Review Sentiment Analysis Based on Deep Learning

With rapid development of E-commerce platforms, automated review sentiment analysis for commodities becomes a research focus, with main purpose to extract potential information within reviews for decision making of consumers. Traditional methods have made some progress on document level sentiment analysis, but with tremendous increasing of data scale, how to process high dimension of data fast and effectively becomes the largest limitation. In this paper, we import deep neural network which is appropriate for high dimension data analysis, and propose a framework of sentiment analysis based on deep learning. Experiments on different data scale and different domains show that the proposed method can solve high dimensional problem with good performance.

[1]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[2]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[3]  Bo Xu,et al.  Recursive Deep Learning for Sentiment Analysis over Social Data , 2014, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[4]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[5]  Rui Xia,et al.  Exploring the Use of Word Relation Features for Sentiment Classification , 2010, COLING.

[6]  Qingcai Chen,et al.  Fuzzy deep belief networks for semi-supervised sentiment classification , 2014, Neurocomputing.

[7]  Mark Levene,et al.  Combining lexicon and learning based approaches for concept-level sentiment analysis , 2012, WISDOM '12.

[8]  Ali Selamat,et al.  Combination of Multi-view Multi-source Language Classifiers for Cross-Lingual Sentiment Classification , 2014, ACIIDS.

[9]  Ping Liu,et al.  Sentiment Classification Based on AS-LDA Model , 2014, ITQM.

[10]  Hidekazu Yanagimoto,et al.  Document similarity estimation for sentiment analysis using neural network , 2013, 2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS).

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

[12]  Chao Li,et al.  Structural information aware deep semi-supervised recurrent neural network for sentiment analysis , 2015, Frontiers of Computer Science.

[13]  Roliana Ibrahim,et al.  A Novel Feature Reduction Method in Sentiment Analysis , 2014 .

[14]  Dong Li,et al.  Word Vector Modeling for Sentiment Analysis of Product Reviews , 2014, NLPCC.

[15]  Yoshua Bengio,et al.  Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.

[16]  Zhaoxia Wang,et al.  Enhancing Machine-Learning Methods for Sentiment Classification of Web Data , 2014, AIRS.

[17]  Yi Yang,et al.  Incorporating conditional random fields and active learning to improve sentiment identification , 2014, Neural Networks.

[18]  Andrea Esuli,et al.  Multi-Faceted Rating of Product Reviews , 2009, ERCIM News.

[19]  Nigel Collier,et al.  Sentiment Analysis using Support Vector Machines with Diverse Information Sources , 2004, EMNLP.

[20]  Padmini Srinivasan,et al.  Exploring Feature Definition and Selection for Sentiment Classifiers , 2011, ICWSM.