Abstract—With more and more frequency, users communicate with each other on social media. Many users start on Twitter or Facebook to find friends who have the same hobby. Our study proposes a method to estimate the users’ interests (hobby) based on tweets on Twitter. One tweet does not, in and of itself, contain a lot of information, and some tweets are not related to the user’s hobby. Therefore, we propose a reliable hobby estimation method by extracting features from multiple, sequential tweets. The proposed method uses Recurrent Neural Networks (RNN) which can accommodate time-series information. We also used a Convolutional Neural Networks (CNN) which can treat contextual information. We used an averaged vector of word distributed representation as a feature. Using the proposed method based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), we obtained a 23.72% improvement as compared with a baseline method using a Random Forest (RF) regression as a machine learning algorithm.
Index Terms—Hobby estimation, deep neural networks, sequential statements, social media.
Koji Bando is with President & CEO of NTT Plala Inc., Tokyo, Japan.
Kazuyuki Matsumoto is with Tokushima University, Japan (e-mail: email@example.com).
Minoru Yoshida is with Department of Information Science and Intelligent Systems, University of Tokushima, Japan.
Kenji Kita is with ATR Interpreting Telephony Research Laboratories, Kyoto, Japan.
Cite: Koji Bando, Kazuyuki Matsumoto, Minoru Yoshida, and Kenji Kita, "Twitter User’s Hobby Estimation Based on Sequential Statements Using Deep Neural Networks," International Journal of Machine Learning and Computing vol. 9, no. 1, pp. 108-114, 2019.