Classification of the Stance in Online Debates Using the Dependency Relations Feature
Online discussion forums offer Internet users a medium for discussions about current political debates. The debate is a system of claims regarding interactivity and representation. Users make claims in an online discussion with superior content to support their position. Factual accuracy and emotional appeal are critical attributes used to convince readers. A key challenge in debate forums is to identify the participants’ stance, each of which is inter-dependent and inter-connected. This research work aims to construct a classifier that takes the linguistic features of the posts as input and outputs predictions for the stance label of each post. Three types of features which include Lexical, Dependency, and Morphology are used to detect the stance of the posts. Lexical features such as cue words are employed as surface features, and deep features include dependency and morphology features. Multinomial Naïve Bayes classifier is used to build a model for classifying stance and the Chi-Square method is used to select the good feature set. The performance of the stance classification system is evaluated in terms of accuracy. The result of stance labels for this proposed research represents as for and against by analyzing the surface and deep features that capture the content of a post.
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