Applying data mining and machine learning techniques for sentiment shifter identification.
Sentiment shifters, as a set of words and expressions that can affect text polarity, play a fundamental role in opinion mining. However, the limited ability of current automated opinion mining systems in handling shifters is a major challenge. This paper presents three novel and efficient methods fo...
| Publicado en: | Language Resources & Evaluation Vol. 53; no. 2; pp. 279 - 303 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Jun2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=136891171&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 136891171 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2019 vid: 53 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 136891171 10.1007/s10579-018-9432-0 ppf: 279 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P size: 856KB tig: atl: Applying data mining and machine learning techniques for sentiment shifter identification. aug: au: Rahimi, Zeinab Shamsfard, Mehrnoush Noferesti, Samira affil: Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran University of Sistan and Baluchestan, Zahedan, Iran su: Sentiment analysis Data mining Machine learning Weighted association rule mining Corpora sug: subj: Sentiment analysis Data mining Machine learning Weighted association rule mining Corpora keyword: Association rule mining Opinion mining Sentiment shifters Shifter identification WARM ab: Sentiment shifters, as a set of words and expressions that can affect text polarity, play a fundamental role in opinion mining. However, the limited ability of current automated opinion mining systems in handling shifters is a major challenge. This paper presents three novel and efficient methods for identifying sentiment shifters in reviews in order to improve the overall accuracy of opinion mining systems: two data mining based algorithms and a machine learning based algorithm. The data mining algorithms do not need shifter tagged datasets. They use weighted association rule mining (WARM) for finding frequent patterns representing sentiment shifters from a domain-specific and a general corpus. These patterns include different kinds of shifter words such as shifter verbs and quantifiers and are able to handle both local and long-distance shifters. The items in WARM for the two designed methods are in the form of dependency relations and SRL arguments of sentences, respectively. Secondly, we implemented a supervised machine learning system based on semantic features of sentences for shifter identification and polarity classification. This method obviously needs shifter tagged dataset for shifter identification. We tested our proposed algorithms on polarity classification task for 2 domains: a specific domain (drug reviews) and a general domain. Experiments demonstrate that (1) the extracted shifters improve the performance of the polarity classification, (2) the proposed data mining methods outperform other implemented methods in shifter identification, and (3) the proposed semantic based machine learning method has the best efficiency among all implemented methods in polarity classification. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2019. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2019 holdings: @attributes: islocal: N |
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