Research on Text Classification Based on Word Vector Models

Text classification plays a crucial role in data collection and information extraction. The advent of word vector models has ushered in a new era for text classification, enabling the avoidance of training models from scratch and accelerating both algorithm research and data collection processes. This paper analyzes the current state of mainstream word vector models used in short text classification by examining traditional word vector models alongside pre-trained ones, based on recent developments in this field. Firstly, we provide a brief overview of the evolution of word vector models. Secondly, we discuss the specific applications of these models within short text classification, highlighting their advantages and disadvantages. Additionally, we outline potential advancements in short text classification following the post-word vector era. Finally, this paper concludes with a summary that explores the limitations currently faced by word vector models in short text classification as well as future directions for development.