Apply word vectors for sentiment analysis of APP reviews

Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features, we achieve F1 85.77%, the recall 85.20%, and the accuracy 86.35% for text sentiment analysis of APP reviews.

[1]  Yoshua Bengio,et al.  Word Representations: A Simple and General Method for Semi-Supervised Learning , 2010, ACL.

[2]  Ting Liu,et al.  Collocation Polarity Disambiguation Using Web-based Pseudo Contexts , 2012, EMNLP.

[3]  Fu Wei.  Opinion Words Expansion Based on Dependency Parsing , 2012 .

[4]  Li Yazhe.  Research of Sentiment Classification Based on the Chinese Stock Blog , 2015 .

[5]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[6]  Wei Gao,et al.  Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities , 2011, EMNLP.

[7]  Ahmed E. Hassan,et al.  What Do Mobile App Users Complain About? , 2015, IEEE Software.

[8]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

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

[10]  Yang Ai-min.  Classification approach of Chinese texts sentiment based on semantic lexicon and naive Bayesian , 2010 .

[11]  Xin Rong,et al.  word2vec Parameter Learning Explained , 2014, ArXiv.

[12]  Christopher Potts,et al.  Learning Word Vectors for Sentiment Analysis , 2011, ACL.

[13]  Kevin Gimpel,et al.  Tailoring Continuous Word Representations for Dependency Parsing , 2014, ACL.

[14]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[15]  Nathan Schneider,et al.  Association for Computational Linguistics: Human Language Technologies , 2011 .

[16]  Du Li-pin.  New Word Detection Based on an Improved PMI Algorithm for Enhancing Segmentation System , 2016 .