Optimizing N-gram based text feature selection in sentiment analysis for commercial products in Twitter through polarity lexicons

This study aims to optimize N-gram based text feature selection in sentiment analysis for commercial products in twitter through polarity lexicons. This can be done by merging dictionary-based weighing with naïve-Bayes classification of sentiments. The study is still ongoing but partial results show potential.

[1]  João Gama,et al.  MARKETING RESEARCH: THE ROLE OF SENTIMENT ANALYSIS , 2013 .

[2]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..

[3]  Pablo Daniel Azar,et al.  Sentiment Analysis in Financial News , 2009 .

[4]  H. Khoshnevis.  Comparing Feature Sets and Classifiers for Sentiment Analysis of Opinionated Free Text , 2012 .

[5]  Jing Wang,et al.  The Sentiment Trend Analysis of Twitter Based on Set Pair Contact Degree , 2013 .

[6]  Archana Shukla.  Sentiment Analysis of Document Based on Annotation , 2011, ArXiv.

[7]  Awais Athar,et al.  Sentiment Analysis of Citations using Sentence Structure-Based Features , 2011, ACL.

[8]  P. Srinivasan,et al.  Sentiment analysis within and across social media streams , 2012 .

[9]  Heng Ji,et al.  Combining Social Cognitive Theories with Linguistic Features for Multi-genre Sentiment Analysis , 2012, PACLIC.

[10]  Mohamed M. Mostafa,et al.  More than words: Social networks' text mining for consumer brand sentiments , 2013, Expert Syst. Appl..

[11]  Akshi Kumar,et al.  Sentiment Analysis on Twitter , 2012 .

[12]  G. Gebremeskel Gebrekirstos.  Sentiment Analysis of Twitter Posts About news , 2011 .

[13]  Ana Sufian,et al.  Social Media Data Mining and Inference system based on Sentiment Analysis , 2011 .

[14]  Hironori Mizuguchi,et al.  Opinion Sentence and Topic Relevant Sentence Extraction by Using Coherent Structure among the Sentences , 2008, NTCIR.

[15]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..