research-article

A Meta-Framework for Modeling the Human Reading Process in Sentiment Analysis

Authors:

Wassim El-Hajj,

Khaled Bashir Shaban,

Ahmad Al-SallabAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 35, Issue 1

Article No.: 7, Pages 1 - 21

https://doi.org/10.1145/2950050

Published: 11 August 2016 Publication History

Abstract

This article introduces a sentiment analysis approach that adopts the way humans read, interpret, and extract sentiment from text. Our motivation builds on the assumption that human interpretation should lead to the most accurate assessment of sentiment in text. We call this automated process Human Reading for Sentiment (HRS). Previous research in sentiment analysis has produced many frameworks that can fit one or more of the HRS aspects; however, none of these methods has addressed them all in one approach. HRS provides a meta-framework for developing new sentiment analysis methods or improving existing ones. The proposed framework provides a theoretical lens for zooming in and evaluating aspects of any sentiment analysis method to identify gaps for improvements towards matching the human reading process. Key steps in HRS include the automation of humans low-level and high-level cognitive text processing. This methodology paves the way towards the integration of psychology with computational linguistics and machine learning to employ models of pragmatics and discourse analysis for sentiment analysis. HRS is tested with two state-of-the-art methods; one is based on feature engineering, and the other is based on deep learning. HRS highlighted the gaps in both methods and showed improvements for both.

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Cited By

Radman ADuwairi R(2024)Towards a robust deep learning framework for Arabic sentiment analysisNatural Language Processing10.1017/nlp.2024.35(1-35)Online publication date: 6-Sep-2024
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Munshi AAlSabban WFarag ARakha OAl Sallab AAlotaibi M(2022)Automated Islamic Jurisprudential Legal Opinions Generation Using Artificial IntelligencePertanika Journal of Science and Technology10.47836/pjst.30.2.1630:2(1135-1156)Online publication date: 14-Mar-2022
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Singh LSingh S(2021)Empirical study of sentiment analysis tools and techniques on societal topicsJournal of Intelligent Information Systems10.1007/s10844-020-00616-756:2(379-407)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s10844-020-00616-7
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Index Terms

A Meta-Framework for Modeling the Human Reading Process in Sentiment Analysis
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 35, Issue 1

January 2017

233 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2986034

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Copyright © 2016 ACM.

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Publication History

Published: 11 August 2016

Accepted: 01 May 2016

Revised: 01 April 2016

Received: 01 August 2015

Published in TOIS Volume 35, Issue 1

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Cited By

Radman ADuwairi R(2024)Towards a robust deep learning framework for Arabic sentiment analysisNatural Language Processing10.1017/nlp.2024.35(1-35)Online publication date: 6-Sep-2024
https://doi.org/10.1017/nlp.2024.35
Munshi AAlSabban WFarag ARakha OAl Sallab AAlotaibi M(2022)Automated Islamic Jurisprudential Legal Opinions Generation Using Artificial IntelligencePertanika Journal of Science and Technology10.47836/pjst.30.2.1630:2(1135-1156)Online publication date: 14-Mar-2022
https://doi.org/10.47836/pjst.30.2.16
Singh LSingh S(2021)Empirical study of sentiment analysis tools and techniques on societal topicsJournal of Intelligent Information Systems10.1007/s10844-020-00616-756:2(379-407)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s10844-020-00616-7
Bi Y(2021)Sentiment classification in social media data by combining triplet belief functionsJournal of the Association for Information Science and Technology10.1002/asi.2460573:7(968-991)Online publication date: 18-Nov-2021
https://doi.org/10.1002/asi.24605
Mcdonald GMacdonald COunis I(2020)How the Accuracy and Confidence of Sensitivity Classification Affects Digital Sensitivity ReviewACM Transactions on Information Systems10.1145/341733439:1(1-34)Online publication date: 12-Oct-2020
https://dl.acm.org/doi/10.1145/3417334
Oliveira WDorini LMinetto RSilva T(2020)OutdoorSentACM Transactions on Information Systems10.1145/338518638:3(1-28)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3385186
Malik AAoudi SAlteneiji SKhdour TSaleh MHamdan I(2020)Mining Opinion and Sentiment from Arabic Text2020 Seventh International Conference on Information Technology Trends (ITT)10.1109/ITT51279.2020.9320774(165-168)Online publication date: 25-Nov-2020
https://doi.org/10.1109/ITT51279.2020.9320774
Badaro GBaly RHajj HEl-Hajj WShaban KHabash NAl-Sallab AHamdi A(2019)A Survey of Opinion Mining in ArabicACM Transactions on Asian and Low-Resource Language Information Processing10.1145/329566218:3(1-52)Online publication date: 7-May-2019
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Baly RHajj HHabash NShaban KEl-Hajj W(2017)A Sentiment Treebank and Morphologically Enriched Recursive Deep Models for Effective Sentiment Analysis in ArabicACM Transactions on Asian and Low-Resource Language Information Processing10.1145/308657616:4(1-21)Online publication date: 13-Jul-2017
https://dl.acm.org/doi/10.1145/3086576

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