Twitter news classification using SVM

Dilrukshi, Inoshika; Zoysa, Kasun De; Caldera, Amitha

doi:10.1109/iccse.2013.6553926

Cited by 78 publications

(42 citation statements)

References 4 publications

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“…Hence, many news groups share their news headlines using microblogging services. The authors in [17] proposed a method to classify Twitter news into different groups, so that a user could identify the most popular news group in a given country at a given time. The data were trained and classified using an SVM method that was suitable for processing short text messages, such as tweets.…”

Section: Education Using Twittermentioning

confidence: 99%

“…In addition, when using 7000 features, the average F-measure value reached the highest value of 87.8 %. The authors in [17] also categorized Twitter news using SVM and achieved an average F-measure value of 77.85 %. Further, the researchers in [28] classified Twitter news using machine-learning methods including Naïve Bayes, Naïve Bayes Multinomial, and SVM.…”

Section: News Classificationmentioning

confidence: 99%

“…Next, we counted the frequencies of the selected words and created a document vector in the dataset. These steps are necessary to obtain a set of good-quality features [17].…”

Section: Feature Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Twitter news-in-education platform for social, collaborative, and flipped learning

Kim¹,

Hwang

Rho

2016

J Supercomput

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NIE (newspaper in education), which is the study utilizing newspaper-based educational resources in the classroom, has been pursued for a long time. Traditional NIE allows only one-way interaction and available media are limited. In addition, it has difficulty in capturing the latest "hot" issues or recognizing public opinions. In this paper, we propose a platform called TNIE (Twitter news in education) that utilizes Twitter in NIE. TNIE has many advantages compared to traditional NIE: (1) it classifies the latest news into various topics, (2) the classified news content can be browsed effectively via a hierarchical visualization scheme, and (3) effective discussion or debate groups can be easily formed based on the learners' news selection. To evaluate the performance of our scheme, we implemented a prototype system and performed several experiments. According to the results, our news classification scheme achieved an 87.8 % F-measure on average, which is superior to the 78.94 % of previous studies. We also had volunteers use our prototype system and evaluate its main functions, e.g., news visualization, classification, learner clustering, and collaborative learning. We report some of these evaluation results. This paper is an extended version of our conference paper [30].

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Section: Education Using Twittermentioning

confidence: 99%

Section: News Classificationmentioning

confidence: 99%

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Twitter news-in-education platform for social, collaborative, and flipped learning

Kim¹,

Hwang

Rho

2016

J Supercomput

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“…Short text classification is technically challanging due to the sparsity of features. Most work in this area has focused on classification of microblog messages (Sriram et al, 2010;Dilrukshi et al, 2013;Go et al, 2009;Chen et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

Predicting News Values from Headline Text and Emotions

Buono¹,

Šnajder²,

Bašić³

et al. 2017

Proceedings of the 2017 EMNLP Workshop: Natural Language Processing Meets Journalism

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We present a preliminary study on predicting news values from headline text and emotions. We perform a multivariate analysis on a dataset manually annotated with news values and emotions, discovering interesting correlations among them. We then train two competitive machine learning models -an SVM and a CNN -to predict news values from headline text and emotions as features. We find that, while both models yield a satisfactory performance, some news values are more difficult to detect than others, while some profit more from including emotion information.

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“…The news providers are,  Ada Derana  Ceylon Today  ITN  Lanka Breaking News  News First The news was classified into 12 categories manually. The 12 categories were defined according to popular newspaper articles and news websites [9]. The 12 groups are,…”

Section: International Journal Of Machine Learning and Computing Volmentioning

confidence: 99%

A Feature Selection Method for Twitter News Classification

Dilrukshi¹,

Zoysa²

2014

IJMLC

Self Cite

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Abstract-This paper presence a new feature selection method which can be used for creating data set in order to classify Twitter short messages. The Twitter short messages contain only 140 characters. Thus, the number of words per sentence is almost equal for all sentences. Once you pool the all text messages together, there can be number of words in the pool but, for a given sentence, there will be only few words included from the pool. This causes to have a sparse matrix as the feature vector. By removing the unrelated words from the feature space, the dimension can be reduced and therefore, the sparseness can be reduced. The unrelated words can be define as the common words (high frequent words) and noise words (low frequent words). Even though by removing these unrelated words, still it may contain some unrelated words. Thus, a feature selection technique was needed to apply in order to select the best feature set. The suggested new feature selection method was based on the Information Theory. It was named as Ratio Method. The calculated value increase when the word occurs frequently in a particular group and it decrease when the word occur in all groups. The best features can be choose by using a proper threshold. Some popular text classifiers such as SVM, Naïve Bayes and Decision Trees are used to evaluate the performance of the new feature selection method and to compare the new method with existing methods.

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Twitter news classification using SVM

Cited by 78 publications

References 4 publications

Twitter news-in-education platform for social, collaborative, and flipped learning

Twitter news-in-education platform for social, collaborative, and flipped learning

Predicting News Values from Headline Text and Emotions

A Feature Selection Method for Twitter News Classification

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