Twitter-Sentiment-Analysis and public-sentiment-analysis-based-on-twitter-hashtags
About Twitter-Sentiment-Analysis
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.
About public-sentiment-analysis-based-on-twitter-hashtags
I-am-sayantan/public-sentiment-analysis-based-on-twitter-hashtags
This project focuses on sentiment analysis. Social Sentiment analysis is the use of natural language processing (NLP) to analyze social conversations online and determine deeper context as they apply to a topic, brand or theme.
This project helps you understand public opinion by analyzing Twitter hashtags. It takes raw Twitter data associated with specific hashtags and processes it to reveal the prevailing sentiment (positive, negative, or neutral). Marketers, public relations professionals, or social researchers can use this to gauge public reaction to events, brands, or topics over time.
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