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Research on Classification and Feature Analysis of Weibo Data

Suicide is now a social and mental health problem that the world needs to continue to pay attention to, and it has caused significant economic and spiritual losses to society and families. With the rapid development of science and technology and the continuous improvement of people’s living standards, the Internet and social platforms have played an increasingly greater role in people’s lives, and new ideas have been brought to the solution of this problem. Young people believe that in social networks, they can freely discuss suicide-related topics, and talk about and exchange emotions and feelings related to suicide. At the same time, social platforms are also an effective tool for them to seek help and help others. We use Weibo as a platform to conduct preliminary research on suicide ideation cause extraction, and build a classification-based suicide ideation cause extraction model that combines content feature, context feature, part-of-speech feature, grammatical feature, emotional feature, and language feature. Experiments show that word feature (words, word vectors) perform better than other feature in various evaluation indicators, indicating that word feature can effectively reflect the content of Weibo.