shayneobrien/sentiment-classification
Neural sentiment classification of text using the Stanford Sentiment Treebank (SST-2) movie reviews dataset, logistic regression, naive bayes, continuous bag of words, and multiple CNN variants.
This project offers tools to classify the sentiment of written text as either positive or negative. It takes in sentences or short paragraphs and outputs a clear "positive" or "negative" label, helping users quickly understand the emotional tone of large volumes of text. This is designed for anyone needing to automate the analysis of textual data, such as market researchers, social media managers, or customer feedback analysts.
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Use this if you need to automatically determine whether short pieces of text, like reviews or comments, express a positive or negative sentiment.
Not ideal if you need to detect nuanced emotions beyond positive/negative, or if your text data is in a domain very different from movie reviews.
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Oct 07, 2018
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