Wazzabeee/NLP_Unsupervised_Sentiment_Analysis_Elon_Musk
Notebook used to explore and classify 500,000 tweets about Elon Musk in an unsupervised manner.
This project helps you understand public opinion about a specific topic or person by analyzing large volumes of social media text. It takes raw tweets and categorizes them into positive, negative, or neutral sentiment without needing pre-labeled data. Anyone interested in public relations, brand monitoring, or social media research can use this to gauge sentiment around key figures or events.
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Use this if you need to quickly assess the general mood or sentiment of a large collection of social media posts about a particular subject, without the upfront effort of manually labeling data.
Not ideal if you require highly nuanced sentiment classification or are dealing with a language where this unsupervised method might struggle to accurately interpret context.
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Jupyter Notebook
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Last pushed
Mar 14, 2023
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