Emotion Recognition of Emoticons Based on Character Embedding

Matsumoto, Kazuyuki; Fujisawa, Akira; Yoshida, Minoru; Kita, Kiyoshi

doi:10.17706/jsw.12.11.849-857

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…correlated features. But when the input features are correlated, the assumption of strong independence of Naive Bayesian network is violated, and the classification effect is not good[21].3.3.2. Hidden Markov Model.Hidden Markov model is used to describe a Markov process with hidden unknown parameters, which is a statistical model.…”

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confidence: 99%

Multimodal Emotion Recognition Algorithm for Artificial Intelligence Information System

Weng

Lin

2022

Wireless Communications and Mobile Computing

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Emotional expression is an essential and important link in human social activities and daily communication activities. Emotion recognition is a technology that uses computer technology to recognize the emotion of recognized objects. When emotion recognition technology was born, the research on human emotion recognition mainly focused on single-modal emotion recognition. Single-modal emotion recognition has been the most commonly used emotion recognition method. However, in recent years, with the continuous development of science and technology and the continuous improvement of people’s awareness of the defects of single-modal emotion recognition, the research on multimodal emotion recognition is increasingly emerging. This paper is aimed at analyzing and studying the multimodal emotion recognition algorithm in artificial intelligence information system. Based on this research topic, this paper designs a single-modal and multimodal emotion recognition comparison simulation experiment for the four emotional characteristics of happiness, sadness, shock, and anger extracted from artificial intelligence information systems. The experiment concluded that the comprehensive emotion recognition accuracy rate of the multimodal emotion recognition algorithm in the artificial intelligence information system reached 93%. Compared with single-mode emotion recognition, the comprehensive recognition accuracy is improved by 7%.

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confidence: 99%

Multimodal Emotion Recognition Algorithm for Artificial Intelligence Information System

Weng

Lin

2022

Wireless Communications and Mobile Computing

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“…Matsumoto et al [5] proposed a method for emotion estimation that learns the character features of emoticons with a convolutional neural network. In their method, the characters of emoticons are converted into character variance representation by word2vec, and their weights are used as initial parameters that are input to a one-dimensional convolutional neural network.…”

Section: Emotion Estimation From Emoticonsmentioning

confidence: 99%

Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences

et al. 2022

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This paper proposes an emotion recognition method for tweets containing emoticons using their emoticon image and language features. Some of the existing methods register emoticons and their facial expression categories in a dictionary and use them, while other methods recognize emoticon facial expressions based on the various elements of the emoticons. However, highly accurate emotion recognition cannot be performed unless the recognition is based on a combination of the features of sentences and emoticons. Therefore, we propose a model that recognizes emotions by extracting the shape features of emoticons from their image data and applying the feature vector input that combines the image features with features extracted from the text of the tweets. Based on evaluation experiments, the proposed method is confirmed to achieve high accuracy and shown to be more effective than methods that use text features only.

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“…Matsumoto et al [5] proposed a method to classify emoticon into emotion category by using character 1 http://www.keishicho.metro.tokyo.jp/about_mpd/shokai/pipo/profile.html 2 http://kenji1234.blog75.fc2.com/ embedding feature. Their method could better performance than the baseline method using character N-gram feature based on machine learning algorithms such as SVM, logistic regression, etc.…”

Section: Classification Of Ascii Artmentioning

confidence: 99%

“…(5),(6),(7) indicate each evaluation score. In Eq (5). and(6), indicates a true positive, an outcome in which the system correctly estimated category c. In Eq.…”

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confidence: 99%

ASCII Art Classification based on Deep Neural Networks Using Image Feature of Characters

Matsumoto¹,

Fujisawa²,

Yoshida³

et al. 2018

JSW

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In recent years, a lot of non-verbal expressions have been used on social media. Ascii art (AA) is an expression using characters with visual technique. In this paper, we set up an experiment to classify AA pictures by using character features and image features. We try to clarify which feature is more effective for a method to classify AA pictures. We proposed five methods: 1) a method based on character frequency, 2) a method based on character importance value and 3) a method based on image features, 4) a method based on image features using pre-trained neural networks and 5) a method based on image features of characters. We trained neural networks by using these five features. In the experimental result, the best classification accuracy was obtained in the feed forward neural networks that used image features of characters.

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Emotion Recognition of Emoticons Based on Character Embedding

Cited by 4 publications

References 7 publications

Multimodal Emotion Recognition Algorithm for Artificial Intelligence Information System

Multimodal Emotion Recognition Algorithm for Artificial Intelligence Information System

Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences

ASCII Art Classification based on Deep Neural Networks Using Image Feature of Characters

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