Lexical Based Semantic Orientation of Online Customer Reviews and Blogs
This addresses the problem of domain-independent sentiment analysis for businesses and researchers, though it is incremental as it builds on existing lexical resources.
The paper tackled sentiment classification of online reviews and blogs by proposing a lexical-based method using WordNet, SentiWordNet, and user-defined dictionaries, achieving 87% precision at the document level and 83% at the sentence level.
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% and83% at document and sentence levels respectively for online comments.