A method to classify emotional expressions of text and synthesize speech

Sugimoto, Futoshi; Yazu, K.; Murakami, Makoto; Yoneyama, Mitsutoshi

doi:10.1109/isccsp.2004.1296469

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…While examining the emotional reactions of players during the 1992 Olympics, this theory found support in the fact that silver medalists felt worse (because they felt the loss of not winning) than bronze medalists (who felt relieved for making it to the podium). Sugimoto (2004) addressed sentence-level emotion recognition with a model that uses a composition assumption: the emotion of a sentence is a function of the emotional affinity of the words in the sentence. They obtain emotional judgments of 73 adjectives and a set of sentences from 15 human subjects and compute words' emotional strength based on the ratio of times a word or a sentence was judged to fall into a particular emotion bucket, given the number of human subjects.…”

Section: Related Workmentioning

confidence: 99%

Application of Affective Lexicons in Sports Text Mining: A Case Study of Fifa World Cup 2018

Avdić¹

2021

South Eastern European Journal of Communication

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World Cup is a major football event that is globally popular and has its very best influence on human emotions. As such, it affects how people verbally discuss football topics on the Internet. In addition, it shows great significance when viewers who usually do not watch other football competitions start paying close attention when their nation plays a World Cup football match. In this paper, fans’ online behaviour during World Cup 2018 was analysed using text mining methods. With the use of emotion analysis, it is noticed that there are different emotional states through which people go while sharing their thoughts with other people about football. Reddit, a discussion Internet website, was used as a generator of user data. Five supervised machine learning algorithms were used to test and revise an existing model. It is affirmed that the model successfully predicts the emotions within the text with an average accuracy of 78%.

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Section: Related Workmentioning

confidence: 99%

Application of Affective Lexicons in Sports Text Mining: A Case Study of Fifa World Cup 2018

Avdić¹

2021

South Eastern European Journal of Communication

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“…Klaus Scherer's work on vocal communication of emotion [7], Litman and Forbes-Riley's work on text-to-speech synthesis (TTS) [8]. A short study by Sugimoto [9] to addresses sentence-level emotion recognition for Japanese TTS. Their model uses a composition assumption: the emotion of a sentence is a function of the emotional affinity of the words in the sentence.…”

Section: Previous Workmentioning

confidence: 99%

Adaptive Machine Learning Approach for Emotional Email Classification

Karthik

Ponnusamy

2011

Human-Computer Interaction. Towards Mobile and Intelligent Interaction Environments

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Emotional e-mail classification is one of the important issues in the service oriented organizations. E-mails are served in a first come first serve basis. Few e-mails express the unfair treatment or dissatisfaction of service. It is essential to serve such e-mails with a high priority. In this paper an attempt is made to identify such mails which express the strong emotions of the customers / stakeholders. This system classifies the e-mails in to three categories via positive, negative and other mails. An adaptive machine learning algorithm that uses combined SVD and KNN methods is developed to solve the problem of emotional e-mail classification. Also an emotional dictionary is used as a central component of this system that serves various emotional words and phrases for classification. The system also adaptive in nature and adapts various new words and phrases that explicates the emotion.

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“…(Scherer, 2003), (Litman and Forbes-Riley, 2004), this appears not to be the case for text-to-speech synthesis (TTS). A short study by (Sugimoto et al, 2004) addresses sentence-level emotion recognition for Japanese TTS. Their model uses a composition assumption: the emotion of a sentence is a function of the emotional affinity of the words in the sentence.…”

Section: Previous Workmentioning

confidence: 99%

Emotions from text

Alm¹,

Roth²,

Sproat³

2005

Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05

567

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In addition to information, text contains attitudinal, and more specifically, emotional content. This paper explores the text-based emotion prediction problem empirically, using supervised machine learning with the SNoW learning architecture. The goal is to classify the emotional affinity of sentences in the narrative domain of children's fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis. Initial experiments on a preliminary data set of 22 fairy tales show encouraging results over a naïve baseline and BOW approach for classification of emotional versus non-emotional contents, with some dependency on parameter tuning. We also discuss results for a tripartite model which covers emotional valence, as well as feature set alternations. In addition, we present plans for a more cognitively sound sequential model, taking into consideration a larger set of basic emotions.

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A method to classify emotional expressions of text and synthesize speech

Cited by 6 publications

References 1 publication

Application of Affective Lexicons in Sports Text Mining: A Case Study of Fifa World Cup 2018

Application of Affective Lexicons in Sports Text Mining: A Case Study of Fifa World Cup 2018

Adaptive Machine Learning Approach for Emotional Email Classification

Emotions from text

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