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Philipp Heinemann's List: Twitter Sentiment Analysis (IDP)
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Oct 27, 09
Annotated link http://www.diigo.com/bookmark/http%3A%2F%2Fwordnet.princeton.edu
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WordNet® is a large lexical database of English
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The simplest algorithms work by scanning keywords to categorize a statement as positive or negative, based on a simple binary analysis
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irony, sarcasm, slang and other idiomatic expressions.
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That same message ended with “we all felt very hungry afterwards” — and the system took the word “hungry” to indicate a negative sentiment.
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state-of-the-art in text mining
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algorithms and operations
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meta-hashtag specifically for talking about all things market-related. It’s become commonplace on the Twitter feeds of financial analysts, day traders and finance bloggers.
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