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

Content Provider	ACM Digital Library
Author	Hobeica, Roula Baly, Ramy Hajj, Hazem El-hajj, Wassim Shaban, Khaled Bashir Al-sallab, Ahmad
Copyright Year	2016
Description	Author Affiliation: American University of Beirut, Beirut, Lebanon(Cairo university, Cairo, Egypt (Al-Sallab, Ahmad; Qatar university, Doha, Qatar (Shaban, Khaled Bashir); El-hajj, Wassim); Hajj, Hazem; Hobeica, Roula; Baly, Ramy)
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.
Starting Page	1
Ending Page	21
Page Count	21
File Format	PDF
ISSN	10468188
e-ISSN	15582868
DOI	10.1145/2950050
Volume Number	35
Issue Number	1
Journal	ACM Transactions on Information Systems (TOIS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-08-11
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sentiment analysis Human reading Psychology Supervised learning and notions
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Information Systems Business, Management and Accounting

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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