NDLI: A corpus-based approach to classifying emotions using Korean linguistic features

Content Provider	Springer Nature Link
Author	Jung, Younghee Park, Kinam Lee, Taemin Chae, Jeongmin Jung, Soonyoung
Copyright Year	2017
Abstract	Recently, social network services have become the popular communication tools among internet and mobile users. And it has been shared various opinions, which could be included various emotions. Emotion analysis aims to extract various emotion information, such as joy, happy, funny, fear, sad, and lonely, and so on, from texts expressed in natural language. Previous studies about emotion analysis on texts written in Korean have focused generally on the basic sentiments such as positive/neutral/negative preferences or 4–10 emotion classes. In this paper, we propose an emotion analysis method based on supervised learning to classify various emotions from messages written in Korean. We had found the feature set optimized to each emotion class through evaluating the combinations of various linguistic features and built a model to classify the emotion using the optimized feature set. To do this, it was constructed the corpus that is manually annotated with 25 emotion classes. We performed a 10-fold cross variation experiment for evaluating the performance of the proposed method. Our method obtained F-value ranged from 73.1 to 98.0% for each of 25 emotion classes. The optimized feature sets for most of emotion classes include commonly word 2-gram, POS 1-gram, and character 1-gram feature.
Starting Page	583
Ending Page	595
Page Count	13
File Format	PDF
ISSN	13867857
Journal	Cluster Computing
Volume Number	20
Issue Number	1
e-ISSN	15737543
Language	English
Publisher	Springer US
Publisher Date	2017-02-27
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Emotion recognition Emotion analysis Emotion classification Message Processor Architectures Operating Systems Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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