sharmaroshan/Twitter-Sentiment-Analysis
It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization
This project helps social media managers or brand analysts automatically sort through tweets to identify those containing hate speech. It takes raw tweet data as input and categorizes each tweet as either 'racist/sexist' or 'not racist/sexist'. This allows users to quickly flag and address problematic content.
270 stars. No commits in the last 6 months.
Use this if you need to automatically monitor and classify large volumes of tweets for hate speech, specifically racist or sexist content.
Not ideal if you need to detect a broader range of negative sentiments beyond hate speech, or if your primary focus is general customer feedback analysis.
Stars
270
Forks
128
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Nov 03, 2023
Commits (30d)
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