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Title: Cognitive-inspired domain adaptation of sentiment lexicons
Authors: Xing, Frank Z.
Pallucchini, Filippo
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Xing, F. Z., Pallucchini, F. & Cambria, E. (2019). Cognitive-inspired domain adaptation of sentiment lexicons. Information Processing and Management, 56(3), 554-564.
Journal: Information Processing and Management
Abstract: Sentiment lexicons are essential tools for polarity classification and opinion mining. In contrast to machine learning methods that only leverage text features or raw text for sentiment analysis, methods that use sentiment lexicons embrace higher interpretability. Although a number of domain-specific sentiment lexicons are made available, it is impractical to build an ex ante lexicon that fully reflects the characteristics of the language usage in endless domains. In this article, we propose a novel approach to simultaneously train a vanilla sentiment classifier and adapt word polarities to the target domain. Specifically, we sequentially track the wrongly predicted sentences and use them as the supervision instead of addressing the gold standard as a whole to emulate the life-long cognitive process of lexicon learning. An exploration-exploitation mechanism is designed to trade off between searching for new sentiment words and updating the polarity score of one word. Experimental results on several popular datasets show that our approach significantly improves the sentiment classification performance for a variety of domains by means of improving the quality of sentiment lexicons. Case-studies also illustrate how polarity scores of the same words are discovered for different domains.
ISSN: 0306-4573
DOI: 10.1016/j.ipm.2018.11.002
Rights: © 2018 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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