# Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM
## 1 Introduction  
Due to the influence of the COVID-19 epidemic, the traditional sales channels of agricultural products have been severely impacted, and the \"short video + live broadcast\" sales model with strong interaction and high participation is gradually replacing the traditional sales model. TikTok stands out among various short video platforms, on which 1.793 million special agricultural products have been sold nationwide [1]. When people watch short _Carya cathavensis_ videos on the TikTok, they will have a topical discussion about the video content, including people's views on the video or _Carya cathavensis_-related content. The problem of how to discover the information contained in it and make use of the information to promote the realization of people's consumption behavior is what businesses are eager to solve at this stage. User comments contain a large amount of user information and subjective emotional expression, but the sentences in user comments are short, context-dependent, colloquial, and noisy, which means it is difficult to classify the hot topics discussed by users. These comments need to be analyzed through data mining in order to understand the communication trend of video marketing and improve the quality of audience discussion hot spot analysis. [PERSON] [2] used the LDA model to achieve a good clustering analysis effect on online public opinion text data. [PERSON] [3] applied LDA model to short text clustering and featureextraction, proving that LDA topic model plays an important role in text mining. Most current classification models are generally based on sentiment classification and large-scale datasets rough domain classification of online comments. Although contextual and local features are considered in the feature extraction of short texts, there are still some shortcomings in it. The LDA topic model can represent the topic information and global information contained in the text. LDA maps the higher-dimensional document-word vector space to the lower-dimensional document-topic and topic-word vector spaces. The potential semantic information of short text can be mined effectively. In order to further improve the classification accuracy of short text reviews, efficiently mine the opinions of individual product reviews, and learn the hot topics of discussion when people watching short videos and buy agricultural products. This paper chooses the _Carya cathayensis_, studies the topic hotspots contained in the comments by crawling the relevant comments on TikTok, and proposes a BiLSTM-self-attention hotspot classification model based on LDA feature fusion. The main contributions of this paper are as follows:  
1. Data collection. Taking short videos containing _Carya cathayensis_ as the main research object and TikTok platform as the main carrier, researchers crawled 16,282 comments data and built datasets based on them, enriching the research on the comment data of TikTok platform. 2. A feature fusion method based on the combination of LDA topic model and Word2 Vec word vector is presented. Based on the word vector, this method integrated the topic features of the text, expanded the feature representation of the information of comments, and solved the problem that the sparse feature representation of short text is not obvious to some extent. 3. Construct a hot spot classification model based on BiLSTM-self-attention. Self-attention mechanism and BiLSTM were combined to extract features of comment information. Self-Attention can reduce the dependence of external parameters and make the model focus on the features of the text itself. The combination facilitated the extraction of more important features for classification. 4. Comparative experiment. The LDA-BiLSTM-self-attention model proposed in this paper (hereinafter referred to as LBSA model) was compared with convolutional neural network (CNN), LSTM, gated recurrent unit (GRU), BiLSTM, CNN-LSTM, GRU-CNN, LDA-CNN, LDA-BiLSTM, CNN-self-attention, BiLSTM-self-attention, and other models.