The paper explores the use of natural language processing (NLP) and neural networks for automating the tagging of articles. With the rise of AI technologies, particularly in NLP, the paper discusses how these technologies can enhance efficiency by classifying and tagging articles. The system developed leverages neural networks with LSTM layers to automatically tag articles, offering a complete cycle from data acquisition to tag storage. This can be particularly useful in various domains, including marketing, technology trend analysis, and data categorization. The application described in the paper focuses on processing articles related to IT industry trends, providing valuable insights for project teams when selecting technology stacks.
Takeaways:
- NLP in AI Development: The paper emphasizes the growing role of natural language processing (NLP) in artificial intelligence, focusing on its ability to classify and extract valuable data from textual information, which is central to many modern applications.
- Auto-Tagging Using Neural Networks: The system designed in the study uses a multilayered neural network, specifically one with Long Short-Term Memory (LSTM) layers, to automatically tag articles. This method is efficient and improves over traditional, manual tagging processes.
- Efficiency and Time-Saving: The automated process developed reduces the time required for data classification, enhancing productivity and enabling faster analysis and decision-making.
- Application in IT Industry: The system’s focus on analyzing articles from popular web forums related to IT trends makes it especially useful for predicting technology stack choices and understanding current industry trends.
- Practical Implications: This auto-tagging system can be adapted for a wide range of industries beyond IT, such as marketing, media, and content management, where automated content categorization can streamline workflows and improve accuracy.
