A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words
Open Access
- 1 January 2015
- journal article
- research article
- Published by Hindawi Limited in Computational Intelligence and Neuroscience
- Vol. 2015, 1-13
- https://doi.org/10.1155/2015/525437
Abstract
With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment words only consider the positive and negative polarity by the Cantor set, and no attention is paid to the fuzziness of the polarity intensity of sentiment words. In order to improve the performance, we propose a fuzzy computing model to identify the polarity of Chinese sentiment words in this paper. There are three major contributions in this paper. Firstly, we propose a method to compute polarity intensity of sentiment morphemes and sentiment words. Secondly, we construct a fuzzy sentiment classifier and propose two different methods to compute the parameter of the fuzzy classifier. Thirdly, we conduct extensive experiments on four sentiment words datasets and three review datasets, and the experimental results indicate that our model performs better than the state-of-the-art methods.Keywords
Funding Information
- National Natural Science Foundation of China (U1405254, 61472092, 61402115, 61271392)
This publication has 15 references indexed in Scilit:
- Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviewsKnowledge-Based Systems, 2013
- Short text classification based on strong feature thesaurusJournal of Zhejiang University SCIENCE C, 2012
- Sentiment Analysis and Opinion MiningSynthesis Lectures on Human Language Technologies, 2012
- Lexicon-Based Methods for Sentiment AnalysisComputational Linguistics, 2011
- Chinese word segmentation as morpheme-based lexical chunkingInformation Sciences, 2008
- Open-Domain Question–AnsweringFoundations and Trends® in Information Retrieval, 2006
- Measuring praise and criticismACM Transactions on Information Systems, 2003
- Text categorization with Support Vector Machines: Learning with many relevant featuresLecture Notes in Computer Science, 1998
- Naive (Bayes) at forty: The independence assumption in information retrievalLecture Notes in Computer Science, 1998
- PRUF—a meaning representation language for natural languagesInternational Journal of Man-Machine Studies, 1978