Document Stance Detection using Word Embedding and Target Vector: A Novel Method Based on ANOVA

Stance detection, which refers to the assessment of a statement's position regarding a specific target, is recognized as an important area in natural language processing. This process generally involves identifying whether a claim is in favor of, against, or neutral towards a particular subject. With the expansion of social networks and the increase in the sharing of opinions and viewpoints, stance detection from textual data has become a powerful tool for analyzing and understanding public opinion. To date, most research in this field has focused on using trained word embedding models to assess the stance of texts. However, these efforts often overlook the key role of the targets against which a text is evaluated. In this paper, considering the importance of determining targets in stance detection, we present an innovative approach that focuses on target-based embeddings. Using the SemEval۲۰۱۶ dataset, which includes five different targets, allowed us to demonstrate the effectiveness of our proposed method. We were able to achieve an average accuracy of ۷۹.۵ percent on this dataset, which is a ۴ percent improvement over the results of previous works.

کلیدواژه ها:

Stance Detection ، Textual Data ، word embedding ، Target embedding ، Stance Prediction ، Ensemble Learning