A Novel Method for Twitter Sentiment Analysis Based on AttentionalGraph Neural Network Twitter sentiment analysis is an effective tool for various Twitterbased analysis tasks. However, there is still no neuralnetworkbased research which takes both the tweettext information and userconnection information into account. To this end, we propose the Attentionalgraph Neural Network based Twitter Sentiment Analyzer (AGNTSA), a Twitter sentiment analyzer based on attentionalgraph neural networks. AGNTSA fuses the tweettext information and the userconnection information through a threelayered neural structure, which includes a wordembedding layer, a userembedding layer and an attentional graph network layer. For the training of AGNTSA, dedicated loss functions are designed for the structural controllability of AGNTSA network. Experiments based on realworld dataset concerning the 2016 presidential election of America exhibit that AGNTSA is superior under multiple metrics over several prevailing methods, with a performance boost of over 5%. The empirical settings of parameters are given based on extensive rotation experiments.

Sentiment Analysis of Events in Social Media The growing popularity of Online Social Networks has open new research directions and perspectives for content analysis, i.e., Network Analysis and Natural Language Processing. From the perspective of information spread, the Network Analysis community propose Event Detection. This approach focuses on the network features, without an indepth analysis of the textual content, summarization being a preferred method. Natural Language Processing analyses only the textual content, not integrating the graphbased structure of the network. To address these limitations, we propose a method that bridges the two directions and integrates contentawareness into networkawareness. Our method uses event detection to extract topics of interest and then applies sentiment analysis on each event. The obtained results have high accuracy, proving that our method determines with high precision the overall sentiment of the detected events.