Sample cutting method for imbalanced text sentiment classification based on BRC

Wang, Suge; Li, Deyu; Zhao, Lidong; Zhang, Jiahao

doi:10.1016/j.knosys.2012.09.003

Cited by 35 publications

(20 citation statements)

References 38 publications

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“…Such imbalance poses a challenge for categorization, especially when the classes have a high degree of overlap [31]. One possible solution for this problem is balancing of the training-set or re-sampling, [5,10,39]. In a previous paper, we demonstrate that classifiers trained on balanced data perform better, on average, than classifiers trained using the original distribution of labels in the corpus [8].…”

Section: Puls Overviewmentioning

confidence: 99%

Improving Supervised Classification Using Information Extraction

Pierce

Pivovarova

et al. 2015

Natural Language Processing and Information Systems

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Abstract. We explore supervised learning for multi-class, multi-label text classification, focusing on real-world settings, where the distribution of labels changes dynamically over time. We use the PULS Information Extraction system to collect information about the distribution of class labels over named entities found in text. We then combine a knowledge-based rote classifier with statistical classifiers to obtain better performance than either classification method alone. The resulting classifier yields a significant improvement in macro-averaged F-measure compared to the state of the art, while maintaining comparable micro-average.

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Section: Puls Overviewmentioning

confidence: 99%

Improving Supervised Classification Using Information Extraction

Pierce

Pivovarova

et al. 2015

Natural Language Processing and Information Systems

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“…In this study, the evaluation metrics [46] for two-class sentiment classification are shown in Table 3. …”

Section: Experimental Designmentioning

confidence: 99%

Determining Fuzzy Membership for Sentiment Classification: A Three-Layer Sentiment Propagation Model

Zhao

Wang

2016

PLoS ONE

Self Cite

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Enormous quantities of review documents exist in forums, blogs, twitter accounts, and shopping web sites. Analysis of the sentiment information hidden in these review documents is very useful for consumers and manufacturers. The sentiment orientation and sentiment intensity of a review can be described in more detail by using a sentiment score than by using bipolar sentiment polarity. Existing methods for calculating review sentiment scores frequently use a sentiment lexicon or the locations of features in a sentence, a paragraph, and a document. In order to achieve more accurate sentiment scores of review documents, a three-layer sentiment propagation model (TLSPM) is proposed that uses three kinds of interrelations, those among documents, topics, and words. First, we use nine relationship pairwise matrices between documents, topics, and words. In TLSPM, we suppose that sentiment neighbors tend to have the same sentiment polarity and similar sentiment intensity in the sentiment propagation network. Then, we implement the sentiment propagation processes among the documents, topics, and words in turn. Finally, we can obtain the steady sentiment scores of documents by a continuous iteration process. Intuition might suggest that documents with strong sentiment intensity make larger contributions to classification than those with weak sentiment intensity. Therefore, we use the fuzzy membership of documents obtained by TLSPM as the weight of the text to train a fuzzy support vector machine model (FSVM). As compared with a support vector machine (SVM) and four other fuzzy membership determination methods, the results show that FSVM trained with TLSPM can enhance the effectiveness of sentiment classification. In addition, FSVM trained with TLSPM can reduce the mean square error (MSE) on seven sentiment rating prediction data sets.

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“…To validate the proposed weighting scheme for word clusters, we also compared our weighting scheme to the Presence scheme. Wang et al [26] suggested that the Presence scheme has the best classification results in sentiment classification. Therefore, we decided not to compare our weighting scheme with Absolute Frequency, Relative Frequency, and TFIDF.…”

Section: ) Comparison Of Different Features and Weighting Schemesmentioning

confidence: 99%

Use of Word Clustering to Improve Emotion Recognition from Short Text

Yuan¹,

Huang²,

Wu³

2016

Journal of Computing Science and Engineering

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Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance. Category: Smart and intelligent computing

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Sample cutting method for imbalanced text sentiment classification based on BRC

Cited by 35 publications

References 38 publications

Improving Supervised Classification Using Information Extraction

Improving Supervised Classification Using Information Extraction

Determining Fuzzy Membership for Sentiment Classification: A Three-Layer Sentiment Propagation Model

Use of Word Clustering to Improve Emotion Recognition from Short Text

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