Co-training and visualizing sentiment evolvement for tweet events
Shenghua Liu, Wenjun Zhu, Ning Xu, Fangtao Li, Xueqi Cheng, Yue Liu, Yuanzhuo Wang · 2013
Sentiment classification on tweet events attracts more interest in recent years. The large tweet stream stops people reading the whole classified list to understand the insights. We employ the co-training framework in the proposed algorithm. Features are split into text view features and non-text view features. Two Random Forest (RF) classifiers are trained with the common labeled data on the two views of features separately. Then for each specific event, they collaboratively and periodically train together to boost the classification performance. At last, we propose a "river" graph to visualize the intensity and evolvement of sentiment on an event, which demonstrates the intensity by both color gradient and opinion labels, and the ups and downs of confronting opinions by the river flow. Comparing with the well-known sentiment classifiers, our algorithm achieves consistent increases in accuracy on the tweet events from TREC 2011 Microblogging and our database. The visualization helps people recognize turning and bursting patterns, and predict sentiment trend in an intuitive way.