Research Of Topic Detection And Sentiment Analysis Of Short Video Text Comments

In the era of big data,short video platforms account for an increasing proportion of social life.Its content involves social news,cultural entertainment,e-commerce marketing and other aspects.The number of short video users is increasing exponentially.A large number of users freely express their opinions and views on all kinds of short videos based on the short video platform,resulting in a large amount of text review data.The topic detection and emotional analysis of short video text commentary data can correctly guide public opinion and understand people’s livelihood to a certain extent,and provide certain reference value for the commercial marketing plan and the formulation of relevant government control measures.The main tasks of this thesis are mainly divided into three aspects:Firstly,through Python data crawling technology,collecting short video text review data related to multiple social hot events on webpage version of Tiktok short video platform in a limited time domain,and clean and filter the collected data.TP-PS-Spectral clustering algorithm is proposed based on spectral clustering algorithm and pairwise similarity calculation.It performs best on short video text review dataset and Chapter level Chinese news report public dataset compared with the set comparison algorithm,reaching 96.83% and 95.91% on ARI value respectively.For each cluster after clustering,three keyword extraction methods,LDA,TF-IDF and Text Rank,are used to extract topic keywords.Secondly,conducting an sentiment orientation analysis of the events involved in the topic detection.In this thesis,Bi LSTM model,attention mechanism and DPCNN model are combined and optimized,and a dual-channel neural network model(DC-EBAD)based on ERNIE pre-training model is constructed as an experimental model for emotional polarity binary classification.An improved DC-EB2 AD model with dual attention mechanism is proposed to further improve the accuracy of emotional polarity classification.The experimental results show that the accuracy of affective propensity discrimination of DC-EBAD model in the short video text commentary dataset used in thesis research and Chinese takeaway comments public dataset is 92.50% and 92.73% respectively.The performance of DC-EBAD model is better than that of other single-channel models as comparison.And the accuracy of DC-EB2 AD model in the two datasets is 93.22% and 93.15% respectively,which is slightly higher than DC-EBAD model.Finally,visualizing the sentiment orientation of the events involved in the short video text comment topic.On the one hand,taking the extraction and filtering of specific keywords as the core,Python-based visual display uses the way of word cloud map to intuitively understand users’ emotional views on related events;On the other hand,with regular processing and key short sentences as the core,Neo4j-based user sentiment view map is constructed to show and analyze the positive and negative emotional views in each event.