Constructing Sentiment Lexicon for Subject-Specific Sentiment Analysis

In this work, we focus on the application of text mining and sentiment analysis techniques for analyzing Tunisian users' statuses updates on Facebook. We aim to extract useful information, about their sentiment and behavior, especially during the “Arabic spring” era. To achieve this task, we describe a method for sentiment analysis using Support Vector Machine and Naïve Bayes algorithms, and applying a combination of more than two features. The output of this work consists, on one hand, on the construction of a sentiment lexicon based on the Emoticons and Acronyms' lexicons that we developed based on the extracted statuses updates; and on the other hand, it consists on the realization of detailed comparative experiments between the above algorithms by creating a training model for sentiment classification.

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Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning

IEEE Access ◽

10.1109/access.2020.2969854 ◽

2020 ◽

Vol 8 ◽

pp. 23522-23530 ◽

Cited By ~ 9

Author(s):

Li Yang ◽

Ying Li ◽

Jin Wang ◽

R. Simon Sherratt

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Product Reviews ◽

Sentiment Lexicon

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Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

Expert Systems ◽

10.1111/exsy.12397 ◽

2019 ◽

Vol 36 (3) ◽

pp. e12397 ◽

Cited By ~ 6

Author(s):

Muhammad Zubair Asghar ◽

Anum Sattar ◽

Aurangzeb Khan ◽

Amjad Ali ◽

Fazal Masud Kundi ◽

...

Keyword(s):

Sentiment Analysis ◽

Sentiment Lexicon ◽

Resource Poor

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Improving Sentiment Analysis in an Online Cancer Survivor Community Using Dynamic Sentiment Lexicon

2013 International Conference on Social Intelligence and Technology ◽

10.1109/society.2013.20 ◽

2013 ◽

Cited By ~ 7

Author(s):

N. Ofek ◽

C. Caragea ◽

L. Rokach ◽

P. Biyani ◽

P. Mitra ◽

...

Keyword(s):

Sentiment Analysis ◽

Cancer Survivor ◽

Sentiment Lexicon

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Automatically detecting open academic review praise and criticism

Online Information Review ◽

10.1108/oir-11-2019-0347 ◽

2020 ◽

Vol 44 (5) ◽

pp. 1057-1076

Author(s):

Mike Thelwall ◽

Eleanor-Rose Papas ◽

Zena Nyakoojo ◽

Liz Allen ◽

Verena Weigert

Keyword(s):

Sentiment Analysis ◽

Peer Review ◽

Academic Publishing ◽

Systematic Bias ◽

Content Type ◽

Test Corpus ◽

Sentiment Lexicon ◽

Negative Comments ◽

Sentiment Detection ◽

Academic Review

PurposePeer reviewer evaluations of academic papers are known to be variable in content and overall judgements but are important academic publishing safeguards. This article introduces a sentiment analysis program, PeerJudge, to detect praise and criticism in peer evaluations. It is designed to support editorial management decisions and reviewers in the scholarly publishing process and for grant funding decision workflows. The initial version of PeerJudge is tailored for reviews from F1000Research's open peer review publishing platform.Design/methodology/approachPeerJudge uses a lexical sentiment analysis approach with a human-coded initial sentiment lexicon and machine learning adjustments and additions. It was built with an F1000Research development corpus and evaluated on a different F1000Research test corpus using reviewer ratings.FindingsPeerJudge can predict F1000Research judgements from negative evaluations in reviewers' comments more accurately than baseline approaches, although not from positive reviewer comments, which seem to be largely unrelated to reviewer decisions. Within the F1000Research mode of post-publication peer review, the absence of any detected negative comments is a reliable indicator that an article will be ‘approved’, but the presence of moderately negative comments could lead to either an approved or approved with reservations decision.Originality/valuePeerJudge is the first transparent AI approach to peer review sentiment detection. It may be used to identify anomalous reviews with text potentially not matching judgements for individual checks or systematic bias assessments.

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