Improve Sentiment Analysis of Citations with Author Modelling

Ma, Zheng; Nam, Jinseok; Weihe, Karsten

doi:10.18653/v1/w16-0420

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…For example, before the advancement of computational citation semantic analysis, Small (2011) concluded that there is a correlation between prominent sentiments and competing knowledge claims. On the other hand, a more recent study (Ma, Nam, & Weihe, 2016) suggested that an author's reputation is a reliable predictor of the sentiment toward one's works. Although these findings contributed to a deeper understanding of citations, we are still far from fully understanding the relationship between citation sentiment and the authors who receive it.…”

Section: Literature Reviewmentioning

confidence: 99%

Authors' status and the perceived quality of their work: Measuring citation sentiment change in nobel articles

Yan

Chen

2019

Asso for Info Science & Tech

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Prior research in status ordering has used numeric indicators to examine the impact of a status change on the perception of a scientist's work. This study measures the perception change directly as reflected in citation sentiment, with the attainment of a Nobel Prize in Chemistry or a Nobel Prize in Physiology or Medicine considered the status change. The article identifies 12,393 citances to 25 Nobel articles in PubMed Central and includes a control article set of 75 articles with 30,851 citances. The results show a moderate increase in citation sentiment toward Nobel articles postaward. Dynamically, for Nobel articles there is a steady sentiment increase, and a Nobel Prize seems to co-occur with this trend. This trend, however, is not evident in the control article set.

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Section: Literature Reviewmentioning

confidence: 99%

Authors' status and the perceived quality of their work: Measuring citation sentiment change in nobel articles

Yan

Chen

2019

Asso for Info Science & Tech

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“…Due to the exponential growth of research publications on the Internet, the frequency of citations to scientific texts has become very high [1,2]. Citation text is valuable and of utmost importance for the qualitative assessment of a paper, but the sheer size of it makes access prohibitive [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Automated citation sentiment analysis using high order n-grams: a preliminary investigation

Ikram¹,

Afzal²,

Butt³

2018

Turk J Elec Eng & Comp Sci

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Abstract:Scientific papers hold an association with previous research contributions (i.e. books, journals or conference papers, and web resources) in the form of citations. Citations are deemed as a link or relatedness of the previous work to the cited work. The nature of the cited material could be supportive (positive), contrastive (negative), or objective (neutral). Extraction of the author's sentiment towards the cited scientific articles is an emerging research discipline due to various linguistic differences between the citation sentences and other domains of sentiment analysis. In this paper, we propose a technique for the identification of the sentiment of the citing author towards the cited paper by extracting unigram, bigram, trigram, and pentagram adjective and adverb patterns from the citation text. After POS tagging of the citation text, we use the sentence parser for the extraction of linguistic features comprising adjectives, adverbs, and n-grams from the citation text. A sentiment score is then assigned to distinguish them as positive, negative, and neutral.In addition, the proposed technique is compared with manually classified citation text and 2 commercial tools, namely SEMANTRIA and THEYSAY, to determine their applicability to the citation corpus. These tools are based on different techniques for determining the sentiment orientation of the sentence. Analysis of the results shows that our proposed approach has achieved results comparable to the commercial counterparts with average precision, recall, and accuracy of 90%, 81.82%, and 85.91% respectively.

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“…In this context, sentiment analysis helps to identify relevant scientific publication. Though there are already few approaches existing in the field of sentiment analysis (Athar, 2011;Xu et al, 2015;Ma et al, 2016;Yu, 2013), none of them have defined an approach that focuses on the combination of sentiment in citations along with the nature of references. This is quite important as it can help researchers in determining the quality of a document for ranking in citation indexes by an inclusion of sentiment in a weighted approach based on nature of references as well.…”

Section: Introductionmentioning

confidence: 99%

SentiCite - An Approach for Publication Sentiment Analysis

Mercier

Bhardwaj

Dengel

et al. 2018

Proceedings of the 10th International Conference on Agents and Artificial Intelligence

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With the rapid growth in the number of scientific publications, year after year, it is becoming increasingly difficult to identify quality authoritative work on a single topic. Though there is an availability of scientometric measures which promise to offer a solution to this problem, these measures are mostly quantitative and rely, for instance, only on the number of times an article is cited. With this approach, it becomes irrelevant if an article is cited 10 times in a positive, negative or neutral way. In this context, it is quite important to study the qualitative aspect of a citation to understand its significance. This paper presents a novel system for sentiment analysis of citations in scientific documents (SentiCite) and is also capable of detecting nature of citations by targeting the motivation behind a citation, e.g., reference to a dataset, reading reference. Furthermore, the paper also presents two datasets (SentiCiteDB and IntentCiteDB) containing about 2,600 citations with their ground truth for sentiment and nature of citation. SentiCite along with other state-of-the-art methods for sentiment analysis are evaluated on the presented datasets. Evaluation results reveal that SentiCite outperforms state-of-the-art methods for sentiment analysis in scientific publications by achieving a F1-measure of 0.71.

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Improve Sentiment Analysis of Citations with Author Modelling

Cited by 8 publications

References 6 publications

Authors' status and the perceived quality of their work: Measuring citation sentiment change in nobel articles

Authors' status and the perceived quality of their work: Measuring citation sentiment change in nobel articles

Automated citation sentiment analysis using high order n-grams: a preliminary investigation

SentiCite - An Approach for Publication Sentiment Analysis

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