Fuzzy approach for sentiment analysis

Chris  Jefferson; Han Liu; Mihaela Cocea

doi:10.1109/FUZZ-IEEE.2017.8015577

Fuzzy approach for sentiment analysis

Chris Jefferson
, Han Liu
, Mihaela Cocea

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Sentiment analysis aims to identify the polarity of a document through natural language processing, text analysis and computational linguistics. Over the last decade, there has been much focus on sentiment analysis as the data available on-line has grown exponentially to include many sentiment based documents (reviews, feedback, articles). Many approaches consider machine learning techniques or statistical analysis, but there has been little use of the fuzzy classifiers in this field especially considering the ambiguity of language and the suitability of fuzzy approaches to deal with this ambiguity. This paper proposes a fuzzy rule based system for sentiment analysis, which can offer more refined outputs through the use of fuzzy membership degrees. We compare the performance of our proposed approach with commonly used sentiment classifiers (e.g. Decision Trees, Naïve Bayes) which are known to perform well in this task. The experimental results indicate that our fuzzy-based approach performs marginally better than the other algorithms. In addition, the fuzzy approach allows the definition of different degrees of sentiment without the need to use a larger number of classes.

Original language	English
Title of host publication	2017 IEEE Conference on Fuzzy Systems (FUZZ-IEE)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	978-1509060344
ISBN (Print)	978-1509060351
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2017.8015577
Publication status	Published - 24 Aug 2017
Event	2017 IEEE International Conference on Fuzzy Systems - Royal-Continental Hotel, Naples, Italy Duration: 9 Jul 2017 → 12 Jul 2017 https://www.fuzzieee2017.org/index.html

Publication series

Name	IEEE FUZZ-IEEE Proceedings Series
Publisher	IEEE
ISSN (Electronic)	1558-4739

Conference

Conference	2017 IEEE International Conference on Fuzzy Systems
Country/Territory	Italy
City	Naples
Period	9/07/17 → 12/07/17
Internet address	https://www.fuzzieee2017.org/index.html

Keywords

data mining
machine learning
fuzzy rule based systems
text classification
sentiment analysis

Access to Document

10.1109/FUZZ-IEEE.2017.8015577

FuzzIEEE2017
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Accepted author manuscript (Post-print), 456 KB

Cite this

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title = "Fuzzy approach for sentiment analysis",

abstract = "Sentiment analysis aims to identify the polarity of a document through natural language processing, text analysis and computational linguistics. Over the last decade, there has been much focus on sentiment analysis as the data available on-line has grown exponentially to include many sentiment based documents (reviews, feedback, articles). Many approaches consider machine learning techniques or statistical analysis, but there has been little use of the fuzzy classifiers in this field especially considering the ambiguity of language and the suitability of fuzzy approaches to deal with this ambiguity. This paper proposes a fuzzy rule based system for sentiment analysis, which can offer more refined outputs through the use of fuzzy membership degrees. We compare the performance of our proposed approach with commonly used sentiment classifiers (e.g. Decision Trees, Na{\"i}ve Bayes) which are known to perform well in this task. The experimental results indicate that our fuzzy-based approach performs marginally better than the other algorithms. In addition, the fuzzy approach allows the definition of different degrees of sentiment without the need to use a larger number of classes.",

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author = "Chris Jefferson and Han Liu and Mihaela Cocea",

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note = "2017 IEEE International Conference on Fuzzy Systems ; Conference date: 09-07-2017 Through 12-07-2017",

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AU - Liu, Han

AU - Cocea, Mihaela

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N2 - Sentiment analysis aims to identify the polarity of a document through natural language processing, text analysis and computational linguistics. Over the last decade, there has been much focus on sentiment analysis as the data available on-line has grown exponentially to include many sentiment based documents (reviews, feedback, articles). Many approaches consider machine learning techniques or statistical analysis, but there has been little use of the fuzzy classifiers in this field especially considering the ambiguity of language and the suitability of fuzzy approaches to deal with this ambiguity. This paper proposes a fuzzy rule based system for sentiment analysis, which can offer more refined outputs through the use of fuzzy membership degrees. We compare the performance of our proposed approach with commonly used sentiment classifiers (e.g. Decision Trees, Naïve Bayes) which are known to perform well in this task. The experimental results indicate that our fuzzy-based approach performs marginally better than the other algorithms. In addition, the fuzzy approach allows the definition of different degrees of sentiment without the need to use a larger number of classes.

AB - Sentiment analysis aims to identify the polarity of a document through natural language processing, text analysis and computational linguistics. Over the last decade, there has been much focus on sentiment analysis as the data available on-line has grown exponentially to include many sentiment based documents (reviews, feedback, articles). Many approaches consider machine learning techniques or statistical analysis, but there has been little use of the fuzzy classifiers in this field especially considering the ambiguity of language and the suitability of fuzzy approaches to deal with this ambiguity. This paper proposes a fuzzy rule based system for sentiment analysis, which can offer more refined outputs through the use of fuzzy membership degrees. We compare the performance of our proposed approach with commonly used sentiment classifiers (e.g. Decision Trees, Naïve Bayes) which are known to perform well in this task. The experimental results indicate that our fuzzy-based approach performs marginally better than the other algorithms. In addition, the fuzzy approach allows the definition of different degrees of sentiment without the need to use a larger number of classes.

KW - data mining

KW - machine learning

KW - fuzzy rule based systems

KW - text classification

KW - sentiment analysis

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DO - 10.1109/FUZZ-IEEE.2017.8015577

M3 - Conference contribution

SN - 978-1509060351

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BT - 2017 IEEE Conference on Fuzzy Systems (FUZZ-IEE)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Fuzzy Systems

Y2 - 9 July 2017 through 12 July 2017

ER -

Fuzzy approach for sentiment analysis

Abstract

Publication series

Conference

Keywords

Access to Document

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Cite this