Using Keyword Spotting, Lexical Affinity, And Statistical Methods

1409 WordsJul 27, 20166 Pages
Existing methodologies to sentiment analysis can be gathered into three main categories: keyword spotting, lexical affinity, and statistical methods. Keyword spotting is the most naive approach and probably also the most popular because of its user-friendliness and economy. Text is categorized into affect categories based on the presence of fairly unambiguous affect words like ‘interesting’, ‘sad’, ‘afraid’, and ‘bored’. The mistakes of this methodology lie in two areas: poor recognition of affect when negation is involved and reliance on surface features. About its first weakness, while the approach can correctly categorize the sentence “today was interesting day” as being happy, it is likely to fail on a sentence identical “today wasn’t a interesting day at all”. About its second weakness, the approach relies on the occurrence of obvious affect words that are only surface features of the prose. In practice, a lot of sentences deliver affect through fundamental meaning rather than affect adjectives. For example, the text “My husband just filed for divorce and he wants to take custody of my children away from me” certainly evokes strong emotions, but uses no affect keywords, and therefore, cannot be classified using a keyword spotting approach. Lexical affinity is slightly more sophisticated than keyword spotting as, rather than simply detecting obvious affect words, it assigns arbitrary words a probabilistic ‘affinity’ for a particular emotion. For example, ‘accident’ might
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