Twitter User’s Hobby Estimation Based on Sequential Statements Using Deep Neural Networks

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

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories