Data from: Lexicon-enhanced sentiment analysis framework using rule-based classification scheme

With the rapid increase in social networks and blogs, the social media
services are increasingly being used by online communities to share their
views and experiences about a particular product, policy and event. Due to
economic importance of these reviews, there is growing trend of writing
user reviews to promote a product. Nowadays, users prefer online blogs and
review sites to purchase products. Therefore, user reviews are considered
as an important source of information in Sentiment Analysis (SA)
applications for decision making. In this work, we exploit the wealth of
user reviews, available through the online forums, to analyze the semantic
orientation of words by categorizing them into +ive and -ive classes to
identify and classify emoticons, modifiers, general-purpose and
domain-specific words expressed in the public’s feedback about the
products. However, the un-supervised learning approach employed in
previous studies is becoming less efficient due to data sparseness, low
accuracy due to non-consideration of emoticons, modifiers, and presence of
domain specific words, as they may result in inaccurate classification of
users’ reviews. Lexicon-enhanced sentiment analysis based on Rule-based
classification scheme is an alternative approach for improving sentiment
classification of users’ reviews in online communities. In addition to the
sentiment terms used in general purpose sentiment analysis, we integrate
emoticons, modifiers and domain specific terms to analyze the reviews
posted in online communities. To test the effectiveness of the proposed
method, we considered users reviews in three domains. The results obtained
from different experiments demonstrate that the proposed method overcomes
limitations of previous methods and the performance of the sentiment
analysis is improved after considering emoticons, modifiers, negations,
and domain specific terms when compared to baseline methods.