Polarity shift detection, elimination and ensemble: A three-stage model for document-level sentiment analysis

A Hybrid Approach for Polarity Shift Detection

“…Sentiment analysis is a growing research field [3]. According to Bing Liu [1], "Sentiment analysis is considered as a highly restricted NLP problem.…”

“…Usually it is difficult to capture the sentiment reversion caused by polarity shifters in the BOW model, as two sentiment-opposite texts (e.g., "I am happy with this phone" and "I am not happy with this phone") are regarded to be very similar in the BOW representation [3]. The two main steps for considering polarity shift are: 1) Detecting polarity shifted words or sentences.…”

“…The different kinds of polarity shifters should be handled in different ways. According to [3], "Negation is able to shift the sentiment polarity within the phrase." The early work on polarity shifting focuses on negation [2], [11], [26], [21].…”

“…Polarity shift is considered to be a linguistic phenomenon. In polarity shift, the polarity of a particular review changes from positive to negative or vice versa [3]. While doing sentiment classification, if the Bag-of-words (BOW) model is used then it will not regard any grammar, as a result of that the syntactic structure of the sentence may be disturbed and that'll cause problems like polarity shift detection, anaphora resolution, etc.…”

“…The linguistic phenomenon in which the sentimental orientation of the whole text is changed from negative to positive or vice versa; is called polarity shifting [3], [4]. Polarity shift is caused by polarity shifters, e.g., contrast, negation, likelihood, etc.…”