A Literature Review on Sentiment Analysis and its Foundational Technologies

Sentiment Analysis is the computational treatment of opinion or sentiment expressed in a source of data. A cross-section of Natural Language Processing and Machine Learning, Sentiment Analysis applications deal with opinions as a tangible commodity often used to leverage big data for the sake of gaining a competitive advantage. To date it appears Sentiment Analysis has no formal frameworks or seminal texts that one can turn to for an accessible but comprehensive look into the field. This literature review based paper addresses this absence by providing a technical summary of common modern frameworks, the taxonomy of its parent fields and a brief analysis of related research dating back to the early 1990s.

[1]  David R. Karger,et al.  Tackling the Poor Assumptions of Naive Bayes Text Classifiers , 2003, ICML.

[2]  Maite Taboada,et al.  Sentiment analysis of player chat messaging in the video game StarCraft 2: Extending a lexicon-based model , 2017, Knowl. Based Syst..

[3]  Vaibhavi N Patodkar,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2016 .

[4]  Sanjiv Sharma,et al.  A Literature Review on Architecture, Classification Technique and Challenges of Sentiment Analysis , 2016 .

[5]  Ulrich W. Eisenecker,et al.  AI: The Tumultuous History of the Search for Artificial Intelligence , 1995 .

[6]  Soo-Min Kim,et al.  Determining the Sentiment of Opinions , 2004, COLING.

[7]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[8]  D Baker,et al.  Global properties of the mapping between local amino acid sequence and local structure in proteins. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Valerie Guralnik,et al.  A scalable algorithm for clustering protein sequences , 2001, BIOKDD.

[10]  Xue Bai,et al.  Predicting consumer sentiments from online text , 2011, Decis. Support Syst..

[11]  Christopher M. Danforth,et al.  Twitter reciprocal reply networks exhibit assortativity with respect to happiness , 2011, J. Comput. Sci..

[12]  Hong Yu,et al.  Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences , 2003, EMNLP.

[13]  Wei Zhang,et al.  Opinion retrieval from blogs , 2007, CIKM '07.

[14]  Japinder Singh,et al.  Feature-based opinion mining and ranking , 2012, J. Comput. Syst. Sci..

[15]  Aniruddha Datta,et al.  Application of big data analytics in process safety and risk management , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[16]  Hiroshi Kanayama,et al.  Fully Automatic Lexicon Expansion for Domain-oriented Sentiment Analysis , 2006, EMNLP.

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

[18]  Wolfgang Nejdl,et al.  How valuable is medical social media data? Content analysis of the medical web , 2009, Inf. Sci..

[19]  David D. Lewis,et al.  Representation and Learning in Information Retrieval , 1991 .

[20]  C. D. Kemp,et al.  Kendall's Advanced Theory of Statistics, Volume 1, Distribution Theory. , 1988 .

[21]  Jeonghee Yi,et al.  Sentiment analysis: capturing favorability using natural language processing , 2003, K-CAP '03.

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

[23]  Yunhe Pan,et al.  Heading toward Artificial Intelligence 2.0 , 2016 .

[24]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[25]  Liu Peng,et al.  Knowledge Acquisition Approach Based On Svm In An Online Aided Decision System For Food Processing Quality And Safety , 2014 .

[26]  Abdelhak Lakhouaja,et al.  Data science in light of natural language processing , 2018 .

[27]  D. Baker,et al.  Recurring local sequence motifs in proteins. , 1995, Journal of molecular biology.

[28]  Claire Cardie,et al.  Using natural language processing to improve eRulemaking: project highlight , 2006, DG.O.

[29]  Fidelson Tanzil,et al.  A Comparison of Text Classification Methods k-NN, Naïve Bayes, and Support Vector Machine for News Classification , 2018 .

[30]  Kathleen R. McKeown,et al.  Predicting the semantic orientation of adjectives , 1997 .

[31]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[32]  Yi Pan,et al.  Mining protein sequence motifs representing common 3D structures , 2005, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05).

[33]  Songbo Tan,et al.  An effective refinement strategy for KNN text classifier , 2006, Expert Syst. Appl..

[34]  Um-e-Ghazia,et al.  CRITICAL REVIEW OF SENTIMENT ANALYSIS TECHNIQUES , 2014 .

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

[36]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[37]  Matt Thomas,et al.  Get out the vote: Determining support or opposition from Congressional floor-debate transcripts , 2006, EMNLP.

[38]  Steven L. Tanimoto The elements of artificial intelligence an introduction using lisp computer science press (1987) , 1987 .

[39]  Youxian Sun,et al.  Fuzzy support vector machine for regression estimation , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[40]  Desheng Dash Wu,et al.  Using text mining and sentiment analysis for online forums hotspot detection and forecast , 2010, Decis. Support Syst..

[41]  Alison Huettner,et al.  Fuzzy Typing for Document Management , 2000 .