A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level

Orestes Appel, Francisco Chiclana, Jenny Carter, Hamido Fujita

Research output: Contribution to journalArticle

76 Citations (Scopus)

Abstract

This contribution presents a hybrid approach to Sentiment Analysis (SA) encompassing the use of semantic rules, fuzzy sets, unsupervised machine learning techniques and a sentiment lexicon improved with the support of Senti-WordNet. A Hybrid Standard Classification is first carried out, which is further enhanced into a Hybrid Advanced approach incorporating linguistic classification of semantic polarity modelled using fuzzy sets. The mechanism of the new SA methodology is illustrated by applying it to compute the polarity of a given sentence and to a benchmarking publicly available dataset: the Movie Review Dataset.
Original languageEnglish
Pages (from-to)110-124
Number of pages15
JournalKnowledge-Based Systems
Volume108
Early online date20 May 2016
DOIs
Publication statusPublished - 15 Sep 2016
Externally publishedYes

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Fuzzy sets
Semantics
Benchmarking
Linguistics
Learning systems
Hybrid approach
Sentiment analysis
WordNet
Movies
Methodology
Sentiment
Machine learning
Encompassing

Cite this

Appel, Orestes ; Chiclana, Francisco ; Carter, Jenny ; Fujita, Hamido. / A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level. In: Knowledge-Based Systems. 2016 ; Vol. 108. pp. 110-124.
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A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level. / Appel, Orestes; Chiclana, Francisco; Carter, Jenny; Fujita, Hamido.

In: Knowledge-Based Systems, Vol. 108, 15.09.2016, p. 110-124.

Research output: Contribution to journalArticle

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