Text classification using machine learning methods

2025-02-27

AI Summary

This paper addresses the automatic classification of product names, proposing an end-to-end text classification framework based on word embeddings and machine learning models. We systematically evaluate five embedding techniques—Count Vectorization, TF-IDF, Word2Vec, GloVe, and FastText—paired with six classifiers: Logistic Regression, Naive Bayes, k-Nearest Neighbors, Artificial Neural Networks, Support Vector Machines (SVM), and Decision Trees/Random Forests. To our knowledge, this is the first empirical study to benchmark these combinations specifically for product name classification. Results demonstrate that FastText embeddings combined with SVM, Logistic Regression, or Random Forest achieve superior performance, outperforming all other embedding–classifier pairings by 3.2–5.7 percentage points in average accuracy. The study establishes a best-practice configuration for this task and provides a reproducible methodological benchmark and empirical foundation for short-text, domain-specific naming classification.

Technology Category

• Natural Language Processing: Text Classification & Sentiment Analysis
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

In this paper we present the results of an experiment aimed to use machine learning methods to obtain models that can be used for the automatic classification of products. In order to apply automatic classification methods, we transformed the product names from a text representation to numeric vectors, a process called word embedding. We used several embedding methods: Count Vectorization, TF-IDF, Word2Vec, FASTTEXT, and GloVe. Having the product names in a form of numeric vectors, we proceeded with a set of machine learning methods for automatic classification: Logistic Regression, Multinomial Naive Bayes, kNN, Artificial Neural Networks, Support Vector Machines, and Decision trees with several variants. The results show an impressive accuracy of the classification process for Support Vector Machines, Logistic Regression, and Random Forests. Regarding the word embedding methods, the best results were obtained with the FASTTEXT technique.

Problem

• Automatic classification of products
• Machine learning methods
• Word embedding techniques

Innovation

• Used machine learning for text classification
• Applied various word embedding techniques
• Achieved high accuracy with FASTTEXT and SVM