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IJSTR >> Volume 9 - Issue 5, May 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Literature Survey On Sentiment Analysis Techniques Involving Social Media And Online Platforms

[Full Text]

 

AUTHOR(S)

Raktim Kumar Dey, Debabrata Sarddar, Indranil Sarkar, Rajesh Bose, Sandip Roy

 

KEYWORDS

Machine Learning, Naïve Bayes, Natural Language Processing (NLP), Neural Network, Opinion Mining (OM), Sentiment Analysis (SA), Social Media, Support Vector Machine (SVM).

 

ABSTRACT

Activities that take place or are influenced as a result of decisions being made are influenced by opinions at the root level. Analysis of opinions or sentiment analysis plays a vital role in trying to make as close approximation as possible. This is an extremely important aspect given that carefully planned and executed sentiment analyses can yield better and more accurate forecasts in politics as well as in business. At the base level, sentiment analysis stems from opinions shared or expressed by individuals and users. In the Internet space that permeates almost every known sphere and area of human activity on our planet, data in millions of bytes are posted and shared by individuals on social networking platforms, blogs, product review sites, and various other web forums. The potential to harvest such information and analyse the data can yield vital insights into how products, services, political personalities, companies, governments, etc. are perceived and viewed. Sentiment Analysis can engage multiple challenges such as accuracy-related issues, binary classification problem, data sparsity problem and polarity shift. While there have been various methods that have been postulated and developed for sentiment analysis, there yet remains to be an efficient approach in extracting and producing accurate sentiment analysis on a consistent basis. Although machine learning algorithms have come a long way, with Naïve Bayes, Support Vector Machine and Maximum Entropy being the significant ones to feature prominently in research and mainstream use, sentiment classification by category involving positive and negative sentiments, is a topic of research interest in its own right. This paper presents a survey on prominent Sentiment Analysis approaches and methodologies and seeks to submit a clear evaluation report upon which grounds for further research can be based.

 

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