ICFICE 2018
Current Result Document : 7 / 7
| 한글제목(Korean Title) |
A Machine Learning Approach to Predict Happiness Based on Sentiment Analysis of Twitter Data |
| 영문제목(English Title) |
A Machine Learning Approach to Predict Happiness Based on Sentiment Analysis of Twitter Data |
| 저자(Author) |
Ki-Won Choi
Satyabrata Aich
Hee-Cheol Kim
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| 원문수록처(Citation) |
VOL 10 NO. 01 PP. 0239 ~ 0241 (2018. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Happiness analysis is a method focused on estimating the degree of happiness of the author among sentiment analysis. It can be applied to various aspects such as group happiness analysis and individual mental health analysis. In this paper, we study the happiness analysis algorithm by applying a dictionary - based analysis method and a machine learning - based analysis method to estimate the degree of happiness through text. We have collected 10,000 Korean tweets from twitter. We have used two machine learning approach such as KNN and SVM for the happiness analysis of twitter data. We have found accuracy of 83% and 88% respectively. This sentiment based analysis will help to predict the degree of happiness of people of Korea.
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| 키워드(Keyword) |
Text Mining
Sentiment Analysis
Machine Learning
Happiness Analysis
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| 파일첨부 |
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