Advertisement
Published in:
2014 | OriginalPaper | Chapter
Feature Based Sentiment Analysis of Tweets in Multiple Languages
Authors : Maike Erdmann, Kazushi Ikeda, Hiromi Ishizaki, Gen Hattori, Yasuhiro Takishima
Published in: Web Information Systems Engineering – WISE 2014
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Feature based sentiment analysis is normally conducted using review Web sites, since it is difficult to extract accurate product features from tweets. However, Twitter users express sentiment towards a large variety of products in many different languages. Besides, sentiment expressed on Twitter is more up to date and represents the sentiment of a larger population than review articles. Therefore, we propose a method that identifies product features using review articles and then conduct sentiment analysis on tweets containing those features. In that way, we can increase the precision of feature extraction by up to 40% compared to features extracted directly from tweets. Moreover, our method translates and matches the features extracted for multiple languages and ranks them based on how frequently the features are mentioned in the tweets of each language. By doing this, we can highlight the features that are the most relevant for multilingual analysis.