Citation Impact Categorization: For Scientific Literature

Hernández-Álvarez, Myriam; Gómez, José Manuel

doi:10.1109/cse.2015.21

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…The major reason of choosing this classifier is its ability to perform effecient for larger feature sets [21] [98]. Structural features and grammatical features were used for the classification [28][30] [31]. They compared their evaluation results with [89].…”

Section: Literature Reviewmentioning

confidence: 99%

Scientific Text Sentiment Analysis using Machine Learning Techniques

Raza¹,

Faizan²,

Hamza³

et al. 2019

IJACSA

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Over time, textual information on the World Wide Web (WWW) has increased exponentially, leading to potential research in the field of machine learning (ML) and natural language processing (NLP). Sentiment analysis of scientific domain articles is a very trendy and interesting topic nowadays. The main purpose of this research is to facilitate researchers to identify quality research papers based on their sentiment analysis. In this research, sentiment analysis of scientific articles using citation sentences is carried out using an existing constructed annotated corpus. This corpus is consisted of 8736 citation sentences. The noise was removed from data using different data normalization rules in order to clean the data corpus. To perform classification on this data set we developed a system in which six different machine learning algorithms including Naïve-Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN) and Random Forest (RF) are implemented. Then the accuracy of the system is evaluated using different evaluation metrics e.g. F-score and Accuracy score. To improve the system' accuracy additional features selection techniques, such as lemmatization, n-graming, tokenization, and stop word removal are applied and found that our system provided significant performance in every case compared to the base system. Our method achieved a maximum of about 9% improved results as compared to the base system.

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Section: Literature Reviewmentioning

confidence: 99%

Scientific Text Sentiment Analysis using Machine Learning Techniques

Raza¹,

Faizan²,

Hamza³

et al. 2019

IJACSA

View full text Add to dashboard Cite

show abstract

“…Machine-learning techniques are a dominant methodological element in citation sentiment studies. In addition to SVM (see also Hernández-Alvarez & Gómez, 2015;Kim & Thoma, 2015;Xu, Martin, & Mahidadia, 2013), notable examples include random forest (Abu-Jbara, Ezra, & Radev, 2013;Parthasarathy & Tomar, 2014), naïve Bayes (Butt et al, 2015;Sula & Miller, 2014), and neural network methods (Lauscher, Glavaš, Ponzetto, & Eckert, 2017). Within this category, SentiWordNet, a lexical resource for opinion mining that is partly based on a semisupervised machine-learning method, has also been used in a number of studies (Goodarzi, Mahmoudi, & Zamani, 2014;Sendhilkumar, Elakkiya, & Mahalakshmi, 2013).…”

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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“…Recall = Correctly classif ied instances Relevant instances = tp tp + f n (6) where relevant instances is the number of learning elements classified as relevant by the classifier. F-measure is the harmonic mean of precision and recall, Fmeasure uses both precision and recall to correctly assess the efficacy of the classification.…”

Section: Resultsmentioning

confidence: 99%

“…Hundred papers were chosen as a corpus size for citation sentences extraction. Our choice of these papers follows a number of previous works [6], [7]. We have used parsing rules to extract citation sentences followed by regular expressions for data cleaning, and non-citation sentences were excluded.…”

Section: A Dataset Selectionmentioning

confidence: 99%

See 1 more Smart Citation

A New Scheme for Citation Classification based on Convolutional Neural Networks

Bakhti¹,

Niu²,

Nyamawe³

2018

International Conferences on Software Engineering and Knowledge Engineering

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Automated classification of citation function in scientific text is a new emerging research topic inspired by traditional citation analysis in applied linguistic and scientometric fields. The aim is to classify citations in scholarly publication in order to identify author's purpose or motivation for quoting or citing a particular paper. Several citation schemes have been proposed to classify the citations into different functions. However, it is extremely challenging to find standard scheme to classify citations, and some of the proposed schemes have similar functions. Moreover, most of previous studies mainly used classical machine learning methods such as support vector machine and neural networks with a number of manually created features. These features are incomplete and suffer from time-consuming and error prone weakness. To address these problems, we present a new citation scheme with less functions and propose a deep learning model for classification. The citation sentences and author's information were fed to convolutional neural networks to build citation and author representations. A corpus was built using the proposed scheme and a number of experiments were carried out to assess the model. Experimental results have shown that the proposed approach outperforms the existing methods in term of accuracy, precision and recall.

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Citation Impact Categorization: For Scientific Literature

Cited by 10 publications

References 17 publications

Scientific Text Sentiment Analysis using Machine Learning Techniques

Scientific Text Sentiment Analysis using Machine Learning Techniques

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

A New Scheme for Citation Classification based on Convolutional Neural Networks

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