| Citation: | WANG Zechen, WANG Shupeng, SUN Liyuan, et al. Weibo tendency analysis based on sentimental object recognition and sentimental rules[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(2): 301-310. doi: 10.13700/j.bh.1001-5965.2020.0404(in Chinese)
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Weibo contains a large number of information reflecting users' likes and dislikes, which is important for popular trend judgment, precision marketing, public opinion monitoring, etc. However, the existing methods tend to focus on the classification of Weibo sentiment. In order to solve the problem of Weibo tendentiousness analysis and position detection, we employ semisupervised learning method, through collaborative training and active learning. We train entity recognition models and combine deep learning with emotional rules. Moreover, the sentiment rules based on principal component analysis are constructed to extract the main components of sentences, normalize the spoken text into the specified format. Then we use the positive and negative aspects of directional entities, the positive and negative meanings of emotional words, and the sentence components of emotional words to judge the tendency of blog posts, and conduct deeper analysis on position classification. Finally, the self comparison experiment and other comparison experiment on different scale data sets show that with the increase of the number of blog posts of labeled entities, the accuracy of the model continues to improve, and the accuracy of this method is significantly higher than the comparison method, which is 2.79% and 10.00% higher than the existing research methods.
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