It is a project of Negative Message Blocker Which will help to remove messages from any social media platform and also any type of data it can remove . Main working with uniqueness is:-

• It will work from server side.
• We will add this feature directly in social media app and whenever client will update its app it will automatically install in client app as a normal feature and work automatically no need of client is there to delete negative messages it will delete whenever it will detect.
• No matter it is a video,audio,image and text it will work for all.

##Build with

The prototype is made using follows as:-

• Python
• Nltk

##Libraries Installation import os import nltk import vader.sentiment import better_profanity

It is a code for text messages only

import os import time from datetime import datetime as dt import nltk.data from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer from better_profanity import profanity while True: if dt(dt.now().year, dt.now().month, dt.now().day,8): dt.now() < dt(dt.now().year, dt.now().month, dt.now().day,16) print("working")

    with open("C:/Users/Vivek Agrawal/OneDrive/Documents/file1.txt",'r+') as file :
        if __name__=="__main__":
            list1=input("enter any  negative sentence");		

            custom_badwords=['rascal','Apathetic','bastard','asshole','bitch','brother fucker','bullshit','bollocks','wanker','dickhead','nigra','damn it','hell','bloody hell','shit as','farted']
            profanity.add_censor_words(custom_badwords)
            profanity.censor(custom_badwords)
            s=profanity.censor(list1)
            print(profanity.censor(s))                    
            file.write(s)  
            content=file.readline()                              
            analyzer=SentimentIntensityAnalyzer()
            polarity_scores=analyzer.polarity_scores(content)
            positive_score=polarity_scores['pos']
            negative_score=polarity_scores['neg']
            compound_score=polarity_scores['compound']           

if compound_score>0.5:
    print("positive message")
    break
elif compound_score< -0.5:
    print("negative message")        
    break
else:
    print("Thanku ")
    break