Title: Mining hidden opinions from objective sentences

Authors: Farek Lazhar

Addresses: Department of Computer Science, University of Guelma, BP 401 Guelma 24000, Algeria

Abstract: Sentiment analysis and opinion mining is a very popular and active research area in natural language processing, it deals with structured and unstructured data to identify and extract people's opinions, sentiments and emotions in many resources of subjectivity such as product reviews, blogs, social networks, etc. All existing feature-level opinion mining approaches deal with the detection of subjective sentences and eliminate objective ones before extracting explicit features and their related positive or negative polarities. However, objective sentences can carry implicit opinions and a lack attention given to such sentences can adversely affect the obtained results. In this paper, we propose a classification-based approach to extract implicit opinions from objective sentences. Firstly, we apply a rule-based approach to extract explicit feature-opinion pairs from subjective sentences. Secondly, in order to build a classification model, we construct a training corpus based on extracted explicit feature-opinion pairs and subjective sentences. Lastly, mining implicit feature-opinion pairs from objective sentences is formulated into a text classification problem using the model previously built. Tested on customer reviews in three different domains, experimental results show the effectiveness of mining opinions from objective sentences.

Keywords: opinion mining; hidden opinion; objectivity; subjectivity; supervised learning.

DOI: 10.1504/IJDMMM.2018.092534

International Journal of Data Mining, Modelling and Management, 2018 Vol.10 No.2, pp.113 - 126

Received: 17 Nov 2016
Accepted: 11 Aug 2017
Published online: 24 Jun 2018 *

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