A Lexical Updating Algorithm for Sentiment Analysis on Chinese Movie Reviews

With the prevalence of Internet, sentiment analysis gets popularity among the world. Researchers have made use of kinds of online documents like commodities reivews and movie reviews as training samples to train their models and classfiers, by which they could speculate the underlying emotion in new ones. Douban is a Chinese online community where users share their personal reviews to express their feelings about movies. Those Chinese movie reviews were utilized by us to train our lexicon-based model. Yet multiple words in a ready-made lexicon do not agree with the movie reviews in a specific domain, which means the original lexicon acquires being updated to gain higher accuracy. In this paper we introduce a lexical updating algorithm based on a widely used lexicon. After turns of training of updating, this lexicon is capable of classifying sentiment among movie reviews. The experimental result shows our model using the updated lexicon could get a better performance than the primitive lexicon-based model.

[1]  Lina Zhou,et al.  Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[2]  Gultekin Özsoyoglu,et al.  Movie review analysis: Emotion analysis of IMDb movie reviews , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[3]  Miao Li,et al.  An combined sentiment classification system for SIGHAN-8 , 2015, SIGHAN@IJCNLP.

[4]  Zhaoxia Wang,et al.  Lexicon Knowledge Extraction with Sentiment Polarity Computation , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[5]  Kai Zhao,et al.  A hybrid method for sentiment classification in Chinese Movie Reviews based on sentiment labels , 2015, 2015 International Conference on Asian Language Processing (IALP).

[6]  Sanjeev Ahuja,et al.  Sentiment analysis of movie reviews: A study on feature selection & classification algorithms , 2016, 2016 International Conference on Microelectronics, Computing and Communications (MicroCom).

[7]  Ye Fei,et al.  Simultaneous Support Vector selection and parameter optimization using Support Vector Machines for sentiment classification , 2016, 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS).

[8]  J. Koenderink.  Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.

[9]  P. Waila,et al.  Sentiment analysis of Movie reviews and Blog posts , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[10]  S. Foroozan,et al.  Improving Sentiment Classification Accuracy of Financial News Using N-Gram Approach and Feature Weighting Methods , 2015, 2015 2nd International Conference on Information Science and Security (ICISS).

[11]  Xinhuai Tang,et al.  Sentiment analysis for short Chinese text based on character-level methods , 2017, 2017 9th International Conference on Knowledge and Smart Technology (KST).

[12]  Hee Yong Youn,et al.  Enhanced Naive Bayes Classifier for real-time sentiment analysis with SparkR , 2016, 2016 International Conference on Information and Communication Technology Convergence (ICTC).

[13]  Sumit Gupta,et al.  A novel dictionary-based classification algorithm for opinion mining , 2016, 2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN).

[14]  Yung-Chun Chang,et al.  Sentiment analysis on Chinese movie review with distributed keyword vector representation , 2016, 2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

[15]  Wang Qian.  Text Sentiment Classification Research Based on Semantic Comprehension , 2010 .

[16]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[17]  Kurt Junshean Espinosa,et al.  Optimizing Support Vector Machine in classifying sentiments on product brands from Twitter , 2014, IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications.

[18]  Zhong Yao,et al.  Research on Semantic Orientation Classification of Chinese Online Product Reviews Based on Multi-Aspect Sentiment Analysis , 2016, 2016 IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT).

[19]  Sanjida Akter,et al.  Sentiment analysis on facebook group using lexicon based approach , 2016, 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT).

[20]  Preslav Nakov,et al.  Combining Word-Level and Character-Level Models for Machine Translation Between Closely-Related Languages , 2012, ACL.