Lexical Based Semantic Orientation of Online Customer Reviews and Blogs
release_5dm2hujqxffgvhxxrvazmtbboe
by
Aurangzeb khan, Khairullah khan, Shakeel Ahmad, Fazal Masood Kundi,
Irum Tareen, Muhammad Zubair Asghar
2016
Abstract
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.
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