Hierarchical classification in text mining for sentiment analysis of online news

Li, Jinyan; Fong, Simon; Zhuang, Yan; Khoury, Richard

doi:10.1007/s00500-015-1812-4

Cited by 48 publications

(18 citation statements)

References 15 publications

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“…Support vector machine (SVM) (Cortes and Vapnik 1995) is a supervised classifier which has been proved highly effective in solving a wide range of pattern recognition and computer vision problems (Arana-Daniel and Bayro-Corrochano 2006;Cyganek 2008;Arana-Daniel et al 2009;Bayro-Corrochano and Arana-Daniel 2010;Cyganek et al 2015;Li et al 2016;Rodan et al 2016). Nowadays, in the era of big data, the machine learning community faces new challenges concerned with applying SVMs in real-life scenarios, which result from data variety, volume, velocity, and veracity.…”

Section: Introductionmentioning

confidence: 99%

Selecting training sets for support vector machines: a review

2018

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Support vector machines (SVMs) are a supervised classifier successfully applied in a plethora of real-life applications. However, they suffer from the important shortcomings of their high time and memory training complexities, which depend on the training set size. This issue is especially challenging nowadays, since the amount of data generated every second becomes tremendously large in many domains. This review provides an extensive survey on existing methods for selecting SVM training data from large datasets. We divide the state-of-the-art techniques into several categories. They help understand the underlying ideas behind these algorithms, which may be useful in designing new methods to deal with this important problem. The review is complemented with the discussion on the future research pathways which can make SVMs easier to exploit in practice.

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Section: Introductionmentioning

confidence: 99%

Selecting training sets for support vector machines: a review

2018

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“…Jinyan Li et al [15] evaluated various classification techniques to develop an approach, called hierarchical classification, using three filtering methods that could reduce the original data set according to the contextual polarity and repeated terms of a document. The performance of this approach in various combinations of classification and filtering techniques was compared with that of three sets of online news articles that performed binary and multi-class classifications.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Traditional text classification approaches utilize several classical techniques, like Chi-square test, Document Frequency (DF), and so on, for the feature extraction [14]. However, these approaches are not directly applicable to sentiment classification [15]. In sentiment analysis, one of the fundamental and widely studied research areas is sentence-level sentiment classification.…”

Section: Introductionmentioning

confidence: 99%

Kernel Optimized-Support Vector Machine and Mapreduce framework for sentiment classification of train reviews

Thakur

Deshpande²

2018

Sādhanā

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Sentiment analysis is one of the popular techniques gaining attention in recent times. Nowadays, people gain information on reviews of users regarding public transportation, movies, hotel reservation, etc., by utilizing the resources available, as they meet their needs. Hence, sentiment classification is an essential process employed to determine the positive and negative responses. This paper presents an approach for sentiment classification of train reviews using MapReduce model with the proposed Kernel Optimized-Support Vector Machine (KO-SVM) classifier. The MapReduce framework handles big data using a mapper, which performs feature extraction and reducer that classifies the review based on KO-SVM classification. The feature extraction process utilizes features that are classification-specific and SentiWordNet-based. KO-SVM adopts SVM for the classification, where the exponential kernel is replaced by an optimized kernel, finding the weights using a novel optimizer, self-adaptive lion algorithm. In a comparative analysis, the performance of KO-SVM classifier is compared with SentiWordNet, Naive Bayes, neural network, and LSVM, using the evaluation metrics, specificity, sensitivity, and accuracy, with train review and movie review database. The proposed KO-SVM classifier could attain maximum sensitivity of 93.46% and 91.249%, specificity of 74.485% and 70.018%; and accuracy of 84.341% and 79.611%, respectively, for train review and movie review databases.

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“…(Li et al, 2015). A new trend in emotion research consists in performing lexical analysis of texts with the aim of identifying the words that can predict the affective states of the authors (Calvo & D'Mello 2010).…”

Section: Affective Dictionariesmentioning

confidence: 99%

A model for providing emotion awareness and feedback using fuzzy logic in online learning

et al. 2016

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Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Soft computing.La publicació final està disponible a Springer a través de http://dx.doi.org/10.1007/s00500-016-2399-0 This is a copy of the author 's final draft version of an article published in the journal Soft computing.The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-016-2399-0 Article publicat / Published article:Arguedas, M. [et al.] (2016) A model for providing emotion awareness and feedback using fuzzy logic in online learning. " Soft computing". Doi: 10.1007/s00500-016-2399-0 Abstract: Monitoring users' emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students' attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students' feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students' emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular, and many assigned by an affective dictionary to every word. The advantage of the statistical approach is to reduce the classical pollution problem of training and analyzing the scenario using the same dataset. Our approach has been tested in a real online learning environment and proved to have a very positive influence on students' learning performance. Section/Category: Methodologies & Application Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation A MODEL FOR PROVIDING EMOTION AWARENESS AND FEEDBACK USING FUZZY LOGIC IN ONLINE LEARNINGAbstract Monitoring users' emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students' attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students' feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students' emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular, and many assigned by an affective dictionary to every word. The advan...

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Hierarchical classification in text mining for sentiment analysis of online news

Cited by 48 publications

References 15 publications

Selecting training sets for support vector machines: a review

Selecting training sets for support vector machines: a review

Kernel Optimized-Support Vector Machine and Mapreduce framework for sentiment classification of train reviews

A model for providing emotion awareness and feedback using fuzzy logic in online learning

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