Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141504
Title: Sentiment analysis : an automatic contextual analysis and ensemble clustering approach and comparison
Authors: AL-Sharuee, Murtadha Talib
Liu, Fei
Pratama, Mahardhika
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: AL-Sharuee, M. T., Liu, F., & Pratama, M. (2018). Sentiment analysis : an automatic contextual analysis and ensemble clustering approach and comparison. Data & Knowledge Engineering, 115, 194-213. doi:10.1016/j.datak.2018.04.001
Journal: Data & Knowledge Engineering
Abstract: Product reviews are one of the most important resources to determine public sentiment. The existing literature on review sentiment analysis mostly utilizes supervised models, which usually suffer from domain-dependency and require expensive manual labelling effort to provide training data. This article addresses these issues by describing a completely automatic and unsupervised approach to sentiment analysis. The method consists of two phases, which are contextual analysis and unsupervised ensemble learning. In the implementation of both phases, a sentiment lexicon, SentiWordNet, is deployed. Using effective contextual procedures and modifying the base learning component (the k-means algorithm) results in developing a successful approach to sentiment analysis which can overcome the domain-dependency and the labelling cost problems. The results show that the proposed nonrandom initialization of k-means yields a significant improvement compared to other algorithms. In terms of accuracy and performance, the proposed method is effective compared to supervised and unsupervised approaches. We also introduce new sentiment analysis problems relating to Australian airlines and home builders which could be potential benchmark problems in the sentiment analysis field. Our experiments on datasets from different domains show that contextual analysis and the ensemble phases improve the clustering performance in term of accuracy, stability and generalizability.
URI: https://hdl.handle.net/10356/141504
ISSN: 0169-023X
DOI: 10.1016/j.datak.2018.04.001
Rights: © 2018 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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