Affiliation:
1. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70503, USA
Abstract
Microblog activity logs are useful to determine user’s interest and sentiment towards specific and broader category of events such as natural disaster and national election. In this paper, we present a corpus model to show how personal attitudes can be predicted from social media or microblog activities for a specific domain of events such as natural disasters. More specifically, given a user’s tweet and an event, the model is used to predict whether the user will be willing to help or show a positive attitude towards that event or similar events in the future. We present a new dataset related to a specific natural disaster event, i.e. Hurricane Harvey, that distinguishes user’s tweets into positive and non-positive attitudes. We build Term Embeddings for Tweet (TEmT) to generate features to model personal attitudes for arbitrary user’s tweets. In addition, we present sentiment analysis on the same disaster event dataset using enhanced feature learning on TEmT generated features by applying Convolutional Neural Network (CNN). Finally, we evaluate the effectiveness of our method by employing multiple classification techniques and comparative methods on the newly created dataset.
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Linguistics and Language,Information Systems,Software
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献