Open AccessFine grained sentiment analysis based on Bert
Author(s) -
Chao Chen,
Xiaoli Hu,
Huibing Zhang,
Zhaoyu Shou
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012016
Subject(s) - sentiment analysis , computer science , dependency grammar , polarity (international relations) , feature (linguistics) , dependency (uml) , set (abstract data type) , parsing , value (mathematics) , artificial intelligence , natural language processing , commodity , data set , machine learning , linguistics , philosophy , genetics , biology , economics , market economy , cell , programming language
The fine-grained sentiment mining based on online review data is helpful to analyze user pain points and improve user experience and business marketing. A fine-grained sentiment analysis model based on Bert is proposed. Commodity feature words and emotion words are extracted by dependency parsing; the polarity of emotion was analyzed by the combination of Bert and emotional words; through the corresponding emotional polarity of feature words, we can get the emotional tendency of commodity features. The experimental results in movie review data set and film review data set show that, the accuracy and F1 value of Bert with feature words and emotional words were 94.67% and 94.55%, which were better than other models.