# Research on the prediction of English topic richness in the context of multimedia data

## TL;DR

This paper presents a new algorithm using the Transformer model to predict English topic richness on Twitter, achieving 82.3% accuracy.

## Contribution

A novel feature fusion approach with the Transformer model for predicting topic richness in multimedia data on Twitter.

## Key findings

- The algorithm achieves 82.3% accuracy in predicting topic richness on Twitter.
- Feature fusion of blog, topic, and user features improves model performance.
- The Transformer model effectively handles multimodal data for topic richness prediction.

## Abstract

With the evolution of the Internet and multimedia technologies, delving deep into multimedia data for predicting topic richness holds significant practical implications in public opinion monitoring and data discourse power competition. This study introduces an algorithm for predicting English topic richness based on the Transformer model, applied specifically to the Twitter platform. Initially, relevant data is organized and extracted following an analysis of Twitter’s characteristics. Subsequently, a feature fusion approach is employed to mine, extract, and construct features from Twitter blogs and users, encompassing blog features, topic features, and user features, which are amalgamated into multimodal features. Lastly, the combined features undergo training and learning using the Transformer model. Through experimentation on the Twitter topic richness dataset, our algorithm achieves an accuracy of 82.3%, affirming the efficacy and superior performance of the proposed approach.