Research Area: Machine Learning
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 [1]–[3], 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.
Name of the Researcher: Ahmed Sulaiman M. Alharbi
Name of the Supervisor(s): Dr. Elise de Doncker,Dr. Alvis Fong , Dr. Ikhlas Abdel-Qader
Year of Completion: 2019
University: Western Michigan University
Thesis Link: Home Page Url
