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The Twitter Sentiment Analysis Dataset contains 1,578,627 classified tweets, each row is marked as 1 for positive sentiment and 0 for negative sentiment. I recommend using 1/10 of the corpus for testing your algorithm, while the rest can be dedicated towards training whatever algorithm you are using to classify sentiment. I tried using this dataset with a very simple Naive Bayesian classification algorithm and the result were 75% accuracy, given that a guess work approach over time will achieve an accuracy of 50% , a simple approach could give you 50% better performance than guess work essentially, not so great, but given that generally (and particularly when it comes to social communication sentiment classification) 10% of sentiment classification by humans can be debated, the maximum relative accuracy any algorithm analysing over-all sentiment of a text can hope to achieve is 90%, this is not a bad starting point Read more↗

Content typeapplication/zip
Updated6 years ago
Checked5 months ago
Keywordssentiment analysis dataset zip

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