Mining and analysis of hot topics in Social Media

Abstract

In social media, useful data information is mined to identify hot topics and predict the development direction of hot topics. The research method is to use web crawler to obtain relevant data, find new hot topics in media, get the original data, use Jieba to segment words, generate word segmentation results, obtain eigenvalues through Term Frequency - Inverse Document Frequency (TF-IDF) word frequency statistics, and use k-means algorithm to complete the clustering of eigenvalues, obtain the cluster results, and judge the hot topics in the cluster. In the experiment of advanced optimization hot topic, Linear Discriminant Analysis (LDA) method is used to classify the topic model, Gibbs sampling is used to sample and train the model, and K-means algorithm is used to achieve the purpose of clustering. The recall rate, accuracy rate and F value are calculated, and the experimental results of hot topics are summarized and analyzed to obtain the LDA model results of current hot topics in network media.