Abstract
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.
Highlights
The Web is a huge repository of facts and opinions available for people around the world about a particular product, service, issue, policy and health-care [1]
With the rapid increase in social media sites, individuals are relying on user review sites for exchanging their personal information, experiences and knowledge [2].The main focus of the studies in this area has been on issues, such as sentiment detection, sentiment classification at aspect, word, sentence and review levels, opinion spam detection, and context aware sentiment analysis [3]
The method is based on the three major steps: 1) firstly, we acquire the data from different online resources; 2) in step, the noise reduction is performed by applying different preprocessing techniques to refine the text that can be used for subsequent processing, and 3) different classification techniques are applied to classify the reviews into +ive, -ive or neutral
Summary
The Web is a huge repository of facts and opinions available for people around the world about a particular product, service, issue, policy and health-care [1]. Due to the growing interest in computing the exact sentiment of terms within the SA applications, the sentiment classification at word, sentence and review level become an active area of research [4]. In most cases, such large number of information seems unstructured for average internet user. The main challenges faced in developing user centric sentiment analysis applications include: (i) emoticon handling, (ii) low accuracy of the classifier in the sentiment analysis of online content, and (iii) incorrect scoring and classification of domain specific words. The sentiment score of a word is generally dependent on a particular domain and changes when a domain switch occurs
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