_classify.review
def feature_classification(reviews):
text_clf = Pipeline([('tfidf', TfidfVectorizer()), ('clf', svm.SVC())])
labels=sorted(np.unique(ambience))
labels = list(map(str, labels))
ambience_classifier = Pipeline([
('tfidf', TfidfVectorizer(stop_words= "english", min_df=1, max_df = 0.995)),
('clf', svm.SVC(C = 2, kernel = 'linear'))])
ambience_clf = ambience_classifier.fit(categ_data.review, ambience)
ambience_predicted = ambience_classifier.predict(reviews)
miscel_classifier = Pipeline([
('tfidf', TfidfVectorizer(stop_words= None, min_df=1, max_df = 0.995)),
('clf', svm.SVC(C = 1, kernel = 'linear'))])
miscel_clf = miscel_classifier.fit(categ_data.review, miscellaneous)
miscel_predicted = miscel_classifier.predict(reviews)
food_classifier = Pipeline([
('tfidf', TfidfVectorizer(stop_words= None, min_df=1, max_df = 0.995)),
('clf', svm.SVC(C = 1, kernel = 'linear'))])
food_clf = food_classifier.fit(categ_data.review, food)
food_predicted = food_classifier.predict(reviews)
price_classifier = Pipeline([
('tfidf', TfidfVectorizer(stop_words= "english", min_df=1, max_df = 0.995)),
('clf', svm.SVC(C = 2, kernel = 'linear'))])
price_clf = price_classifier.fit(categ_data.review, price)
price_predicted = price_classifier.predict(reviews)
service_classifier = Pipeline([
('tfidf', TfidfVectorizer(stop_words= "english", min_df=1, max_df = 0.995)),
('clf', svm.SVC(C = 2, kernel = 'linear'))])
service_clf = service_classifier.fit(categ_data.review, service)
service_predicted = service_classifier.predict(reviews)
combined_classes = np.array(list(zip(ambience_predicted, miscel_predicted, \
food_predicted, price_predicted, service_predicted)))
return combined_classes
combined_classes = feature_classification(All_review_to_classify)
count_classes = np.sum(combined_classes , axis=0)
fig1, ax1 = plt.subplots()
ax1.pie(count_classes, explode= (0, 0, 0.1, 0 , 0) , labels = mlb.classes_ ,autopct='%1.1f%%')
ax1.axis('equal')
(-1.1861625405042877,
1.1041029995557592,
-1.0648191950982024,
1.1631035656679731)
combined_classes
array([[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 1],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 1],
[0, 0, 1, 0, 1],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 1],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 1],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 1],
[0, 0, 1, 0, 0]])
classify
| Ambience | Miscelleneous | Food | Price | Service | |
|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 0 | 0 | 1 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 1 |
| 3 | 0 | 0 | 1 | 0 | 0 |
| 4 | 0 | 0 | 1 | 0 | 0 |
| 5 | 0 | 0 | 1 | 0 | 0 |
| 6 | 0 | 0 | 1 | 0 | 0 |
| 7 | 0 | 0 | 1 | 0 | 0 |
| 8 | 0 | 0 | 1 | 0 | 0 |
| 9 | 0 | 0 | 1 | 0 | 0 |
| 10 | 0 | 0 | 1 | 0 | 0 |
| 11 | 0 | 0 | 1 | 0 | 0 |
| 12 | 0 | 0 | 1 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 0 | 1 | 0 | 1 |
| 15 | 0 | 0 | 1 | 0 | 1 |
| 16 | 0 | 0 | 1 | 0 | 0 |
| 17 | 0 | 0 | 1 | 0 | 0 |
| 18 | 0 | 0 | 1 | 0 | 1 |
| 19 | 0 | 0 | 1 | 0 | 0 |
| 20 | 0 | 0 | 1 | 0 | 0 |
| 21 | 0 | 0 | 1 | 0 | 1 |
| 22 | 0 | 0 | 1 | 0 | 0 |
| 23 | 0 | 0 | 1 | 0 | 1 |
| 24 | 0 | 0 | 1 | 0 | 0 |
classify = pd.DataFrame(list(map(np.ravel, combined_classes)))
classify.columns = ['Ambience', 'Miscelleneous', 'Food', 'Price', 'Service']
classify
All_Reviews = pd.DataFrame(All_review_to_classify)
Final_Reviews = All_Reviews.join(classify)
Final_Reviews['Food Mentioned'] = df['menu_item']
Final_Reviews['Rating'] = df['review_rating']
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyser = SentimentIntensityAnalyzer()
sentiment = Final_Reviews['review'].apply(lambda x: analyser.polarity_scores(x))
Final_Reviews = pd.concat([Final_Reviews,sentiment.apply(pd.Series)],1)
Final_Reviews
| review | Ambience | Miscelleneous | Food | Price | Service | Food Mentioned | Rating | neg | neu | pos | compound | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | I always order the most basic burger whenever ... | 0 | 0 | 1 | 0 | 0 | The Greek Burger,The Mexican Burger,Parmesan T... | 5.0 | 0.019 | 0.709 | 0.271 | 0.9847 |
| 1 | After my second time here I can truly say I r... | 0 | 0 | 1 | 0 | 0 | Mussels and Clams,Cavatelli alla Norcina,Musse... | 5.0 | 0.019 | 0.668 | 0.312 | 0.9994 |
| 2 | First of all their minimum for delivery is on... | 0 | 0 | 0 | 0 | 1 | Tea,Fries,Fries,Fries,Cilantro Lime Salad,Frie... | 5.0 | 0.000 | 0.836 | 0.164 | 0.9827 |
| 3 | Was not disappointed once I was in range and c... | 0 | 0 | 1 | 0 | 0 | Dinner Plate,Wings,Wings,Brunch Special,Wings,... | 5.0 | 0.056 | 0.771 | 0.173 | 0.9352 |
| 4 | Had Croque Monsieur it can come in ham turke... | 0 | 0 | 1 | 0 | 0 | 30 Crepe Xpress,French Onion Soup,Water,15 Cro... | 5.0 | 0.023 | 0.851 | 0.126 | 0.9072 |
| 5 | Delish I used to love this cute French crepes... | 0 | 0 | 1 | 0 | 0 | La Colombienne,La Larzac,La Scandinave,La Bres... | 4.0 | 0.027 | 0.764 | 0.209 | 0.9867 |
| 6 | Had to "do the dirty" while in SoCal I'm veg... | 0 | 0 | 1 | 0 | 0 | The Dirty Corn,The Green Dog,The Green Dog,The... | 4.0 | 0.082 | 0.693 | 0.225 | 0.9401 |
| 7 | I finally got to try this sandwich shop They ... | 0 | 0 | 1 | 0 | 0 | Cannoli,Macaroni Salad,Cannoli,Cannoli,Chicken... | 3.0 | 0.029 | 0.764 | 0.207 | 0.9800 |
| 8 | My favorite tacos ever Everyone I take here I... | 0 | 0 | 1 | 0 | 0 | Steak Picado,Chicarron,Mole Poblano,Quesadilla... | 5.0 | 0.000 | 0.800 | 0.200 | 0.9833 |
| 9 | After a fulfilling day at the LA Flower Distri... | 0 | 0 | 1 | 0 | 0 | Chorizo,Quesadilla,Steak Picado,Quesadilla,Hor... | 5.0 | 0.035 | 0.790 | 0.175 | 0.9855 |
| 10 | Holy moly I just got my socks blown off That ... | 0 | 0 | 1 | 0 | 0 | Skinny Jimmy,Half Bird,Dark,Wings,Batter's Box... | 5.0 | 0.020 | 0.873 | 0.106 | 0.9891 |
| 11 | These sandwiches are better than Cole's There... | 0 | 0 | 1 | 0 | 0 | Egg,Avocado,Grilled Chicken Sandwich,Breakfast... | 5.0 | 0.062 | 0.720 | 0.218 | 0.9771 |
| 12 | This is an update from last year's post I we... | 0 | 0 | 1 | 0 | 0 | Seafood,Beef,Dessert,Seafood,Wagyu Beef,Beef,S... | 5.0 | 0.091 | 0.801 | 0.108 | 0.5771 |
| 13 | Street parking only Very hard to find Paid Lo... | 0 | 0 | 0 | 0 | 0 | Lobster,Lobster,Yellowtail,Toro,Blue Crab,Bay ... | 5.0 | 0.021 | 0.774 | 0.206 | 0.9826 |
| 14 | This place is a hidden gem in the LA area Th... | 0 | 0 | 1 | 0 | 1 | Las Jamburgesas,Chancluditas (2 Pcs) | 5.0 | 0.018 | 0.771 | 0.212 | 0.9956 |
| 15 | The Food The food here is very good The past... | 0 | 0 | 1 | 0 | 1 | Apple Sauce,Cole Slaw,Chili,Pastrami,Chili Che... | 5.0 | 0.043 | 0.795 | 0.162 | 0.9824 |
| 16 | Visiting the LA area for a conference I gave ... | 0 | 0 | 1 | 0 | 0 | Egg and Cheese,Cobb Salad,Anti - Flu Juice,Ita... | 5.0 | 0.018 | 0.618 | 0.364 | 0.9960 |
| 17 | 3 5 stars Parking street parkingFood ordered... | 0 | 0 | 1 | 0 | 0 | Chilaquiles,Huevos Rancheros,Enchiladas de Mol... | 3.0 | 0.035 | 0.808 | 0.157 | 0.6529 |
| 18 | Came here with my family after many years crav... | 0 | 0 | 1 | 0 | 1 | Chicken,Beef,Supreme,Pepperoni,Chicken,Beef,Su... | 5.0 | 0.007 | 0.821 | 0.172 | 0.9916 |
| 19 | The lemon herb chicken sandwich What is in i... | 0 | 0 | 1 | 0 | 0 | Lemon Herb Chicken Sandwich,Tuna Sandwich,Lemo... | 5.0 | 0.000 | 0.910 | 0.090 | 0.7217 |
| 20 | Best sushi and sashimi near Los Angeles Price ... | 0 | 0 | 1 | 0 | 0 | Sashimi,Rice,California Roll,Sashimi,Rice,Soup... | 4.0 | 0.000 | 0.766 | 0.234 | 0.7469 |
| 21 | This review is long overdue Hands down the be... | 0 | 0 | 1 | 0 | 1 | Simple | 5.0 | 0.040 | 0.764 | 0.196 | 0.9830 |
| 22 | Was across the way at the coffee shop and want... | 0 | 0 | 1 | 0 | 0 | Texas BBQ Wrap,The Stack Combo,Mac N Cheese,BB... | 4.0 | 0.033 | 0.847 | 0.121 | 0.9355 |
| 23 | Top notch cafe inside of the cool Sci Arc camp... | 0 | 0 | 1 | 0 | 1 | Turkey Burger,Ghost Burger,Ghost Burger,Black ... | 5.0 | 0.000 | 0.760 | 0.240 | 0.9735 |
| 24 | I had Semi booked because the brunch pictures ... | 0 | 0 | 1 | 0 | 0 | Tomato Bisque,Tomato Bisque,Nitro Cold Brew,Ni... | 4.0 | 0.025 | 0.767 | 0.208 | 0.9972 |
filter = (Final_Reviews['Ambience'] + Final_Reviews['Miscelleneous'] + Final_Reviews['Food'] + Final_Reviews['Price'] + Final_Reviews['Service'] + Final_Reviews['Rating'] + Final_Reviews['compound'] >= 7.5) & (Final_Reviews['Rating']>4)
Final_Reviews.loc[filter,"Revisit Intention"] = 'Definitely'
filter = ((Final_Reviews['Ambience'] + Final_Reviews['Miscelleneous'] + Final_Reviews['Food'] + Final_Reviews['Price'] + Final_Reviews['Service'] + Final_Reviews['Rating'] + Final_Reviews['compound'] >= 6.5 )& (Final_Reviews['Ambience'] + Final_Reviews['Miscelleneous'] + Final_Reviews['Food'] + Final_Reviews['Price'] + Final_Reviews['Service'] + Final_Reviews['Rating'] + Final_Reviews['compound']<7.5)) & (Final_Reviews['Rating']>4)
Final_Reviews.loc[filter,"Revisit Intention"] = 'Probably'
filter = ((Final_Reviews['Ambience'] + Final_Reviews['Miscelleneous'] + Final_Reviews['Food'] + Final_Reviews['Price'] + Final_Reviews['Service'] + Final_Reviews['Rating'] + Final_Reviews['compound'] >= 4.5 )& (Final_Reviews['Ambience'] + Final_Reviews['Miscelleneous'] + Final_Reviews['Food'] + Final_Reviews['Price'] + Final_Reviews['Service'] + Final_Reviews['Rating'] + Final_Reviews['compound']<6.5)) & (Final_Reviews['Rating']>=3)
Final_Reviews.loc[filter,"Revisit Intention"] = 'May or May Not'
filter = ((Final_Reviews['Ambience'] + Final_Reviews['Miscelleneous'] + Final_Reviews['Food'] + Final_Reviews['Price'] + Final_Reviews['Service'] + Final_Reviews['Rating'] + Final_Reviews['compound'] >= 3.5 )& (Final_Reviews['Ambience'] + Final_Reviews['Miscelleneous'] + Final_Reviews['Food'] + Final_Reviews['Price'] + Final_Reviews['Service'] + Final_Reviews['Rating'] + Final_Reviews['compound']<4.5)) & (Final_Reviews['Rating']>=3)
Final_Reviews.loc[filter,"Revisit Intention"] = 'Probably Not'
filter = ((Final_Reviews['Ambience'] + Final_Reviews['Miscelleneous'] + Final_Reviews['Food'] + Final_Reviews['Price'] + Final_Reviews['Service'] + Final_Reviews['Rating'] + Final_Reviews['compound']<3.5)) & (Final_Reviews['Rating']<3)
Final_Reviews.loc[filter,"Revisit Intention"] = 'Definitely Not'
Final_Reviews
| review | Ambience | Miscelleneous | Food | Price | Service | Food Mentioned | Rating | neg | neu | pos | compound | Revisit Intention | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | I always order the most basic burger whenever ... | 0 | 0 | 1 | 0 | 0 | The Greek Burger,The Mexican Burger,Parmesan T... | 5.0 | 0.019 | 0.709 | 0.271 | 0.9847 | Probably |
| 1 | After my second time here I can truly say I r... | 0 | 0 | 1 | 0 | 0 | Mussels and Clams,Cavatelli alla Norcina,Musse... | 5.0 | 0.019 | 0.668 | 0.312 | 0.9994 | Probably |
| 2 | First of all their minimum for delivery is on... | 0 | 0 | 0 | 0 | 1 | Tea,Fries,Fries,Fries,Cilantro Lime Salad,Frie... | 5.0 | 0.000 | 0.836 | 0.164 | 0.9827 | Probably |
| 3 | Was not disappointed once I was in range and c... | 0 | 0 | 1 | 0 | 0 | Dinner Plate,Wings,Wings,Brunch Special,Wings,... | 5.0 | 0.056 | 0.771 | 0.173 | 0.9352 | Probably |
| 4 | Had Croque Monsieur it can come in ham turke... | 0 | 0 | 1 | 0 | 0 | 30 Crepe Xpress,French Onion Soup,Water,15 Cro... | 5.0 | 0.023 | 0.851 | 0.126 | 0.9072 | Probably |
| 5 | Delish I used to love this cute French crepes... | 0 | 0 | 1 | 0 | 0 | La Colombienne,La Larzac,La Scandinave,La Bres... | 4.0 | 0.027 | 0.764 | 0.209 | 0.9867 | May or May Not |
| 6 | Had to "do the dirty" while in SoCal I'm veg... | 0 | 0 | 1 | 0 | 0 | The Dirty Corn,The Green Dog,The Green Dog,The... | 4.0 | 0.082 | 0.693 | 0.225 | 0.9401 | May or May Not |
| 7 | I finally got to try this sandwich shop They ... | 0 | 0 | 1 | 0 | 0 | Cannoli,Macaroni Salad,Cannoli,Cannoli,Chicken... | 3.0 | 0.029 | 0.764 | 0.207 | 0.9800 | May or May Not |
| 8 | My favorite tacos ever Everyone I take here I... | 0 | 0 | 1 | 0 | 0 | Steak Picado,Chicarron,Mole Poblano,Quesadilla... | 5.0 | 0.000 | 0.800 | 0.200 | 0.9833 | Probably |
| 9 | After a fulfilling day at the LA Flower Distri... | 0 | 0 | 1 | 0 | 0 | Chorizo,Quesadilla,Steak Picado,Quesadilla,Hor... | 5.0 | 0.035 | 0.790 | 0.175 | 0.9855 | Probably |
| 10 | Holy moly I just got my socks blown off That ... | 0 | 0 | 1 | 0 | 0 | Skinny Jimmy,Half Bird,Dark,Wings,Batter's Box... | 5.0 | 0.020 | 0.873 | 0.106 | 0.9891 | Probably |
| 11 | These sandwiches are better than Cole's There... | 0 | 0 | 1 | 0 | 0 | Egg,Avocado,Grilled Chicken Sandwich,Breakfast... | 5.0 | 0.062 | 0.720 | 0.218 | 0.9771 | Probably |
| 12 | This is an update from last year's post I we... | 0 | 0 | 1 | 0 | 0 | Seafood,Beef,Dessert,Seafood,Wagyu Beef,Beef,S... | 5.0 | 0.091 | 0.801 | 0.108 | 0.5771 | Probably |
| 13 | Street parking only Very hard to find Paid Lo... | 0 | 0 | 0 | 0 | 0 | Lobster,Lobster,Yellowtail,Toro,Blue Crab,Bay ... | 5.0 | 0.021 | 0.774 | 0.206 | 0.9826 | May or May Not |
| 14 | This place is a hidden gem in the LA area Th... | 0 | 0 | 1 | 0 | 1 | Las Jamburgesas,Chancluditas (2 Pcs) | 5.0 | 0.018 | 0.771 | 0.212 | 0.9956 | Definitely |
| 15 | The Food The food here is very good The past... | 0 | 0 | 1 | 0 | 1 | Apple Sauce,Cole Slaw,Chili,Pastrami,Chili Che... | 5.0 | 0.043 | 0.795 | 0.162 | 0.9824 | Definitely |
| 16 | Visiting the LA area for a conference I gave ... | 0 | 0 | 1 | 0 | 0 | Egg and Cheese,Cobb Salad,Anti - Flu Juice,Ita... | 5.0 | 0.018 | 0.618 | 0.364 | 0.9960 | Probably |
| 17 | 3 5 stars Parking street parkingFood ordered... | 0 | 0 | 1 | 0 | 0 | Chilaquiles,Huevos Rancheros,Enchiladas de Mol... | 3.0 | 0.035 | 0.808 | 0.157 | 0.6529 | May or May Not |
| 18 | Came here with my family after many years crav... | 0 | 0 | 1 | 0 | 1 | Chicken,Beef,Supreme,Pepperoni,Chicken,Beef,Su... | 5.0 | 0.007 | 0.821 | 0.172 | 0.9916 | Definitely |
| 19 | The lemon herb chicken sandwich What is in i... | 0 | 0 | 1 | 0 | 0 | Lemon Herb Chicken Sandwich,Tuna Sandwich,Lemo... | 5.0 | 0.000 | 0.910 | 0.090 | 0.7217 | Probably |
| 20 | Best sushi and sashimi near Los Angeles Price ... | 0 | 0 | 1 | 0 | 0 | Sashimi,Rice,California Roll,Sashimi,Rice,Soup... | 4.0 | 0.000 | 0.766 | 0.234 | 0.7469 | May or May Not |
| 21 | This review is long overdue Hands down the be... | 0 | 0 | 1 | 0 | 1 | Simple | 5.0 | 0.040 | 0.764 | 0.196 | 0.9830 | Definitely |
| 22 | Was across the way at the coffee shop and want... | 0 | 0 | 1 | 0 | 0 | Texas BBQ Wrap,The Stack Combo,Mac N Cheese,BB... | 4.0 | 0.033 | 0.847 | 0.121 | 0.9355 | May or May Not |
| 23 | Top notch cafe inside of the cool Sci Arc camp... | 0 | 0 | 1 | 0 | 1 | Turkey Burger,Ghost Burger,Ghost Burger,Black ... | 5.0 | 0.000 | 0.760 | 0.240 | 0.9735 | Definitely |
| 24 | I had Semi booked because the brunch pictures ... | 0 | 0 | 1 | 0 | 0 | Tomato Bisque,Tomato Bisque,Nitro Cold Brew,Ni... | 4.0 | 0.025 | 0.767 | 0.208 | 0.9972 | May or May Not |
Final_Reviews.review[15]
"The Food The food here is very good The pastrami literally breaks into a billion pieces as soon as you put it in your mouth The flavor is so subtle and juicy it literally melts I got the number 19 which is the pastrami sandwich with cole slaw and Swiss cheese on rye bread I got two Latkas with cream and apple sauce and the pastrami chili cheese fries The sand which was awesome the cole slaw was amazing Creamy cold and crunchy perfectly complimenting the flavors The latkas were deep fried to perfection And the chili cheese fries were huge Like super huge Had to take left over home The location it's located in the center of west la on Wiltshire Traffic and parking can be a little tough but they have their own parking area wth validation The restaurant is clean and nice The Service as we walked in we were asked how big our party was I was with my mom so just two I was afraid it would take forever to get seated because the place was packed But after a few minutes we were seated and promptly someone came over to take our drink order The Service was nice the lady was very attentive refilled my drink and my mom coffee with out even asking she just knew and it was seamless I didn't even notice it got refilled until she brought it food Amazing The food was starved relatively quickly and once you're done you go to the front and pay there "