Aspect based citation sentiment analysis using linguistic patterns for better comprehension of scientific knowledge

Ikram, Muhammad Touseef; Afzal, Muhammad

doi:10.1007/s11192-019-03028-9

Cited by 27 publications

(12 citation statements)

References 24 publications

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“…In recent years, access to full-text scholarly publications allows the scientific community to extract various features of a citation, particularly those relating to its function and purpose (Abu-Jbara et al, 2013;Siddharthan & Teufel, 2007), location (Boyack et al, 2018), polarity (Hatzivassiloglou & McKeown, 1997), and linguistic pattern (Ikram & Afzal, 2019). Some studies have used publications in XML format to develop classifiers to identify a citation's function, purpose, and polarity (Jha et al, 2017), thus, demonstrating that the analysis of large-scale datasets and feature extraction (citation context, citation location, sentiment, etc.)…”

Section: Background To Classic Citation Indexingmentioning

confidence: 99%

“…Thus, it seems that SEMANTRIA has some weaknesses. Ikram and Afzal (2019) used the dataset from the field of computer science presented by Athar (2011) and another from the bioinformatics domain. In the dataset from the bioinformatics domain, 285 papers were randomly selected containing 3172 neutral, 702 positive, and 308 negative citations.…”

Section: Role Of Linguistics Features For Sentiment Classificationmentioning

confidence: 99%

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A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

et al. 2021

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Section: Background To Classic Citation Indexingmentioning

confidence: 99%

Section: Role Of Linguistics Features For Sentiment Classificationmentioning

confidence: 99%

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

et al. 2021

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“…Muhammad Touseef IkramIkram1 et al, proposed a model to extract hidden patterns in the sentences and provides a wide variety of papers with thousands of citations [18]. By using the patterns of opinionated phrases in the citation sentences, it extracts the aspects and uses linguistic rule-based approach.…”

Section: Related Workmentioning

confidence: 99%

ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

Syamala¹,

Nalini²

2021

TEM Journal

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Aspect based sentient analysis (ABSA) is identified as one of the current research problems in Natural Language Processing (NLP). Traditional ABSA requires manual aspect assignment for aspect extraction and sentiment analysis. In this paper, to automate the process, a domain-independent dynamic ABSA model by the fusion of Efficient Named Entity Recognition (E-NER) guided dependency parsing technique with Neural Networks (NN) is proposed. The extracted aspects and sentiment terms by E-NER are trained to a Convolutional Neural Network (CNN) using Word embedding’s technique. Aspect categorybased polarity prediction is evaluated using NLTK Vader Sentiment package. The proposed model was compared to traditional rule-based approach, and the proposed dynamic model proved to yield better results by 17% when validated in terms of correctly classified instances, accuracy, precision, recall and F-Score using machine learning algorithms.

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“…Ikram et al [26] has proposed an aspect level sentiment analysis technique for selecting a research paper which has more positive aspect level sentiments. This research has followed the usual phases of aspect based sentiment analysis that are (1) aspect identification and (2) opinion determination.…”

Section: Aspect Identification Techniques For Different Application Dmentioning

confidence: 99%

Movie Aspects Identification Model for Aspect Based Sentiment Analysis

Mir

Mahmood

2020

ITC

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Aspect Based Sentiment Analysis techniques have been applied in several application domains. From the last two decades, these techniques have been developed mostly for product and service application domains. However, very few aspect-based sentiment techniques have been proposed for the movie application domain. Moreover, these techniques only mine specific aspects (Script, Director, and Actor) of a movie application domain, nevertheless, the movie application domain is more complex than the product and service application domain. Since, it contains NER (Named Entity Recognition) problem and it cannot be ignored, since there is an opinion often associated with it. Consequently, in this paper MAIM (Movie Aspect Identification Model) is proposed that can extract not only movie specific aspects, also identifies NEs (Named Entities) such as Person Name and Movie Title. The three main contributions are 1) the identification of infrequent aspects, 2) the identification of NE (named entity) in movie application domain, 3) identifying N-gram opinion words as an entity. MAIM incorporates the BiLSTM-CRF hybrid technique and is implemented on the movie application domain having precision 89.9%, recall 88.9% and f1-measure 89.4%. The experimental results show that MAIM performs better than baseline models CRF and LSTM-CRF.

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Aspect based citation sentiment analysis using linguistic patterns for better comprehension of scientific knowledge

Cited by 27 publications

References 24 publications

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

Movie Aspects Identification Model for Aspect Based Sentiment Analysis

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