Recognition of Emotion with SVMs

Teng, Zhi; Ren, Fuji; Kuroiwa, Shingo

doi:10.1007/11816171_87

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…A linguistic resource WordNet-Affect was developed in [22] for the lexical representation of affective words. Applications of support vector machine (SVM) and conditional random field (CRF) for emotion detection are proposed in [23] and [24], respectively. Dung et al [20] exploited human mental states w.r.t.…”

Section: Related Workmentioning

confidence: 99%

How Intense Are You? Predicting Intensities of Emotions and Sentiments using Stacked Ensemble [Application Notes]

Akhtar

Ekbal

Wang

2020

IEEE Comput. Intell. Mag.

215

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E motions and sentiments are subjective in nature. They differ on a case-to-case basis. However, predicting only the emotion and sentiment does not always convey complete information. The degree or level of emotions and sentiments often plays a crucial role in understanding the exact feeling within a single class (e.g., 'good' versus 'awesome'). In this paper, we propose a stacked ensemble method for predicting the degree of intensity for emotion and sentiment by combining the outputs obtained from several deep learning and classical feature-based models using a multi-layer perceptron network. We develop three deep learning models based on convolutional neural network, long short-term memory and gated recurrent unit and one classical supervised model based on support vector regression. We evaluate our proposed technique for two problems, i.e., emotion analysis in the generic domain and sentiment analysis in the financial domain. The proposed model shows impressive results for both the problems. Comparisons show that our proposed model achieves improved performance over the existing state-of-theart systems.

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Section: Related Workmentioning

confidence: 99%

How Intense Are You? Predicting Intensities of Emotions and Sentiments using Stacked Ensemble [Application Notes]

Akhtar

Ekbal

Wang

2020

IEEE Comput. Intell. Mag.

215

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show abstract

“…In recent years SVMs have been successfully applied to a lot of applications such as particle identification, face identification and text categorization to engine knock detection, bioinformatics and database marketing. * We used libsvm [13], a library for SVMs, because of its support for multi-class classification [14]. Use the Libsvm we must train the data before prediction.…”

Section: Category Classificationmentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: A foundation for knowledge system with application in information retrieval and knowledge acquisition

Teng

Liu

Ren

2008

2008 International Conference on Natural Language Processing and Knowledge Engineering

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In this paper we present a knowledge system, which is a self sufficient system for aggregating, extraction and classifying news from around the world. This paper present some algorithms for monitoring and gathering news from RSS and websites, creating special domain corpora. The system, however, goes beyond the traditional news aggregation sites by mining information and knowledge from the news articles to aid in such tasks as decision making. The system is able to collect, classify and mine knowledge automatically without the need for an editor or intervention by an administrator. As knowledge system, average users, news professionals and researchers alike will find that the system offers something for them. Our work shows that better performance is achieved and the result showed that this method has potential in this field.

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“…Originally the problem was to determine emotions from input texts but now the problem is to classify the input texts into different emotions. Unlike keyword-based detection methods, learning-based methods try to detect emotions based on a previously trained classifier, which apply various theories of machine learning such as support vector machines [8] and conditional random fields [9], to determine which emotion category should the input text belongs.…”

Section: Learning-based Methodsmentioning

confidence: 99%