Emotion Recognition from Text based on the Rough Set Theory and the Support Vector Machines

Teng, Zhi; Ren, Fuji; Kuroiwa, Shingo

doi:10.1109/nlpke.2007.4368008

Cited by 19 publications

(11 citation statements)

References 6 publications

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“…On the other hand, there are also many examples of multi-class classification for the purpose of recognition, such as emotion identification (Teng et al 2007). Due to the popularity of probabilistic approaches in traditional machine learning, SVM and NB have been used for discriminating one emotion from the other ones (Altrabsheh et al 2015).…”

Section: Review Of Recognition-intensive Classificationmentioning

confidence: 99%

“…Classification is one of the most popular tasks of machine learning, which has been popularly involved in various application areas, such as sentiment analysis Pedrycz and Chen 2016;Jefferson et al 2017), image processing Wang and Yu 2016), pattern recognition (Teng et al 2007;Wu et al 2011) and decision making (Liu and Gegov 2015;Xu and Wang 2016;Liu and You 2017).…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy rule-based systems for recognition-intensive classification in granular computing context

Liu

Zhang

2018

Granul. Comput.

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In traditional machine learning, classification is typically undertaken in the way of discriminative learning using probabilistic approaches, i.e. learning a classifier that discriminates one class from other classes. The above learning strategy is mainly due to the assumption that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in the context of real-life data classification, especially when the nature of a classification task is to recognize patterns of specific classes. For example, in the context of emotion detection, multiple emotions may be identified from the same person at the same time, which indicates in general that different emotions may involve specific relationships rather than mutual exclusion. In this paper, we focus on classification problems that involve pattern recognition. In particular, we position the study in the context of granular computing, and propose the use of fuzzy rule-based systems for recognition-intensive classification of real-life data instances. Furthermore, we report an experimental study conducted using 7 UCI data sets on life sciences, to compare the fuzzy approach with four popular probabilistic approaches in pattern recognition tasks. The experimental results show that the fuzzy approach can not only be used as an alternative one to the probabilistic approaches but also is capable to capture more patterns which probabilistic approaches cannot achieve.

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Section: Review Of Recognition-intensive Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy rule-based systems for recognition-intensive classification in granular computing context

Liu

Zhang

2018

Granul. Comput.

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“…The method involves training the learning system on a set of labeled text data and the conversion of text words into keywords that are assigned indices (taken from the Vector Space Model) and values proportional to their frequency [2]. Few approaches towards opinion mining are feature-based, where certain topics or features are known before the data sets are trained.…”

Section: Related Workmentioning

confidence: 99%

“…The first and last context blocks (paragraph) of the document are known to have the most relevant views [2]. Therefore, the sub-graphs pertaining to the first and last paragraphs are emphasized with respect to those of other paragraphs, by assigning additional weights to their nodes in order to elevate their emotional relevance for further distilling.…”

Section: B Pre-processing Of Textmentioning

confidence: 99%

A Framework for Extensible Emotion Analysis System

Sugandhi¹,

Mulye²,

Wadhai³

2011

IJET

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“…Most of the existing solutions are for medical text written in English. In [3] a scheme in which emotion recognition from text through classification with the rough set theory and the support vector machines (SVMs) is proposed for Chinesse language. The experiment results showed that rough set theory and SVMs method are effective in emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Clinical Narrative Analytics Challenges

et al. 2016

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Abstract. Precision medicine or evidence based medicine is based on the extraction of knowledge from medical records to provide individuals with the appropriate treatment in the appropriate moment according to the patient features. Despite the efforts of using clinical narratives for clinical decision support, many challenges have to be faced still today such as multilinguarity, diversity of terms and formats in different ser vices, acronyms, negation, to name but a few. The same problems exist when one wants to analyze narratives in literature whose analysis would provide physicians and researchers with highlights. In this talk we will analyze challenges, solutions and open problems and will analyze several frameworks and tools that are able to perform NLP over free text to extract medical entities by means of Named Entity Recognition process. We will also analyze a framework we have developed to extract and val idate medical terms. In particular we present two uses cases: (i) medical entities extraction of a set of infectious diseases description texts pro vided by MedlinePlus and (ii) scales of stroke identification in clinical narratives written in Spanish.

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Emotion Recognition from Text based on the Rough Set Theory and the Support Vector Machines

Cited by 19 publications

References 6 publications

Fuzzy rule-based systems for recognition-intensive classification in granular computing context

Fuzzy rule-based systems for recognition-intensive classification in granular computing context

A Framework for Extensible Emotion Analysis System

Clinical Narrative Analytics Challenges

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