A WeChat Official Account Reading Quantity Prediction Model Based on Text and Image Feature Extraction

作者：Zijian Bai, Shuangyi Ma, Geng Li · 发表于：IEEE Access · 年份：2022 · DOI：10.1109/access.2022.3157715 · 被引用次数：7 · 研究领域：Sentiment Analysis and Opinion Mining、Spam and Phishing Detection、Text and Document Classification Technologies

This paper describes a study that built a neural network prediction model based on feature extraction, focusing on text analysis and image analysis of WeChat official accounts reading quantity. Based on the embedding method of the deep learning model, we extracted the text features in the title and the image features in the cover picture, explored the relationship between these features and the reading quantity, and built a neural network model based on these features to predict the reading quantity. The results show that there is a phenomenon of sentiment fusion in the text, and a sentence vector model based on Doc2Vec and a neural network model both had a good performance. This paper proposes a tool that can predict the reading quantity in advance and help administrators adjust the titles and images according to the predicted results.