Twitter stance detection using deep learning model with FastText Embedding

Abstract

The interactivity of social media platforms allows a large number of users to comment on different political or social issues to express their views, and identifying users' stances from online comment texts helps the government to monitor public opinion more effectively. The automatic recognition of stance information in comment text has become a new research hotspot in the field of natural language processing. Most of the existing text stance analysis corpus focuses on political topics in European and American countries, and high-quality stance analysis corpus research on political topics in Southeast Asian countries is relatively scarce. In order to stimulate this research direction, this paper provides a dataset about the 2022 Philippine presidential election, which annotates the stance information of the two popular presidential candidates and provides reliable data support for subsequent stance analysis model research. Next, we build a stance detection model of hybrid deep neural networks based on BiLSTM, CNN, and Attention, and we demonstrate its effectiveness on multiple datasets and obtain the best results on the SemEval-2016 dataset. In addition, we compare FastText and Word2Vec, two pre-trained word embeddings for word encoding, and discuss which word embedding is preferred in stance detection tasks. This result shows that the stance analysis model proposed in this paper can be effectively applied to Twitter text stance data.