Yuki Yamamoto
ABSTRACT
Twitter has become a popular social media service, accumulating and distributing vast amounts of information for its numerous users. One feature of Twitter is that it enables a user to follow other users, who can obtain the information her/his followees tweeted. However, it is difficult for the user to find a promising followee because there are so many Twitter users. Therefore, numerous studies have investigated the issue of recommendation of followees for Twitter users. Many methods recommend followees based on topics extracted from their tweets. It seems beneficial to recommend followees who not only have similar interests but also similar sentiments to those of the user. We propose a system that recommends a followee based on topics and their sentiments about topics. In this paper, as a first step of our study on followee recommendation, new followee candidates are limited to people who are being followed by at least one of the user's followees but who the user is not following. In this paper, we refer to the target persons as "ff-users." (1) For each ff-user, our proposed system uses clustering to extract common topics between the user and ff-user, and then it extracts the sentiments of their tweets about each of the common topics. (2) As a result, those ff-users who have a larger number of similar sentiments related to a larger number of common topics are recommended as new followees. (3) The system visualizes user sentiments and those of candidate followees using a radar chart. We also conducted an experiment, and confirmed the validity of our proposed system.
- Marcelo G. Armentano, Daniela Godoy, and Analia Amandi. Towards a followee recommender system for information seeking users in twitter. In Proceedings of the Workshop on Semantic Adaptive Social Web, pp. 27--38, 2011.Google Scholar
- Marcelo G. Armentano, Daniela Godoy, and Analia Amandi. Recommending information sources to information seekers in twitter. In International workshop on social web mining, 2011.Google Scholar
- M. Pennacchiotti and S. Gurumurthy. Investigating topic models for social media user recommendation. In Proceedings of the 20th International Conference Companion on World Wide Web, pp. 101--102, 2011.Google ScholarDigital Library
- Nakamura A. Sentiment Dictionary. Tokyodo Publisher, 1993.Google Scholar
- Jilin Chen, Werner Geyer, Casey Dugan, Michael Muller, and Ido Guy. Make new friends, but keep the old: Recommending people on social networking sites. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 201--210, 2009.Google ScholarDigital Library
- John Hannon, Kevin McCarthy, and Barry Smyth. Finding useful users on twitter: twittomender the followee recommender. In Advances in Information Retrieval 33rd European Conference on IR Research, pp. 18--21, 2011.Google ScholarDigital Library
- Marcelo G. Armentano, Daniela Godoy, and Analia Amandi. A topology-based approach for followees recommendation in twitter. In Workshop chairs, p. 22, 2011.Google Scholar
- Weng Jianshu, Lim Ee-Peng, Jiang Jing, and He Qi. Proc. of the third acm international conference on web search and data mining. In TwitterRank: finding topic-sensitive influential twitterers, pp. 261--270, 2010.Google Scholar
- Adam Sadilek, Henry Kautz, and Jeffrey P. Bigham. Proc. of the fifth acm international conference on web search and data mining. In Finding your friends and following them to where you are, pp. 723--732, 2012.Google Scholar
- K Dave, S Lawrence, and David M. Pennock. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International Conference on World Wide Web, pp. 519--528, 2003.Google ScholarDigital Library
- M Kobayashi, T Inui, and K. Inui. Dictionary-based acquisition of the lexical knowledge for p/n analysis (in japanese). SIG-SLUD, pp. 45--50, 2001.Google Scholar
- S Fujimura, M Toyoda, and M. Kitsuregawa. A reputation extracting method considering structure of sentence (in japanese). Institute of Electronics, Information and Communication Engineers, Data Engineering Wordkshop, 2005.Google Scholar
- Davide F. Gurini and F. Gasparetti. A sentiment-based approach to twitter user recommendation. In In RSWeb@ RecSys, 2013.Google Scholar
- R Plutchik. The multi factor-analytic theory of emotion. In The Journal of Psychology: Interdisciplinary and Applied, pp. 153--171, 1960.Google Scholar
- R Plutchik. The nature of emotions. In American Scientist, pp. 344--350, 2001.Google ScholarCross Ref
- R Tokuhisa, K Inui, and Y. Matsumoto. Emotion classification using massive examples extracted from the web. In Proceedings of the 22Nd International Conference on Computational Linguistics - Volume 1, pp. 881--888, 2008.Google ScholarDigital Library
- H Takamura, T Inui, and M. Okumura. Extracting semantic orientations of words using spin model. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 133--140, 2005.Google ScholarDigital Library
- T. Kumamoto. Design of impression scales for assessing impressions of news articles. In Proceedings of the 15th International Conference on Database Systems for Advanced Applications, pp. 285--295, 2010.Google ScholarDigital Library
- T Kumamoto, Y Kawai, and K. Tanaka. Improving a method for quantifying readers' impressions of news articles with a regression equation. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pp. 87--95, 2011.Google ScholarDigital Library
- K Takaoka and A. Nadamoto. Words-of-wisdom search based on multi-dimensional sentiment vector. In Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services, pp. 66--73, 2011.Google ScholarDigital Library
- Ying Zhao and George Karypis. Comparison of agglomerative and partitional document clustering algorithms. Technical Report 02-014, University of Minnesota, 2002.Google ScholarCross Ref
- Fujisawa Mizuki. Bayon - a simple and fast clustering tool - Google Project Hosting, 2012. https://code.google.com/p/bayon/wiki/Tutorial_en [Online; accessed 11-August-2015](in Japanese).Google Scholar
- Karypis George. CLUTO - Software for Clustering High-Dimensional Datasets, 2006. http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview [Online; accessed 11-August-2015](in Japanese).Google Scholar
- Y. Yamamoto, T. Kumamoto, and A. Nadamoto. Multidimensional sentiment calculation method for twitter based on emoticons. In International Journal of Pervasive Computing and Communications, pp. 212--232, 2015.Google ScholarCross Ref
Index Terms
- Followee recommendation based on topic extraction and sentiment analysis from tweets
Recommendations
Emotion-based method for latent followee recommendation in Twitter
iiWAS '17: Proceedings of the 19th International Conference on Information Integration and Web-based Applications & ServicesSocial media services have become popular. Especially, Twitter is accumulating and distributing vast amounts of information for its numerous users. One feature of Twitter is that a user follows other users, who can obtain information that is tweeted ...
A personalized hashtag recommendation approach using LDA-based topic model in microblog environment
With wide use of cloud computing technologies, microblog is used more widely for services providing more personal communities by user information sharing, dissemination and acquisition. In Microblog environment, hashtag is used to find messages with a ...
Analyzing and predicting viral tweets
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide WebTwitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of ...
Comments