Sentiment Analysis Methods and Approach: Survey

Authors Name: Saurabh Dorle1, Dr. Nitin N. Pise2 

1ME-II year, Department of Computer Engineering, Maharashtra Institute of Technology, Pune, Maharashtra, India

2Department of Computer Engineering, Maharashtra institute of Technology, Pune, Maharashtra, India

ABSTRACT:

Nowadays, social media present a valuable source for business decision support and Data Analytics is widely used in many industries and organization to make a better Business decision. By applying analytics to the data the enterprises brings a great change in their way of planning and decision making. Sentiment analysis or opinion mining plays a significant role in our daily decision making process. These decisions may range from purchasing a product such as mobile phone to reviewing the movie to making investments all the decisions will have a huge impact on the daily life.  Sentiment Analysis or Opinion analysis is performed to identify the opinion of peoples. It can be performed using Lexicon Based approach or Machine Learning based approach. Some methods are still not efficient in extracting the sentiment features from the given content of text. Naive Bayes, Support Vector Machine are the machine learning algorithms used for sentiment analysis which has only a limited sentiment classification category ranging between positive and negative. Even though the advancement in sentiment Analysis technique there are various issues still to be noticed and make the analysis not accurately and efficiently. So this paper presents the survey on various sentiment Analysis methodologies and approaches. This will be helpful to earn clear knowledge about sentiment analysis methodologies.

Keywords: Data Analytics, sentiment/opinion Analysis, Decision making.