用机器学习自动分类商品，FASTTEXT+SVM表现最佳
Text classification using machine learning methods
- 将商品名转为向量，对比多种嵌入与分类方法
- SVM、逻辑回归和随机森林准确率突出，最高达98%
- FASTTEXT在词嵌入中表现最优，适合商品分类场景
本文通过实验探索使用机器学习方法实现商品自动分类。为应用自动分类，将商品名称从文本转换为数值向量（即词嵌入），采用的方法包括：词袋模型、TF-IDF、Word2Vec、FASTTEXT 和 GloVe。在获得数值向量后，应用多种机器学习算法进行分类：逻辑回归、多项式朴素贝叶斯、kNN、人工神经网络、支持向量机及决策树及其变体。结果表明，支持向量机、逻辑回归和随机森林分类效果优异，准确率表现突出。在词嵌入方法中，FASTTEXT取得最佳效果。
原文摘要 · Abstract (English)
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.
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