An Intelligent Logistic Regression Approach for Verb Expression’s Sentiment Analysis

Sentiment analysis of text has tremendous value in many fields. But verb expression is absent, while lots of researchers concentrate on identifying opinions from adjective, adverb, and noun expressions in recent years. In this paper, we find that verb expressions in a sentence can be more important because verb expressions not only imply opinions but also give a direct way for enterprise to improve their products. It is meaningful that the verb expressions are extracted and analyzed. In order to deal with this problem, we propose a new method of linear regression optimized by particle swarm optimization to analyze verb expression extracted from reviews. Since our training data is obtained from titles of reviews whose labels are automatically inferred from review ratings, our method is able to work without manual involvement. Experimental results demonstrate our approach has great performance in terms of both precision and efficiency.

[1]  S Kavitha,et al.  A comparative analysis on linear regression and support vector regression , 2016 .

[2]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[3]  Arjun Mukherjee,et al.  Extracting Verb Expressions Implying Negative Opinions , 2015, AAAI.

[4]  Lukás Burget,et al.  Neural network based language models for highly inflective languages , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  Ali A. Ghorbani,et al.  Verb Oriented Sentiment Classification , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[6]  Xiaohua Yang,et al.  Features-level Sentiment Analysis of Movie Reviews , 2015 .

[7]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

[8]  Omer Levy,et al.  word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method , 2014, ArXiv.

[9]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[10]  Danushka Bollegala,et al.  Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus , 2013, IEEE Transactions on Knowledge and Data Engineering.

[11]  Diego Reforgiato Recupero,et al.  AVA: Adjective-Verb-Adverb Combinations for Sentiment Analysis , 2008, IEEE Intelligent Systems.

[12]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[13]  Arjun Mukherjee,et al.  Aspect Extraction with Automated Prior Knowledge Learning , 2014, ACL.