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Opinion mining using ensemble text hidden Markov models for text classification

Title
Opinion mining using ensemble text hidden Markov models for text classification
Author
이기천
Keywords
Opinion mining; Sentiment analysis; Hidden Markov models; Ensemble; Boosting; Clustering
Issue Date
2018-03
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
EXPERT SYSTEMS WITH APPLICATIONS, v. 94, page. 218-227
Abstract
With the rapid growth of social media, text mining is extensively utilized in practical fields, and opinion mining, also known as sentiment analysis, plays an important role in analyzing opinion and sentiment in texts. Methods in opinion mining generally depend on a sentiment lexicon, which is a set of predefined key words that express sentiment. Opinion mining requires proper sentiment words to be extracted in advance and has difficulty classifying sentences that imply an opinion without using any sentiment key words. This paper presents a new sentiment analysis method, based on text-based hidden Markov models (TextHMMs), for text classification that uses a sequence of words in training texts instead of a predefined sentiment lexicon. We sought to learn text patterns representing sentiment through ensemble TextHMMs. Our method defines hidden variables in TextHMMs by semantic cluster information in consideration of the co-occurrence of words, and thus calculates the sentiment orientation of sentences by fitted TextHMMs. To reflect diverse patterns, we applied an ensemble of TextHMM-based classifiers. In the experiments with a benchmark data set, we show that this method is superior to some existing methods and particularly has potential to classify implicit opinions. We also demonstrate the practicality of the proposed method in a real-life data set of online market reviews. (C) 2017 Elsevier Ltd. All rights reserved.
URI
https://www.sciencedirect.com/science/article/pii/S0957417417304979?via%3Dihubhttp://repository.hanyang.ac.kr/handle/20.500.11754/117776
ISSN
0957-4174; 1873-6793
DOI
10.1016/j.eswa.2017.07.019
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INDUSTRIAL ENGINEERING(산업공학과) > Articles
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