Social Media Sentiment Analysis with a Deep Neural Network: An Enhanced Approach Using User Behavioral Information
Date of Award
Doctor of Philosophy
Dr. Elise de Doncker
Dr. Alvis Fong
Dr. Ikhlas Abdel-Qader
Opinion mining, sentiment analysis, social media, deep learning, natural language processing, Twitter
Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data (including tweet length, spelling errors, abbreviations, and special characters), the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis constitutes a fundamental problem with many interesting applications, such as for Business Intelligence, Medical Monitoring, and National Security. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this research, we propose deep learning based frameworks that also incorporate user behavioral information within a given document (tweet). Within these frameworks, there are several models based on a variety of neural network architectures. Each of these models is trained on a specific aspect of user behavior. Then, the frameworks exploit these multi-aspect learning models to jointly take on a mutual task (the sentiment analysis task). The results of the preliminary experiments, which are reported in –, demonstrate that going beyond the content of a document is beneficial in sentiment classification, because it provides the classifier with a deeper understanding of the task.
Restricted to Campus until
Alharbi, Ahmed Sulaiman M., "Social Media Sentiment Analysis with a Deep Neural Network: An Enhanced Approach Using User Behavioral Information" (2019). Dissertations. 3513.