barissayil/SentimentAnalysis
Sentiment analysis neural network trained by fine-tuning BERT, ALBERT, or DistilBERT on the Stanford Sentiment Treebank.
This tool helps analyze customer feedback, social media comments, or product reviews to understand the emotional tone behind text. You input raw text data, and it outputs whether the sentiment is positive, negative, or neutral. This is ideal for marketers, customer support teams, or product managers who need to quickly gauge public opinion or user satisfaction from large volumes of text.
380 stars. No commits in the last 6 months.
Use this if you need to automatically categorize text data by its emotional sentiment, such as identifying positive or negative comments from surveys or social media feeds.
Not ideal if you need to understand nuanced emotions beyond positive, negative, or neutral, or if your text data contains highly domain-specific jargon that requires custom emotion labeling.
Stars
380
Forks
48
Language
Python
License
MIT
Category
Last pushed
Jun 12, 2023
Commits (30d)
0
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