Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 74-79.

• Intelligent Computing • Previous Articles     Next Articles

Movie Review Professionalism Classification Using LSTM and Features Fusion

WU Fan, LI Shou-shan, ZHOU Guo-dong   

  1. Institute of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: Movie Reviews on social networks usually include professional reviews written by professional critics,as well as non-professional reviews written by ordinary audience,and it is of great value to distinguish whether online film reviews are professional reviews for film quality evaluation.Due to the fact that film review is a short text book with irregular words and sparse features,the traditional text feature selection method and traditional classification model cannot fully apply to the classification of film review’s professional level.Therefore,the paper mainly studied movie review professionalism classification based on neural network model,that is judging whether it is professional review or non-professional review.The representation of different features is learned through neural network-based LSTM model,including word-based representation,part-of-speech representation,and representation based on dependencies,and valid text features are learned and captured by fusing different feature representations to help review professionalism classification.The method was experimented on the Rotten Tomatoes dataset of the famous American film review website.The experimental results show that the classification accuracy rate of the model combining part-of-speech and dependency is 88.30%,which is 3.66% higher than the benchmark model only using word features.This shows that the method of introducing part-of-speech features and dependency features into the model can effectively improve the effectiveness of professional classification of reviews.

Key words: LSTM, Multi-feature fusion, Neural networks, Review professionalism classification, SVM

CLC Number: 

  • TP391
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