Male and Female Users’ Differences in Online Technology Community Based on Text Mining

With the emergence of online communities, more and more people are participating in online technology communities to meet personalized learning needs. This study aims to investigate whether and how male and female users behave differently in online technology communities. Using text data from the Python Technology Community, through the LDA (Latent Dirichlet Allocation) model, sentiment analysis, and regression analysis, this paper reveals the different topics of male and female users in the online technology community, their sentimental tendencies and activity under different topics, and their correlation and mutual influence. The results show the following: (1) Male users tend to provide information help, while female users prefer to participate in the topic of making friends and advertising. (2) When communicating in the technology community, male and female users mostly express positive emotions, but female users express positive emotions more frequently. (3) Different emotional tendencies of male and female users under different topics have different effects on their activity in the community. The activity of female users is more susceptible to emotional orientation.