Improvement of sentiment analysis via re-evaluation of objective words in SenticNet for hotel reviews.
In order to extract the correct sentiment polarity from word of mouth (WOM), a wide-scale and well-organized sentiment lexicon is generally beneficial. SenticNet is one such lexicon. However, it consists of a high proportion of objective words, which are generally considered to be of little use for...
| Published in: | Language Resources & Evaluation Vol. 55; no. 2; pp. 585 - 596 |
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| Main Authors: | , , |
| Format: | Article |
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Springer Nature
Jun2021
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=150471574&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 150471574 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2021 vid: 55 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 150471574 10.1007/s10579-020-09512-6 ppf: 585 ppct: 11 formats: fmt: @attributes: type: P size: 270KB tig: atl: Improvement of sentiment analysis via re-evaluation of objective words in SenticNet for hotel reviews. aug: au: Hung, Chihli Wu, Wan-Rong Chou, Hsien-Ming affil: Department of Information Management, Chung Yuan Christian University, Taoyuan City, Taiwan su: Hotel ratings & rankings Sentiment analysis Support vector machines Decision trees Vocabulary User-generated content sug: subj: Hotel ratings & rankings Sentiment analysis Support vector machines Decision trees Vocabulary User-generated content keyword: Objective word SenticNet Sentiment lexicon Word of mouth ab: In order to extract the correct sentiment polarity from word of mouth (WOM), a wide-scale and well-organized sentiment lexicon is generally beneficial. SenticNet is one such lexicon. However, it consists of a high proportion of objective words, which are generally considered to be of little use for sentiment classification due to their ambiguity and lack of sentiments. In the literature, there is a dearth of models that focus on this issue. An objective word appearing more frequently in positive sentences than in negative sentences implies a strong relationship in a positive sentiment orientation, and conversely, an objective word appearing more frequently in negative sentences implies a strong relationship in a negative sentiment orientation. Thus, the ratio of an objective word appearing in positive and negative sentences provides the sentiment orientation. Based on this concept, this paper re-assigns the sentiment values to the objective words in SenticNet and builds a revised SenticNet. Three classification techniques, the J48 decision tree, support vector machine, and multilayer perceptron neural network are used for classification. According to the experiments, the proposed models which extract sentiment values from the revised SenticNet, significantly outperform those models which extract sentiment values from the original non-revised SenticNet. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2021. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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