research-article

Analyzing Implicit Aspects and Aspect Dependent Sentiment Polarity for Aspect-based Sentiment Analysis on Informal Turkish Texts

Authors:

I. Hakki Toroslu,

Murat KalenderAuthors Info & Claims

MEDES '17: Proceedings of the 9th International Conference on Management of Digital EcoSystems

Pages 134 - 141

https://doi.org/10.1145/3167020.3167041

Published: 07 November 2017 Publication History

Abstract

The web provides a suitable media for users to post comments on different topics. In most of such content, authors express different opinions on different features or aspects of the topic. In aspect based sentiment analysis, it is analyzed as to for which aspect which opinion is expressed. Once aspects are available, the next important step is to match aspects with correct sentiments. In this work, we investigate enhancements for two cases in matching step: extracting implicit aspects, and sentiment words whose polarity depends on the aspect. The techniques are applied on Turkish informal texts collected from a products forum. Experimental evaluation shows that additional steps applied for these cases improve the accuracy of aspect based sentiment analysis.

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Cited By

Soni PRambola R(2022)A Survey on Implicit Aspect Detection for Sentiment Analysis: Terminology, Issues, and ScopeIEEE Access10.1109/ACCESS.2022.318320510(63932-63957)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3183205
Ahmad SGupta AGupta N(2022)A Heuristic Approach to Extract Knowledge from the Text Considering Explicit and Implicit Features BothProceedings of Data Analytics and Management10.1007/978-981-16-6289-8_26(309-317)Online publication date: 4-Jan-2022
https://doi.org/10.1007/978-981-16-6289-8_26
TOKCAER S(2021)Türkçe Metinlerde Duygu AnaliziJournal of Yaşar University10.19168/jyasar.92884316:63(1516-1536)Online publication date: 30-Sep-2021
https://doi.org/10.19168/jyasar.928843
Show More Cited By

Index Terms

Analyzing Implicit Aspects and Aspect Dependent Sentiment Polarity for Aspect-based Sentiment Analysis on Informal Turkish Texts
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis

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MEDES '17: Proceedings of the 9th International Conference on Management of Digital EcoSystems

November 2017

299 pages

ISBN:9781450348959

DOI:10.1145/3167020

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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New York, NY, United States

Publication History

Published: 07 November 2017

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MEDES '17

MEDES '17: The 9th International Conference on Management of Digital EcoSystems

November 7 - 10, 2017

Bangkok, Thailand

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MEDES '17 Paper Acceptance Rate 41 of 65 submissions, 63%;

Overall Acceptance Rate 267 of 682 submissions, 39%

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Cited By

Soni PRambola R(2022)A Survey on Implicit Aspect Detection for Sentiment Analysis: Terminology, Issues, and ScopeIEEE Access10.1109/ACCESS.2022.318320510(63932-63957)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3183205
Ahmad SGupta AGupta N(2022)A Heuristic Approach to Extract Knowledge from the Text Considering Explicit and Implicit Features BothProceedings of Data Analytics and Management10.1007/978-981-16-6289-8_26(309-317)Online publication date: 4-Jan-2022
https://doi.org/10.1007/978-981-16-6289-8_26
TOKCAER S(2021)Türkçe Metinlerde Duygu AnaliziJournal of Yaşar University10.19168/jyasar.92884316:63(1516-1536)Online publication date: 30-Sep-2021
https://doi.org/10.19168/jyasar.928843
Maitama JIdris NAbdi AShuib LFauzi R(2020)A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment AnalysisIEEE Access10.1109/ACCESS.2020.30312178(194166-194191)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3031217
Zuo EZhao HChen BChen Q(2020)Context-Specific Heterogeneous Graph Convolutional Network for Implicit Sentiment AnalysisIEEE Access10.1109/ACCESS.2020.29752448(37967-37975)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2975244
Xu XZhang S(2020)Micro-blog Sentiment Analysis Based on Emoticon Preferences and Emotion Commonsense2020 International Conference on Applications and Techniques in Cyber Intelligence10.1007/978-3-030-53980-1_123(838-844)Online publication date: 13-Aug-2020
https://doi.org/10.1007/978-3-030-53980-1_123
Karagoz PKama BOzturk MToroslu ICanturk D(2019)A framework for aspect based sentiment analysis on turkish informal textsJournal of Intelligent Information Systems10.1007/s10844-019-00565-wOnline publication date: 20-Jun-2019
https://doi.org/10.1007/s10844-019-00565-w
Demir SAlkan OCekinel FKaragoz P(2019)Extracting Potentially High Profit Product Feature Groups by Using High Utility Pattern Mining and Aspect Based Sentiment AnalysisHigh-Utility Pattern Mining10.1007/978-3-030-04921-8_9(233-260)Online publication date: 19-Jan-2019
https://doi.org/10.1007/978-3-030-04921-8_9

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