# Notebook from Shantanu-Gupta-au16/Data-Science-Portfolio
Path: Text Analytics/Yelp_Reviews_Classification.ipynb

_____no_output_____# YELP REVIEWS CLASSIFICATION (NLP)
# By Shantanu Gupta

_____no_output_____# PROBLEM STATEMENT_____no_output_____- In this project, Natural Language Processing (NLP) strategies will be used to analyze Yelp reviews data
- Number of 'stars' indicate the business rating given by a customer, ranging from 1 to 5
- 'Cool', 'Useful' and 'Funny' indicate the number of cool votes given by other Yelp Users.

_____no_output_____# STEP #0: LIBRARIES IMPORT
_____no_output_____

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline_____no_output_____

# STEP #1: IMPORT DATASET_____no_output_____

#Running or Importing .py Files with Google Colab
from google.colab import drive
drive.mount('/content/drive/')Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount("/content/drive/", force_remount=True).
yelp_df = pd.read_csv('/content/drive/My Drive/app/yelp.csv')
yelp_df_____no_output_____yelp_df.head(10)_____no_output_____yelp_df.tail()_____no_output_____yelp_df.describe()_____no_output_____yelp_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 10 columns):
business_id    10000 non-null object
date           10000 non-null object
review_id      10000 non-null object
stars          10000 non-null int64
text           10000 non-null object
type           10000 non-null object
user_id        10000 non-null object
cool           10000 non-null int64
useful         10000 non-null int64
funny          10000 non-null int64
dtypes: int64(4), object(6)
memory usage: 781.3+ KB

# STEP #2: VISUALIZE DATASET_____no_output_____

# Let's get the length of the messages
yelp_df['length'] = yelp_df['text'].apply(len)
yelp_df.head()_____no_output_____yelp_df['length'].plot(bins=100, kind='hist') _____no_output_____yelp_df.length.describe()_____no_output_____# Let's see the longest message 43952
yelp_df[yelp_df['length'] == 4997]['text'].iloc[0]_____no_output_____# Let's see the shortest message
yelp_df[yelp_df['length'] == 1]['text'].iloc[0]_____no_output_____# Let's see the message with mean length
yelp_df[yelp_df['length'] == 710]['text'].iloc[0]_____no_output_____sns.countplot(y = 'stars', data=yelp_df)_____no_output_____g = sns.FacetGrid(data=yelp_df, col='stars', col_wrap=3)_____no_output_____g = sns.FacetGrid(data=yelp_df, col='stars', col_wrap=5)
g.map(plt.hist, 'length', bins = 20, color = 'r')_____no_output_____# Let's divide the reviews into 1 and 5 stars_____no_output_____yelp_df_1 = yelp_df[yelp_df['stars']==1]_____no_output_____yelp_df_5 = yelp_df[yelp_df['stars']==5]_____no_output_____yelp_df_1_____no_output_____yelp_df_5_____no_output_____yelp_df_1_5 = pd.concat([yelp_df_1 , yelp_df_5])_____no_output_____yelp_df_1_5_____no_output_____yelp_df_1_5.info()<class 'pandas.core.frame.DataFrame'>
Int64Index: 4086 entries, 23 to 9999
Data columns (total 11 columns):
business_id    4086 non-null object
date           4086 non-null object
review_id      4086 non-null object
stars          4086 non-null int64
text           4086 non-null object
type           4086 non-null object
user_id        4086 non-null object
cool           4086 non-null int64
useful         4086 non-null int64
funny          4086 non-null int64
length         4086 non-null int64
dtypes: int64(5), object(6)
memory usage: 383.1+ KB
print( '1-Stars percentage =', (len(yelp_df_1) / len(yelp_df_1_5) )*100,"%")1-Stars percentage = 18.330885952031327 %
print( '5-Stars percentage =', (len(yelp_df_5) / len(yelp_df_1_5) )*100,"%")5-Stars percentage = 81.66911404796868 %
sns.countplot(yelp_df_1_5['stars'], label = "Count") _____no_output_____

# STEP #3: CREATE TESTING AND TRAINING DATASET/DATA CLEANING_____no_output_____# STEP 3.1 EXERCISE: REMOVE PUNCTUATION_____no_output_____

import string
string.punctuation_____no_output_____Test = 'Hello Mr. Future, I am so happy to be learning AI now!!'_____no_output_____Test_punc_removed = [char for char in Test if char not in string.punctuation]
Test_punc_removed_____no_output_____# Join the characters again to form the string.
Test_punc_removed_join = ''.join(Test_punc_removed)
Test_punc_removed_join_____no_output_____

# STEP 3.2 EXERCISE: REMOVE STOPWORDS_____no_output_____

pip install nltkRequirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (3.2.5)
Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk) (1.12.0)
import nltk
nltk.download('stopwords')[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Unzipping corpora/stopwords.zip.
# You have to download stopwords Package to execute this command
from nltk.corpus import stopwords
stopwords.words('english')_____no_output_____Test_punc_removed_join_____no_output_____Test_punc_removed_join_clean = [word for word in Test_punc_removed_join.split() if word.lower() not in stopwords.words('english')]_____no_output_____Test_punc_removed_join_clean # Only important (no so common) words are left_____no_output_____mini_challenge = 'Here is a mini challenge, that will teach you how to remove stopwords and punctuations!'_____no_output_____challege = [ char     for char in mini_challenge  if char not in string.punctuation    ]
challenge = ''.join(challege)
challenge = [  word for word in challenge.split() if word.lower() not in stopwords.words('english')  ] _____no_output_____

# STEP 3.3 EXERCISE: COUNT VECTORIZER EXAMPLE _____no_output_____

from sklearn.feature_extraction.text import CountVectorizer
sample_data = ['This is the first document.','This document is the second document.','And this is the third one.','Is this the first document?']

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(sample_data)
_____no_output_____print(vectorizer.get_feature_names())
['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']
print(X.toarray())  [[0 1 1 1 0 0 1 0 1]
 [0 2 0 1 0 1 1 0 1]
 [1 0 0 1 1 0 1 1 1]
 [0 1 1 1 0 0 1 0 1]]
mini_challenge = ['Hello World','Hello Hello World','Hello World world world']

vectorizer_challenge = CountVectorizer()
X_challenge = vectorizer_challenge.fit_transform(mini_challenge)
print(X_challenge.toarray())[[1 1]
 [2 1]
 [1 3]]

### Let's Apply The Previous Three Process To Our YELP Reviews _____no_output_____

# Let's define a pipeline to clean up all the messages
# The pipeline performs the following: (1) remove punctuation, (2) remove stopwords

def message_cleaning(message):
    Test_punc_removed = [char for char in message if char not in string.punctuation]
    Test_punc_removed_join = ''.join(Test_punc_removed)
    Test_punc_removed_join_clean = [word for word in Test_punc_removed_join.split() if word.lower() not in stopwords.words('english')]
    return Test_punc_removed_join_clean_____no_output_____# Let's test the newly added function
yelp_df_clean = yelp_df_1_5['text'].apply(message_cleaning)_____no_output_____print(yelp_df_clean[1]) # show the cleaned up version['idea', 'people', 'give', 'bad', 'reviews', 'place', 'goes', 'show', 'please', 'everyone', 'probably', 'griping', 'something', 'faultthere', 'many', 'people', 'like', 'case', 'friend', 'arrived', '550', 'PM', 'past', 'Sunday', 'pretty', 'crowded', 'thought', 'Sunday', 'evening', 'thought', 'would', 'wait', 'forever', 'get', 'seat', 'said', 'well', 'seated', 'girl', 'comes', 'back', 'seating', 'someone', 'else', 'seated', '552', 'waiter', 'came', 'got', 'drink', 'orders', 'Everyone', 'pleasant', 'host', 'seated', 'us', 'waiter', 'server', 'prices', 'good', 'well', 'placed', 'orders', 'decided', 'wanted', '602', 'shared', 'baked', 'spaghetti', 'calzone', 'small', 'Heres', 'Beef', 'pizza', 'try', 'calzone', 'huge', 'got', 'smallest', 'one', 'personal', 'got', 'small', '11', 'pizza', 'awesome', 'friend', 'liked', 'pizza', 'better', 'liked', 'calzone', 'better', 'calzone', 'sweetish', 'sauce', 'thats', 'like', 'sauce', 'box', 'part', 'pizza', 'take', 'home', 'door', '642', 'everything', 'great', 'like', 'bad', 'reviewers', 'goes', 'show', 'try', 'things', 'bad', 'reviewers', 'serious', 'issues']
print(yelp_df_1_5['text'][1]) # show the original versionI have no idea why some people give bad reviews about this place. It goes to show you, you can please everyone. They are probably griping about something that their own fault...there are many people like that.

In any case, my friend and I arrived at about 5:50 PM this past Sunday. It was pretty crowded, more than I thought for a Sunday evening and thought we would have to wait forever to get a seat but they said we'll be seated when the girl comes back from seating someone else. We were seated at 5:52 and the waiter came and got our drink orders. Everyone was very pleasant from the host that seated us to the waiter to the server. The prices were very good as well. We placed our orders once we decided what we wanted at 6:02. We shared the baked spaghetti calzone and the small "Here's The Beef" pizza so we can both try them. The calzone was huge and we got the smallest one (personal) and got the small 11" pizza. Both were awesome! My friend liked the pizza better and I liked the calzone better. The calzone does have a sweetish sauce but that's how I like my sauce!

We had to box part of the pizza to take it home and we were out the door by 6:42. So, everything was great and not like these bad reviewers. That goes to show you that  you have to try these things yourself because all these bad reviewers have some serious issues.

### Let's apply Count Vectorizer To Our YELP Reviews_____no_output_____

from sklearn.feature_extraction.text import CountVectorizer
# Define the cleaning pipeline we defined earlier
vectorizer = CountVectorizer(analyzer = message_cleaning)
yelp_countvectorizer = vectorizer.fit_transform(yelp_df_1_5['text'])_____no_output_____print(vectorizer.get_feature_names())
['0', '00s', '01042012', '022010', '03342', '05', '050', '06', '07092008', '075', '09', '090', '095', '1', '10', '100', '1000', '10000', '1000am', '1000pm', '1000x', '1001', '100150', '1007', '100s', '100th', '101', '1011', '1012', '101412', '1015', '102', '1025', '1030', '103011', '1030pm', '10312012', '105', '105°', '1070', '108', '109', '1095', '1099', '10Shrimp', '10X', '10am', '10am4pm', '10close', '10each', '10gallon', '10ish', '10min', '10mins', '10minutes', '10person', '10pm', '10pm12am', '10th', '10thand', '10yard', '10yo', '11', '110', '1100', '1100am', '1103', '111', '1113', '1115', '1118', '111807', '111809', '111909', '111PM', '111th', '112', '112312', '1125', '1130', '1130am', '1135', '1139', '114', '1141', '1145', '115', '1150', '1152', '1154', '1155', '115th', '116', '118', '1182010', '1199', '11991079', '11AM', '11PM', '11a3p', '11am', '11ish', '11person', '11pm', '12', '120', '1200', '12000', '1205', '1207', '120year', '121007', '121107', '121207', '1213', '121407', '1216', '122111', '122112', '1230', '1230P', '123110', '1232', '123200', '1240', '1244', '125', '1250', '12712', '128i', '129', '1295', '12am', '12maybe', '12oz', '12pm', '12th', '12year', '12yr', '13', '130', '1300', '131415', '13331', '1361', '13are', '13dont', '13person', '13th', '13years', '14', '140', '1400', '1417', '1418', '147', '149', '1495', '1499', '1499lb', '14Half', '14lbs', '15', '150', '1500', '15000', '150200', '150mm', '1520', '1520min', '1525', '1530', '1564', '157', '15plate', '15th', '15yard', '16', '160', '1600', '162', '165', '1650', '169', '1699', '16th', '17', '1700', '1719', '175', '17th', '18', '180', '1800', '1800s', '185', '1850', '1895', '1895s', '1899', '18lb', '18th', '19', '1900', '1900s', '1913', '1928', '1929', '1930s', '1940s', '195', '1952', '1953', '1955', '1956', '1960s', '1961', '1968', '1969', '1970', '1978', '1980', '1980s', '1987', '199', '1990', '1990s', '1992', '1995', '1996', '1997', '1998', '1999', '199not', '19th', '1Full', '1I', '1PM', '1cent', '1k', '1perscoop', '1pm', '1st', '1to1', '2', '20', '200', '2000', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '200lbs', '2010', '2011', '2012', '2012They', '202', '2025', '2030', '2038', '205', '20mbs', '20miles', '20min', '20minutes', '20s', '20something', '20th', '20xs', '21', '2100', '214', '215', '21but', '21st', '21we', '22', '220', '22312', '2240', '225', '2253', '2295', '22oz', '23', '230', '235', '24', '241', '2412', '247', '249', '249449', '2499', '24and', '24hrs', '24st', '24th', '25', '250', '2500', '25000', '250400', '2540', '25min', '25person', '25th', '26', '260', '2600', '2608', '26Whole', '26th', '27', '27243', '275', '27th', '28', '285', '29', '295', '299', '2995', '2998', '2999', '29th', '2Fresh', '2I', '2Mbps', '2am', '2nd', '2pm', '2pound', '2rd', '2top', '2topping', '2wice', '2x', '3', '30', '300', '3000', '300degree', '3035', '3040', '3045', '3080', '30extra', '30s', '30somethings', '30th', '30years', '311', '3122669', '315', '316', '32', '3200', '32010because', '325', '328', '32s', '33', '330', '330pm', '33rd', '34', '3400', '34000', '345pm', '349', '3499', '34th', '35', '350', '350ib', '3540', '35c', '35th', '36', '369', '36pm', '37', '370', '375', '3755', '379', '38', '389', '38th', '39', '3900', '394', '398', '399', '39th', '3AM', '3Also', '3D', '3G', '3I', '3K', '3PM', '3Xyear', '3am', '3bean', '3egg', '3lbs', '3p6p', '3pm', '3rd', '3stars', '3x', '3xwk', '3year', '4', '40', '400', '4000', '400a', '400s', '4050', '40Lm', '40ish', '40min', '40th', '40yearold', '40years', '41', '411', '415', '41711', '42', '4200', '420000', '425', '43', '430', '430530', '430pm', '435', '4499', '44th', '45', '450', '450600', '450ish', '455', '45min', '46', '46th', '47', '4730pm', '475', '48', '480', '48th', '49', '490', '491', '495', '499', '4B', '4Little', '4PM', '4hr', '4pack', '4pm', '4pm7pm', '4s', '4star', '4th', '4that', '4x6', '5', '50', '500', '5000', '50000', '500pm', '5060', '5070', '50Lm', '50cents', '50s', '50thats', '51', '510', '515', '517', '517p', '52', '520', '52010', '5231', '525', '528', '53', '530', '530p', '530pm', '54', '545', '549', '55', '550', '552', '56', '565', '57', '575', '58', '595', '599', '5995', '59th', '5Make', '5k', '5min', '5p', '5pm', '5star', '5stars', '5th', '5yo', '6', '60', '600', '6000', '60000', '600am', '600pm', '601', '602', '6080', '60miles', '60s', '610', '612', '615pm', '618', '61st', '620', '62010', '620pm', '623', '6235474655', '6239774458', '625', '63', '630', '630AM', '630am', '64', '642', '64ish', '64th', '65', '650', '653pm', '65person', '66', '6600', '67', '67000', '68', '69', '699', '6PM', '6am', '6pack', '6pm', '6th', '7', '70', '700', '700am', '70lb', '70s', '70th', '71', '710', '711', '711ish', '715', '719', '71st', '725', '730', '730ish', '730pm', '74th', '75', '750', '7500', '76', '775', '78', '7807', '79', '7AM6PM', '7AM9PM', '7PM', '7am', '7pm', '7th', '8', '80', '800', '8000hp', '800am', '8020', '80s', '81', '810', '811', '812', '8132010', '814', '815', '815am', '816', '819', '820', '82012', '83', '830', '830pm', '832we', '83rd', '845', '849', '85', '850', '85340', '85year', '86', '875', '88', '89', '895', '899', '8AM', '8PM', '8V', '8am', '8amagain', '8close', '8pm', '8pm10pm', '8th', '8x10', '8yo', '9', '90', '900', '9000', '90lower', '90minute', '90s', '91', '911', '915', '925', '92nd', '93', '930', '930am', '945', '945am', '95', '950', '950ea', '951pm', '96', '975', '98', '99', '995', '9975', '999', '99999', '999for', '99cent', '9am', '9oz', '9p11p', '9pm', '9th', '9year', '9yo', 'A1', 'A2', 'AA', 'AAA', 'AAAAMAZING', 'AAAMMMAZZING', 'AB', 'ABC', 'ABSOLUTE', 'ABSOLUTELY', 'ABUELOSit', 'AC', 'ACCURATE', 'ADD', 'ADORABLE', 'ADORE', 'AEG', 'AFRAID', 'AID', 'AIRPORT', 'AJ', 'AJi', 'AJs', 'AKA', 'ALEXANDRA', 'ALMOST', 'ALOT', 'ALTERNATIVE', 'ALWAYS', 'ALofts', 'AMAAAAAZING', 'AMAZED', 'AMAZING', 'AMC', 'AMORE', 'AMP', 'AMPLE', 'AMPM', 'AMazing', 'AMber', 'ANDshes', 'ANGER', 'ANGRY', 'ANIMAL', 'ANISTON', 'ANOTHER', 'ANYBODY', 'ANYONE', 'ANYTHING', 'ANYWHERE', 'AP', 'APRIL', 'APeeling', 'AREA', 'ARIZONA', 'ARTS', 'ASAP', 'ASK', 'ASS', 'ASU', 'ASUs', 'ATC', 'ATCs', 'ATF', 'ATL', 'ATM', 'ATT', 'AVG', 'AVOID', 'AVOIDING', 'AW', 'AWARDWINNING', 'AWAY', 'AWEFUL', 'AWESOME', 'AWFUL', 'AWSOME', 'AZ', 'AZ88', 'AZN', 'AZSpaGirls', 'AZs', 'Aaron', 'AbbaZabba', 'Abby', 'Absinthe', 'Absolut', 'Absolute', 'Absolutely', 'Abuelos', 'Acacia', 'Academy', 'Acai', 'Acapulco', 'Access', 'According', 'Account', 'Accutemp', 'Ace', 'Acid', 'Acknowledge', 'Acne', 'Acres', 'Acting', 'Actor', 'Actually', 'Adam', 'Adams', 'Add', 'Added', 'Addendum', 'Addicted', 'Addiction', 'Adding', 'Additional', 'Additionally', 'Adelman', 'Adios', 'Admission', 'Admittedly', 'Adobe', 'Adobo', 'Adovada', 'Adrian', 'Adrianne', 'Adrienne', 'Adult', 'Advisor', 'Advocate', 'Aerobics', 'Aesthetically', 'Affordable', 'Africa', 'Afterdark', 'Afterward', 'Afterwards', 'Agaves', 'Aged', 'Agent', 'Agree', 'Agua', 'Aguila', 'Agwa', 'Ah', 'Ahha', 'Ahhh', 'Ahhhhh', 'Ahhhhhh', 'Ahi', 'Ahmazing', 'Ahwahtukee', 'Ahwatukee', 'Aid', 'Aiellos', 'Aiko', 'Air', 'Airlines', 'Airpark', 'Airport', 'Airwarys', 'Airways', 'Airwaysnever', 'Aisha', 'Aji', 'Ajo', 'Akor', 'Al', 'Alabama', 'Aladdins', 'Alain', 'Alameda', 'Alan', 'Alas', 'AlasIve', 'Alaska', 'Alaskan', 'Alaus', 'Albacore', 'Alber', 'Albertsons', 'Alcatraz', 'Ale', 'Alessias', 'Alex', 'Alexs', 'Alfalfa', 'Alfonso', 'Alfred', 'Alfredo', 'Ali', 'Alice', 'Alicia', 'AllAmerican', 'AllStar', 'Allegiant', 'Allen', 'Allende', 'Allergic', 'Allesias', 'Allison', 'Allow', 'Allstars', 'Almond', 'Almost', 'Aloe', 'Aloha', 'Along', 'Alongside', 'Alp', 'Alphagraphics', 'Already', 'Alright', 'Als', 'Also', 'Alterations', 'Although', 'Altic', 'Alvadora', 'Always', 'Amados', 'Amalfi', 'Amanda', 'Amaro', 'Amazed', 'Amazing', 'Amazingly', 'Amazon', 'Amazoncom', 'Ambassador', 'Amber', 'Ambiance', 'Ambrosia', 'Ambulance', 'Amenities', 'AmeriZona', 'America', 'American', 'AmericanFrench', 'Americana', 'Americanized', 'AmericanizedItalian', 'Americano', 'Americanos', 'Americans', 'Americas', 'Ameritrash', 'Amin', 'Amish', 'Amomi', 'Amore', 'Amphitheatre', 'Ample', 'Amsterdam', 'Amtrack', 'Amuse', 'Amy', 'Anaheim', 'Anchored', 'Andiamo', 'Andrea', 'Andrew', 'Andrews', 'Andwait', 'AndyAndrew', 'Andys', 'Angel', 'Angela', 'Angelas', 'Angeles', 'Angelesjust', 'Angello', 'Angels', 'Angie', 'Angry', 'Anibal', 'Animal', 'Animals', 'Anise', 'Ann', 'Anne', 'Annette', 'Annie', 'Annies', 'Annihilator', 'Anniversary', 'Annoying', 'Anns', 'Another', 'Ansel', 'Answer', 'Anthem', 'Anthems', 'Anthony', 'Anti', 'Antipasto', 'Antique', 'Antiques', 'Antonio', 'Antono', 'Ants', 'Anyhow', 'Anyone', 'Anything', 'Anytime', 'Anyway', 'Anyways', 'AnywaysI', 'Anywaysthe', 'Anywaystheir', 'Anywaytry', 'Anywhere', 'Anywho', 'Ao', 'Apache', 'Apartment', 'Apology', 'Apothic', 'Appalachians', 'Apparel', 'Apparently', 'Appearances', 'Appears', 'Appetit', 'Appetite', 'Appetizer', 'Appetizers', 'Apple', 'Applebees', 'Apples', 'Applewood', 'Appreciated', 'Appreciation', 'Approximately', 'Apps', 'Apricot', 'April', 'Aqua', 'Arabic', 'Arai', 'Arbol', 'Arborio', 'Arbuckles', 'Arcadia', 'ArcadiaBiltmoreCamelback', 'Arcane', 'Archery', 'Architect', 'Architecture', 'Arco', 'Area', 'Arena', 'Arent', 'Argentine', 'Argentinian', 'Argyle', 'Ariel', 'Arigato', 'Arizona', 'Arizonans', 'Arizonas', 'Arizonian', 'Arizonians', 'Arkansas', 'Arlecchinos', 'Arlin', 'Armando', 'Armed', 'Armour', 'Arms', 'Army', 'Arnold', 'Around', 'Arpaio', 'Arriba', 'Arribas', 'ArribasGo', 'Arrive', 'Arrived', 'Arrivederci', 'Arriving', 'Arrogant', 'Arrowhead', 'Art', 'Artful', 'Arthur', 'Artichoke', 'Arties', 'Artist', 'Artois', 'Arts', 'Arugula', 'Asada', 'Asado', 'Asahi', 'Asaian', 'Ashamedly', 'Ashley', 'Ashleys', 'Ashtanga', 'Asia', 'Asiago', 'Asian', 'AsianMexican', 'Asians', 'Aside', 'Ask', 'Asked', 'Asparagus', 'Ass', 'Assistant', 'Association', 'Ate', 'Athenian', 'Athletics', 'Atlanta', 'Atlantic', 'Atmosphere', 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'IvegottheTwangyTartwithdrawalshakes', 'Ivejustbeenbitchslappedbycaffeine', 'Iveve', 'Ivory', 'Ivy', 'Iwe', 'IxtapaZihuatenejo', 'J', 'JAM', 'JC', 'JCooks', 'JD', 'JENNIFER', 'JERK', 'JESTERS', 'JIB', 'JJ', 'JJs', 'JK', 'JP', 'JUNEand', 'JW', 'Jack', 'Jackass', 'Jacket', 'Jackie', 'Jacks', 'Jacob', 'Jacobs', 'Jacques', 'Jacquie', 'Jacs', 'Jade', 'Jaeger', 'Jagermeister', 'Jaimes', 'Jake', 'Jakes', 'Jalapeno', 'Jalapenos', 'Jalapeño', 'Jalepeno', 'Jam', 'Jamaica', 'Jamaican', 'Jamba', 'Jambalaya', 'Jamburrito', 'James', 'Jameson', 'Jan', 'Janes', 'Janet', 'Janis', 'Jans', 'Janslet', 'January', 'Japan', 'Japanese', 'Jar', 'Jared', 'Jarred', 'Jarvis', 'Jasmin', 'Jasmine', 'Jason', 'Jasons', 'Java', 'Javas', 'Jaws', 'Jay', 'Jazz', 'Jcrew', 'Jeans', 'Jedis', 'Jeez', 'Jeff', 'Jefferson', 'Jekyll', 'Jen', 'Jenn', 'Jenna', 'Jennfer', 'Jennifer', 'Jennifers', 'Jenny', 'Jens', 'Jensy', 'Jeremiah', 'Jerk', 'Jerron', 'Jerrys', 'Jersey', 'Jess', 'Jesse', 'Jessie', 'Jesterz', 'Jesus', 'Jetland', 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'winter', 'winters', 'wipe', 'wiped', 'wiper', 'wipes', 'wiping', 'wire', 'wired', 'wireless', 'wires', 'wiring', 'wisdom', 'wise', 'wisely', 'wiser', 'wish', 'wished', 'wishes', 'wishful', 'wishing', 'witches', 'withbut', 'withdrawal', 'withdrawn', 'withe', 'withheld', 'withhis', 'within', 'without', 'withstand', 'withstanding', 'witness', 'witnessed', 'witnesses', 'witty', 'wives', 'wiz', 'wks', 'wll', 'wmushroom', 'wo', 'wobblesee', 'wobbly', 'woefilled', 'woes', 'wok', 'woke', 'woken', 'wolfed', 'woman', 'women', 'womens', 'wonder', 'wondered', 'wonderful', 'wonderfulThe', 'wonderfuland', 'wonderfully', 'wonderfulness', 'wonderfulthe', 'wondering', 'wonderous', 'wonders', 'wondrous', 'wont', 'wonton', 'wontons', 'woo', 'wood', 'woodcarving', 'wooden', 'woodfired', 'woodpaneled', 'woods', 'woodworker', 'wooed', 'wool', 'word', 'wordexcellent', 'wordis', 'wordly', 'words', 'wordsit', 'wore', 'work', 'workappropriate', 'worked', 'worker', 'workers', 'working', 'workmanship', 'workout', 'workouts', 'works', 'workschool', 'workshops', 'workstudyread', 'world', 'worldTyson', 'worldand', 'worldlolI', 'worlds', 'worldwide', 'worm', 'worn', 'worried', 'worries', 'worrisome', 'worry', 'worrying', 'worse', 'worship', 'worst', 'worstershire', 'worth', 'worthless', 'worthwhile', 'worthy', 'woudnt', 'would', 'woulda', 'wouldnt', 'wouldve', 'wound', 'wounds', 'wow', 'wowd', 'wowed', 'wowner', 'wowzer', 'wrap', 'wrapped', 'wrapper', 'wrappers', 'wrapping', 'wraps', 'wrath', 'wreck', 'wrestling', 'wrice', 'wrinkled', 'wrinkles', 'wrinkling', 'wrist', 'wristbands', 'write', 'writer', 'writers', 'writes', 'writing', 'written', 'wrkout', 'wrong', 'wrongthings', 'wrote', 'wtf', 'wth', 'wthe', 'wthin', 'wthis', 'wtih', 'wussies', 'wussy', 'wwwaddictionovercomecom', 'wwwdogparkriotcom', 'wwwphoenixrodandgun', 'x', 'x2', 'xeriscape', 'xrays', 'xylophone', 'xèo', 'ya', 'yaaaaay', 'yack', 'yah', 'yaki', 'yakisoba', 'yakitoriThe', 'yall', 'yam', 'yankin', 'yard', 'yards', 'yardsale', 'yawning', 'yay', 'yds', 'yea', 'yeah', 'yeahplease', 'year', 'yearly', 'yearning', 'yearnings', 'years', 'yearshow', 'yearsif', 'yearsso', 'yearssuper', 'yearsthe', 'yearsuntil', 'yearthough', 'yeast', 'yeh', 'yell', 'yelled', 'yelling', 'yellow', 'yellowfin', 'yellowish', 'yellowtail', 'yells', 'yelp', 'yelpcom', 'yelped', 'yelper', 'yelpers', 'yelping', 'yelps', 'yen', 'yep', 'yeppie', 'yer', 'yerba', 'yes', 'yesssss', 'yesterday', 'yet', 'yetD', 'yetthe', 'yettobeadded', 'yielding', 'yikes', 'yo', 'yodels', 'yoga', 'yogurt', 'yogurtcucumbermint', 'yogurtland', 'yogurts', 'yolk', 'york', 'yorker', 'youFeed', 'youd', 'youll', 'young', 'younger', 'youngest', 'youngggg', 'younight', 'youre', 'yourselfdoes', 'youth', 'youthful', 'youve', 'youwhen', 'yow', 'yr', 'yrs', 'yu', 'yuck', 'yucky', 'yuk', 'yukon', 'yum', 'yuma', 'yumgasms', 'yumm', 'yummie', 'yummier', 'yumminess', 'yummm', 'yummmm', 'yummmmmmers', 'yummo', 'yummy', 'yummyalthough', 'yummydont', 'yummythat', 'yummywe', 'yumness', 'yup', 'yuppies', 'yur', 'yuuuuummmmmyyy', 'yuyuyummy', 'yuzu', 'zen', 'zenRoad', 'zenlike', 'zero', 'zerostar', 'zest', 'zhou', 'zichini', 'zilch', 'zillion', 'zin', 'zinc', 'zing', 'zip', 'ziploc', 'zipper', 'ziti', 'zone', 'zones', 'zoning', 'zoo', 'zoom', 'zucchini', 'zuccini', 'zuchinni', 'zupa', 'zwiebelkräuter', 'École', 'éclairs', 'ém']
print(yelp_countvectorizer.toarray())  [[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
yelp_countvectorizer.shape_____no_output_____

# STEP#4: TRAINING THE MODEL WITH ALL DATASET_____no_output_____

from sklearn.naive_bayes import MultinomialNB
NB_classifier = MultinomialNB()
label = yelp_df_1_5['stars'].values
_____no_output_____label_____no_output_____NB_classifier.fit(yelp_countvectorizer, label)_____no_output_____testing_sample = ['amazing food! highly recommmended']
testing_sample_countvectorizer = vectorizer.transform(testing_sample)
test_predict = NB_classifier.predict(testing_sample_countvectorizer)
test_predict_____no_output_____testing_sample = ['shit food, made me sick']
testing_sample_countvectorizer = vectorizer.transform(testing_sample)
test_predict = NB_classifier.predict(testing_sample_countvectorizer)
test_predict_____no_output_____

# STEP#4: DIVIDE THE DATA INTO TRAINING AND TESTING PRIOR TO TRAINING_____no_output_____

X = yelp_countvectorizer
y = label_____no_output_____X.shape_____no_output_____y.shape_____no_output_____from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)_____no_output_____from sklearn.naive_bayes import MultinomialNB
NB_classifier = MultinomialNB()
NB_classifier.fit(X_train, y_train)_____no_output_____

# STEP#5: EVALUATING THE MODEL _____no_output_____

from sklearn.metrics import classification_report, confusion_matrix
_____no_output_____y_predict_train = NB_classifier.predict(X_train)
y_predict_train
cm = confusion_matrix(y_train, y_predict_train)
sns.heatmap(cm, annot=True)_____no_output_____# Predicting the Test set results
y_predict_test = NB_classifier.predict(X_test)
cm = confusion_matrix(y_test, y_predict_test)
sns.heatmap(cm, annot=True)_____no_output_____print(classification_report(y_test, y_predict_test))              precision    recall  f1-score   support

           1       0.79      0.66      0.72       163
           5       0.92      0.96      0.94       655

    accuracy                           0.90       818
   macro avg       0.86      0.81      0.83       818
weighted avg       0.89      0.90      0.89       818

# STEP #6: LET'S ADD ADDITIONAL FEATURE TF-IDF_____no_output_____- Tf–idf stands for "Term Frequency–Inverse Document Frequency" is a numerical statistic used to reflect how important a word is to a document in a collection or corpus of documents.
- TFIDF is used as a weighting factor during text search processes and text mining.
- The intuition behing the TFIDF is as follows: if a word appears several times in a given document, this word might be meaningful (more important) than other words that appeared fewer times in the same document. However, if a given word appeared several times in a given document but also appeared many times in other documents, there is a probability that this word might be common frequent word such as 'I' 'am'..etc. (not really important or meaningful!).

- TF: Term Frequency is used to measure the frequency of term occurrence in a document:
    - TF(word) = Number of times the 'word' appears in a document / Total number of terms in the document
- IDF: Inverse Document Frequency is used to measure how important a term is:
    - IDF(word) = log_e(Total number of documents / Number of documents with the term 'word' in it).

- Example: Let's assume we have a document that contains 1000 words and the term “John” appeared 20 times, the Term-Frequency for the word 'John' can be calculated as follows:
    - TF|john = 20/1000 = 0.02

- Let's calculate the IDF (inverse document frequency) of the word 'john' assuming that it appears 50,000 times in a 1,000,000 million documents (corpus).
    - IDF|john = log (1,000,000/50,000) = 1.3

- Therefore the overall weight of the word 'john' is as follows
    - TF-IDF|john = 0.02 * 1.3 = 0.026_____no_output_____

yelp_countvectorizer_____no_output_____from sklearn.feature_extraction.text import TfidfTransformer
yelp_tfidf = TfidfTransformer().fit_transform(yelp_countvectorizer)
print(yelp_tfidf.shape)(4086, 26435)
yelp_tfidf_____no_output_____print(yelp_tfidf[:,:])
# Sparse matrix with all the values of IF-IDF  (0, 26268)	0.08050274254786693
  (0, 26228)	0.09348620453726474
  (0, 26144)	0.07169873884062605
  (0, 26045)	0.15817660070664968
  (0, 25776)	0.06066174442364943
  (0, 25773)	0.12002965112412296
  (0, 25418)	0.14546095084580743
  (0, 25122)	0.2703898166869907
  (0, 24911)	0.14546095084580743
  (0, 24670)	0.12383875478045604
  (0, 24644)	0.13058459416402163
  (0, 24623)	0.04459768543876988
  (0, 24154)	0.06030465665278142
  (0, 23120)	0.09876367605721408
  (0, 22450)	0.09315710247790514
  (0, 22430)	0.1097636086218109
  (0, 22000)	0.06662808805692144
  (0, 21801)	0.13058459416402163
  (0, 20785)	0.09005184530467268
  (0, 19733)	0.14546095084580743
  (0, 19354)	0.09547907116641852
  (0, 19039)	0.12613266766643505
  (0, 18911)	0.1133722206104443
  (0, 18805)	0.10789642494273591
  (0, 18740)	0.08834411964653922
  :	:
  (4085, 16843)	0.12124793979515064
  (4085, 15626)	0.056073903602148346
  (4085, 15041)	0.08518302578980794
  (4085, 14689)	0.13505937049458547
  (4085, 14480)	0.08518302578980794
  (4085, 14423)	0.10064506371832901
  (4085, 13617)	0.19963796890231086
  (4085, 12214)	0.29952324839506667
  (4085, 11514)	0.11529629949383458
  (4085, 11501)	0.11809666496753578
  (4085, 10457)	0.14650841668583717
  (4085, 9817)	0.13505937049458547
  (4085, 9285)	0.2170895701515176
  (4085, 9031)	0.17197783534477967
  (4085, 8204)	0.1591166056610063
  (4085, 7528)	0.19963796890231086
  (4085, 7523)	0.1567169489941616
  (4085, 6333)	0.14166500441179955
  (4085, 4932)	0.1940198081594198
  (4085, 4377)	0.12651882139660317
  (4085, 1237)	0.17656820691021305
  (4085, 1053)	0.11358870702258798
  (4085, 505)	0.2170895701515176
  (4085, 448)	0.27629234133665775
  (4085, 344)	0.12042237932403005
X = yelp_tfidf
y = label

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15)

from sklearn.naive_bayes import MultinomialNB
NB_classifier = MultinomialNB()
NB_classifier.fit(X_train, y_train)

from sklearn.metrics import classification_report, confusion_matrix
y_predict_train = NB_classifier.predict(X_train)
y_predict_train
cm = confusion_matrix(y_train, y_predict_train)
sns.heatmap(cm, annot=True)_____no_output_____

# EXCELLENT JOB! NOW YOU BECAME FAMILIAR WITH NLP, GREAT JOB!_____no_output_____#End of the project_____no_output_____