Detecting Topics and Polarity From Twitter: A University Faculty Case

Abstract

Social networks have become a powerful communication tool, with millions of people exchanging information, opinions, and experiences daily. Companies, organizations, and even people have turned this tool into a marketing platform to position themselves and gain popularity. However, not only do companies present products or services to society, but society also provides feedback. This feedback also has a significant impact. It is impossible to process all this vast information manually in time, but it is crucial. This information is precious even to governmental or public entities such as universities. Potential future students will use social media to learn about the general feel of the institution. Therefore, this study presents a new dataset called CEIMaT2021, which compiles all tweets in Spanish related to the Technical School of Industrial Engineering of the Universidad Politécnica de Madrid (ETSII-UPM). This dataset is designed for two main tasks of Online Reputation Management: 1) automatic detection of topics and 2) polarity. Furthermore, this study shows that the BETO model obtains better performance for topic detection for these tasks. Meanwhile, the MarIA model obtains better results for polarity detection.

Keywords

• Dataset
• information retrieval
• polarity
• social network analysis
• topic
• Twitter