SENTI2POP: Sentiment-Aware Topic Popularity Prediction on Social Media

Topic popularity prediction is an important task on social media, which aims at predicting the ongoing trends of topics according to logged historical text-based records. However, only limited existing approaches apply sentiment analysis to facilitate popularity prediction. Public sentiment is worth taking into consideration because the topics with strong sentiment tend to spread faster and broader on social media. In this paper, we propose a novel framework, SENTI2POP, to predict topic popularity utilizing sentiment information. We first adapt a state-of-art popularity quantification method to capture the topic popularity, and then design a novel tree-like network (Tree-Net) combining Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for sentiment analysis. In addition, we propose a sentiment-aware time series prediction approach based on Dynamic Time Warping (DTW) and Autoregressive Integrated Moving Average model (ARIMA) to predict topic popularity. We prove by experiments that SENTI2POP outperforms the existing popularity prediction models on a real-world Twitter dataset by reducing the prediction error. Experimental results also show that SENTI2POP could be applied to improve the accuracy of most non-sentiment popularity prediction models.