Lexical Based Semantic Orientation of Online Customer Reviews and Blogs-J-Am Sci 10(8) 143_147--07-june-2014.pdf

Rapid increase in internet users along with growing power of online review sites and social media has

given birth to sentiment analysis or opinion mining, which aims at determining what other people think and

comment. Sentiments or Opinions contain public generated content about products, services, policies and politics.

People are usually interested to seek positive and negative opinions containing likes and dislikes, shared by users for

features of particular product or service. This paper proposed sentence-level lexical based domain independent

sentiment classification method for different types of data such as reviews and blogs. The proposed method is based

on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for semantic orientation. The

relations and glosses of these dictionaries provide solution to the domain portability problem. The method performs

better than word and text level corpus based machine learning methods for semantic orientation. The results show

the proposed method performs better as it shows precision of 87% and 83% at document and sentence levels

respectively for online comments.