Consumer insight mining: Aspect based Twitter opinion mining of mobile phone reviews

Rathan, M and Hulipalled, Vishwanath R and Venugopal, K R and Patnaik, L M (2018) Consumer insight mining: Aspect based Twitter opinion mining of mobile phone reviews. In: APPLIED SOFT COMPUTING, 68 . pp. 765-773.

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Abstract

Micro-blogging sites such as Twitter are often considered as rich source of opinions of the masses towards products. The character length limit in tweets encourages people to use emojis, emoticons and out of vocabulary words. Due to the huge volume of tweets being generated, it is difficult to manually label tweets and create a supervised learning model for sentiment analysis. Looking into these challenges, the research paper aims to create a feature level sentiment analysis model for Twitter data mining including features such as emoji detection, spelling correction and emoticon detection. The proposed model consists of automated training data labeling by using lexicon based approach. It is an ontology based system with the domain of ``Smartphone''. In addition to the general lexicon used, a set of lexicons specific for each attribute of the domain ``Smartphone'' are used to improve classification accuracy for training data generation. This is used to classify tweets obtained about a particular mobile phone using SVM classifier. Experimental results show that the classifier based on automated training data provides good accuracy. It also demonstrates the importance of emoji detection and the attribute specific lexicons which help improve the classification accuracy. (C) 2017 Elsevier B.V. All rights reserved.

Item Type:	Journal Article
Publication:	APPLIED SOFT COMPUTING
Publisher:	ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
Additional Information:	Copy right of this article belong to ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited:	02 Jul 2018 14:52
Last Modified:	02 Jul 2018 14:52
URI:	http://eprints.iisc.ac.in/id/eprint/60113

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