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http://dx.doi.org/10.36498/kbigdt.2020.5.1.197

Emotion Analysis Using a Bidirectional LSTM for Word Sense Disambiguation  

Ki, Ho-Yeon (이화여자대학교 일반대학원 빅데이터분석학 협동과정)
Shin, Kyung-shik (이화여자대학교 일반대학원 빅데이터분석학 협동과정)
Publication Information
The Journal of Bigdata / v.5, no.1, 2020 , pp. 197-208 More about this Journal
Abstract
Lexical ambiguity means that a word can be interpreted as two or more meanings, such as homonym and polysemy, and there are many cases of word sense ambiguation in words expressing emotions. In terms of projecting human psychology, these words convey specific and rich contexts, resulting in lexical ambiguity. In this study, we propose an emotional classification model that disambiguate word sense using bidirectional LSTM. It is based on the assumption that if the information of the surrounding context is fully reflected, the problem of lexical ambiguity can be solved and the emotions that the sentence wants to express can be expressed as one. Bidirectional LSTM is an algorithm that is frequently used in the field of natural language processing research requiring contextual information and is also intended to be used in this study to learn context. GloVe embedding is used as the embedding layer of this research model, and the performance of this model was verified compared to the model applied with LSTM and RNN algorithms. Such a framework could contribute to various fields, including marketing, which could connect the emotions of SNS users to their desire for consumption.
Keywords
Bidirectional LSTM; GloVe; Emotion Analysis; Lexical Ambiguity; Word Sense Disambiguation; NLP;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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