pip install -U textblob
python -m textblob.download_corpora

from textblob import TextBlob

sentence = '''Still, in a basic and undeniable sense, this is a good movie, and fans who have earned their credit hours with the first two will want to see this one. "The Matrix Revolutions" is a terrific action achievement.'''

# Firstly, we create a TextBlob object.
analysis = TextBlob(sentence)
# Secondly, we show its sentiment property.
print(analysis.sentiment)

Sentiment(polarity=0.21..., subjectivity=0.43...)

sentence = '''The movie was awful and crappy but on such a painful level, so we walked out before it was over.'''
Sentiment(polarity=-0.56..., subjectivity=0.79...)

sentence = '''The boiling point of water is 212 degrees Fahrenheit or 100 degrees Celsius. Water has three states solid as ice, liquid as water, and gas as vapor.'''
Sentiment(polarity=0.0, subjectivity=0.1)

from textblob import TextBlob
# The textblob.sentiments module contains two sentiment analysis implementations: PatternAnalyzer and NaiveBayesAnalyzer (an NLTK classifier trained on a movie reviews corpus).
from textblob.sentiments import NaiveBayesAnalyzer
# Tweepy is an easy-to-use Python library for accessing the Twitter API. 
import tweepy, credentials

api_key = 'XXXXXXXXXXXXXXXXXXXXXXXXX' 
api_secret = 'XXXXXXXXXXXXXXXXXXXXXXXXX' 
access_token = 'XXXXXXXXXXXXXXXXXXXXXXXXX' 
access_secret = 'XXXXXXXXXXXXXXXXXXXXXXXXX'

auth = tweepy.auth.OAuthHandler(credentials.api_key, credentials.api_secret)
auth.set_access_token(credentials.access_token, credentials.access_secret)
api = tweepy.API(auth, wait_on_rate_limit=True) # auth is the authentication handler to be used. It is a good idea to set wait_on_rate_limit to True as twitter may block you from using its API if it finds that you are exceeding some user limit.

positive_tweets = 0
positive_total = 0
negative_tweets = 0
negative_total = 0

for tweet in api.search_tweets(q="covid restrictions", lang="en", count=50):
    print(tweet.text)
    # We apply the "NaiveBayesAnalyzer sentiment" analysis implementation.
    blob_object = TextBlob(tweet.text, analyzer=NaiveBayesAnalyzer())
    # The NaiveBayesAnalyzer returns its result as a namedtuple of the form: Sentiment(classification, p_pos, p_neg).
    analysis = blob_object.sentiment
    print(analysis)
    if analysis.classification=='pos':
        positive_tweets += 1
        positive_total += analysis.p_pos
    else:
        negative_tweets += 1
        negative_total += analysis.p_neg

print(f"Positive tweets: {positive_tweets}. Average positive sentiment: {positive_total/positive_tweets}")
print(f"Negative tweets: {negative_tweets}. Average negative sentiment: {negative_total/negative_tweets}")

import pandas as pd
from textblob import TextBlob

def sentiment(reviews, positive):

    sumPolarity = correct = 0
    for review in reviews: # It loops through the reviews.
        analysis = TextBlob(review) # It creates a TextBlob object. The constructor takes as a parameter a review.
        
        if positive and analysis.sentiment.polarity > 0:
            correct += 1
            sumPolarity += analysis.sentiment.polarity
        elif not positive and analysis.sentiment.polarity <= 0:
            correct += 1
            sumPolarity += analysis.sentiment.polarity
        
    return sumPolarity/correct, correct/len(reviews)*100.0 

def sentimentSubjectivity(reviews, positive):

    count = correct = sumPolarity = 0
    for review in reviews:
        analysis = TextBlob(review)
        if analysis.sentiment.subjectivity > 0.7:
            if positive and analysis.sentiment.polarity > 0:
                correct += 1
                sumPolarity += analysis.sentiment.polarity
            elif not positive and analysis.sentiment.polarity < 0:
                correct += 1
                sumPolarity += analysis.sentiment.polarity
            count += 1
        
    return sumPolarity/correct, correct/count*100.0 

# pd.read_csv reads a comma-separated values (csv) file, our dataset, into a DataFrame.
data = pd.read_csv('dataset.csv')
print(data.head())
print(len(data))

# We initialize our positive and negative reviews lists.
positiveReviews=[]
negativeReviews=[]

# The dataset.csv file has two columns: text which consists of movie reviews and label which consists of the values 1 or 0. 1 stands for positive sentiment and 0 stands for negative sentiment. We are going to loop through the DataFrame to separate positive and negative reviews.
for i in range(0,len(data)-1):
    if data.label[i]==1:
        positiveReviews.append(data.text[i])
        
    else:
        negativeReviews.append(data.text[i])
        
avgPolarity, correct = sentiment(positiveReviews, True)
avgPolarity2, correct2 = sentimentSubjectivity(positiveReviews, True)

print(f"Total positive reviews {len(positiveReviews)}. Positive accuracy: {correct:.2f}. Average sentiment: {avgPolarity:.2f}. Accuracy considering subjectivity: {correct2:.2f}. Average sentiment considering subjectivity: {avgPolarity2:.2f}.")

avgPolarity, correct = sentiment(negativeReviews, False)
avgPolarity2, correct2 = sentimentSubjectivity(negativeReviews, False)      

print(f"Total negative reviews {len(negativeReviews)}. Negative accuracy: {correct:.2f}. Average sentiment: {avgPolarity:.2f}. Accuracy considering subjectivity: {correct2:.2f}. Average sentiment considering subjectivity: {avgPolarity2:.2f}.")

from textblob import TextBlob 
from gingerit.gingerit import GingerIt # It provides correcting spelling and grammar mistakes based on the context of complete sentences. It is a wrapper around the gingersoftware.com API. Installation: pip install gingerit.

def spellcheck(text):
    txt = TextBlob(text) # First, we create a TextBlob object.
    correct_txt = txt.correct() # Then, we use the correct() method to attempt spelling correction. 
    parser = GingerIt()

    return correct_txt, parser.parse(text)['result']
 
def main():    
    text = "The smelt of fliwers bring back memories. He love playing fotball."
    print("Original Text: " + text)  
    sc1, sc2 = spellcheck(text)
    print("Spell Check results:")
    print(sc1)
    print(sc2)

if __name__ == "__main__":
    main()

from textblob import TextBlob # TextBlob relies on Google Translate's API. In other words, it requires an active internet connection for performing translations.
blob = TextBlob("I swear I couldn’t love you more than I do right now, and yet I know I will tomorrow.") # We create a TextBlob object.
print(blob.translate(to='th')) # We use the TextBlob translate() method. It accepts two arguments from_lang and to. The from_lang is automatically set depending on the language it detects.
print(blob.translate(to='es'))
print(blob.translate(to='hi'))
print(blob.translate(to='ja'))

from translate import Translator # pip install translate

translator= Translator(to_lang="th")
print(translator.translate("You know you’re in love when you can’t fall asleep because reality is finally better than your dreams."))