Abstract
In measuring TV ratings, some features can be significant at a certain time, whereas they can be meaningless in other time periods. Because the importance of features can change, a model capturing the time changing relevance is required in order to estimate TV ratings more accurately. Therefore, we focus on the time-awareness of features, particularly the time when the words of tweets are used. We develop a correlation-based, time-aware feature selection algorithm which finds the optimal time period of each feature, and the estimation method using e-SVR based on top-n-features that are ordered by correlation. We identify that the correlation values between features and TV ratings vary according to the time of postings - before and after the broadcast time. This implies that the relevance of features can change according to the time of the tweets. Experimental results indicate that the proposed method has better performance compared with the method based on count-based features. This result implies that understanding the time-dependency of features can be helpful in improving the accuracy of measuring TV ratings.
Original language | English |
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Pages (from-to) | 2810-2813 |
Number of pages | 4 |
Journal | IEICE Transactions on Information and Systems |
Volume | E97D |
Issue number | 10 |
DOIs | |
State | Published - 1 Oct 2014 |
Bibliographical note
Publisher Copyright:Copyright © 2014 The Institute of Electronics, Information and Communication Engineers.
Keywords
- Microblog
- Time awareness
- TV ratings