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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.