# Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM
## 5 Conclusions  
To address gap in the research field of short video comment content and the problems of sparse short text data and incomplete feature extraction, this paper crawled all the comments in videos about _Carya cathayensis_ from April 2021 to April 2022 on the TikTok platform as the research object, combined the LDA topic model with the Word2 Vec method, and thus proposed a feature fusion method based on short text topic vector. After that, the fused word vectors were input into the BiLSTM for backward and forward text semantic modeling to obtain the high-level feature expressions of text sequences. The Self-Attention was used to assign certain weights to important features to reduce the influence of features of noise. Considering that the weight update would lead to reliance on some features causing model overfitting, the dropout layer was added to randomly deactivate some parameters and finally input to the softmax classifier to achieve the purpose of classification for short video comment hotspots. The specific findings of the study are as follows:  
1. A study using TikTok short video comments as a dataset was proposed. Crawling comments in TikTok short video about _Carya cathayensis_ as the original data for short video research expands the ideas for short video marketing research. It fills the gap in the field of short video comment research.  
2. The method of feature fusion based on the combination of the LDA topic model and Word2 Vec word vector was proposed. The method incorporated the topic features of the text based on the word vector, extended the feature representation of the comment information, and solved the problem of inconspicuous sparse feature representation of short text to some extent.  
3. The short text classification model based on the feature fusion-LBSA model was proposed. It had a better classification effect for the short video comments of _Carya cathayensis_, with a precision of 91.65%, which was 2\(\sim\)5% higher than CNN, LSTM, GRU, BiLSTM, CNN-LSTM, GRU-CNN, LDA-CNN, LDA-BiLSTM, CNN-Self-Attention, and BiLSTM-Self-Attention models. The research of this method can help merchants quickly extract the discussion hotpots of consumers in videos from many comments and provide new decision information for _Carya cathayensis_ merchants when posting videos and selling _Carya cathayensis_.  
Figure 7: Confusion matrix comparison chart. (**a**) the confusion matrix diagram of the BiLSTM-Self-Attention. (**b**) the confusion matrix diagram of the LBSA.  
The feature fusion rules set in this paper are simple, and the impact of potential topics on the classification effect is not considered. In addition, the current data set has a single domain, and each comment only contains one kind of hotspot. Therefore, the next research direction is to enhance the comprehensiveness of theme feature fusion, expand the data set, expand the data set in different fields, and identify multiple hot spots in a review.  
Conceptualization, L.L. and D.D.; methodology, L.L. and D.D.; software, L.L.; validation, L.L.; formal analysis, L.L., H.L, Y.Y., L.D. and Y.X.; investigation, L.L., D.D., H.L., Y.Y., L.D. and Y.X.; data curation, L.L.; writing--original draft. L.L.; resources, D.D. and H.L.; writing--review & editing, D.D. and H.L.; visualization, D.D., H.L. and Y.Y.; supervision, D.D. and H.L.; project administration. D.D. and H.L.; funding acquisition, D.D., H.L. and L.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Youth Science Foundation Project under grant number 42001354, the Ministry of Education Humanities and Social Sciences Research Program Fund Project under grant number 18 YJA630030 and 21 YJA630054, and the Basic Public Welfare Research Program of Zhejiang Province under grant number LGN18C130003.  
Not applicable.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.