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On extracting keywords from long‐and‐difficult English sentences for smart sentiment analysis

2020; Wiley; Volume: 4; Issue: 1 Linguagem: Inglês

10.1002/itl2.226

2476-1508

Xin Ke, Michael Bublé,

Text and Document Classification Technologies

With the population of social multi‐media and website, smart sentiment analysis from sentences or language becomes a new interesting application in the field of smart cities. In this paper, the sentiment is automatically analyzed by extracting the keywords from the long‐and‐difficult English sentences. In supervised scenario, we extract the keywords that are more relevant to express emotions according to syntactic relationship and logical structure, and then assign these keywords weights. In semi‐supervised scenario, we combine the key sentence extraction and classifier fusion algorithm to extract key sentences that contain more sentiment words. During extracting key sentences, we consider sentiment word attribute, position attribute, punctuation attribute, and keywords attribute. During classifier fusion stage, we utilize the classifier with highest confidence to decide the final classification result. The experiments, performed on IMDB and Rotten Tomatoes, show that the proposed method performs better than previous ones. Therefore, extracting keywords via syntactic relationship and logical structure is helpful for smart sentiment analysis.