Global Centrality Measures in Word Graphs for Twitter Sentiment Analysis

This paper presents a word graph based method for Twitter sentiment analysis (TSA) using global centrality metrics over graphs to evaluate sentiments, either positive or negative, expressed in microblogs. The proposed technique measures the importance of a sentence s for a given sentiment graph G by calculating its SentiElection coefficient. SentiElection, the method introduced in this work, is an ensemble of three global centrality measures: Katz index, Eigenvector centrality and PageRank. The results are compared to a previous model based on Containment similarity and Maximum Common Subgraph-based similarity metrics specifically designed to identify sentiments expressed in short texts. Using the geometric mean of their accuracies, we show the new suggested method outperforms the previous one.