Sentiment Analysis and Opinion Mining: A Survey

In this age, in this nation, public sentiment is everything. With it, nothing can fail; against it, nothing can succeed. Whoever molds public sentiment goes deeper than he who enacts statutes, or pronounces judicial decisions (Abraham Lincoln, 1858 ) [1]. It is apparent from President Lincoln's well known quote that legislators understood the force of open assumption quite a while prior. In today world, the Internet is the main source of information. An enormous amount of information and opinion online is scattered and unstructured with no machine to arrange it. Because of demand the public to know opinions about exact product and services, political issues, or social scientists. That’s led us to study of field Opining Mining and Sentiment Analysis. Opining Mining and Sentiment Analysis have recently played a significant role for researchers because analysis of online text is beneficial for the market research political issue, business intelligence, online shopping, and scientific survey from psychological. Sentiment Analysis identifies the polarity of extracted public opinions. This paper presents a survey which covers Opining Mining, Sentiment Analysis, techniques, tools and classification.

[1]  Goutam Chakraborty,et al.  Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS , 2013 .

[2]  Hari Priya,et al.  Sentiment Analysis and Opinion Mining , 2018 .

[3]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[4]  Chetan Mate.  Product Aspect Ranking using Sentiment Analysis: A Survey , 2016 .

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

[6]  Meng Wang,et al.  Product Aspect Ranking and Its Applications , 2014, IEEE Transactions on Knowledge and Data Engineering.

[7]  Claire Cardie,et al.  39. Opinion mining and sentiment analysis , 2014 .

[8]  Michael Gamon,et al.  Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis , 2004, COLING.

[9]  Yanjun Qi,et al.  Sentiment classification based on supervised latent n-gram analysis , 2011, CIKM '11.

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

[11]  João Francisco Valiati,et al.  Document-level sentiment classification: An empirical comparison between SVM and ANN , 2013, Expert Syst. Appl..

[12]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

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

[14]  Anand Mahendran,et al.  Opinion Mining For Text Classification , 2013 .

[15]  Yong Shi,et al.  The Role of Text Pre-processing in Sentiment Analysis , 2013, ITQM.

[16]  Srinath Srinivasa,et al.  A faceted characterization of the opinion mining landscape , 2014, 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS).

[17]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[18]  Felipe Bravo-Marquez,et al.  Opinion Zoom: A Modular Tool to Explore Tourism Opinions on the Web , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).