Reseach And Implementation Of Text Stance Detection Algorithm For Social Media

With the continuous rise of the Internet and the continuous popularization of major social media,users with smart terminals can browse current affairs hotspot reports anytime,anywhere.They can use social media platforms such as Twitter,Weibo,and Zhihu to express their opinions and exchange opinions in a timely manner.In application scenarios such as rumor monitoring and public opinion analysis,the user's stance expressed in the text is very important.However,the traditional manual sampling survey method has the problems of high cost and low efficiency.Therefore,we need a series of automated means to help us identify the user's stance on the relevant event from the massive social media text.Traditional sentiment analysis technology only conducts polarity analysis on text,and cannot classify the stance of specific event targets.Therefore,in response to the existing stance detection needs,designing new stance detection methods and models to improve the efficiency and accuracy of the existing algorithms is the focus of this target,which is mainly divided into the following three points.1.Supervised stance classification algorithm based on long and short-term memory neural network and attention mechanism:First,the word embedding model is pre-trained through the social media corpus to extract the semantic features of the vocabulary.Then,the semantic representation of words is learned through the multi-layer two-way long and short-term memory neural network,combined with the attention mechanism to absorb target features,and comparison experiments are performed on different data sets to verify the effect of the algorithm.2.Weakly supervised stance classification algorithm based on graph neural network:traditional supervised learning algorithms have common problems such as high cost of labeling and too little labeling data.Aiming at the task characteristics of stance detection,this paper constructs the user's social text as a heterogeneous graph of document vocabulary,and uses a graph attention network to update the node characteristics to improve the experimental effect of weakly supervised stance classification.3.Design and implementation of the text stance detection system:In order to reduce the threshold for users to use,we built a text stance detection system based on the algorithm proposed in this target.We designed the relevant components in detail,interacted directly with the user through the front end of the webpage,and tested the data crawling module and the stance classification module to achieve a simple and convenient text stance detection system.