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

Author(s):  
N. Ofek ◽  
C. Caragea ◽  
L. Rokach ◽  
P. Biyani ◽  
P. Mitra ◽  
...  
2018 ◽  
Vol 77 (16) ◽  
pp. 21265-21280 ◽  
Author(s):  
Hongyu Han ◽  
Jianpei Zhang ◽  
Jing Yang ◽  
Yiran Shen ◽  
Yongshi Zhang

Author(s):  
Jalel Akaichi

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.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 23522-23530 ◽  
Author(s):  
Li Yang ◽  
Ying Li ◽  
Jin Wang ◽  
R. Simon Sherratt

2019 ◽  
Vol 36 (3) ◽  
pp. e12397 ◽  
Author(s):  
Muhammad Zubair Asghar ◽  
Anum Sattar ◽  
Aurangzeb Khan ◽  
Amjad Ali ◽  
Fazal Masud Kundi ◽  
...  

2020 ◽  
Vol 44 (5) ◽  
pp. 1057-1076
Author(s):  
Mike Thelwall ◽  
Eleanor-Rose Papas ◽  
Zena Nyakoojo ◽  
Liz Allen ◽  
Verena Weigert

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.


2014 ◽  
Vol 23 (04) ◽  
pp. 1460019
Author(s):  
Hsiang Hui Lek ◽  
Danny Chiang Choon Poo

Sentiment lexicon plays an important role in determining the polarity of words and proves to be an important component in sentiment analysis applications. Most of these sentiment lexicons assign a fixed polarity to each word. However, it has been noted that the polarity of words depends on how they are used and so these lexicons are unable to accurately classify the polarity of the sentiments. By considering the aspect and domain of a word will allow us to more accurately classify sentiments. This paper presents a fully automatic method to build an aspect and domain sensitive sentiment lexicon which assigns a polarity to a word depending on both the aspect and the domain. The experimental results show that our lexicon significantly outperforms other commonly used sentiment lexicons / state-of-the-art approaches. To the best of our knowledge, such a lexicon is not publicly available. As such, we also make this lexicon publicly available as we believe it will benefit the research community. In addition, we observe the long tail distribution behavior of product aspects and propose the possibility of aspect ranking by comparing the number of domains and number of sentiment words present for an aspect.


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