2017
DOI: 10.17706/jsw.12.11.849-857
|View full text |Cite
|
Sign up to set email alerts
|

Emotion Recognition of Emoticons Based on Character Embedding

Abstract: This paper proposes a method for estimating the emotions expressed by emoticons based on a distributed representation of the character meanings of the emoticon. Existing studies on emoticons have focused on extracting the emoticons from texts and estimating the associated emotions by separating them into their constituent parts and using the combination of parts as the feature. Applying a recently developed technique for word embedding, we propose a versatile approach to emotion estimation from emoticons by tr… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
4
0

Year Published

2018
2018
2022
2022

Publication Types

Select...
3
1

Relationship

2
2

Authors

Journals

citations
Cited by 4 publications
(4 citation statements)
references
References 7 publications
0
4
0
Order By: Relevance
“…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.…”
mentioning
confidence: 99%
“…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.…”
mentioning
confidence: 99%
“…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%
“…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.…”
mentioning
confidence: 99%