Comparison of document vectorization methods
a case study with textual data
Palabras clave:
Text Mining, Doc2Vec, TF-IDF, Classificaton MethodsResumen
The explosion of digital information in recent decades has brought a massive volume of text data. The interest in extracting knowledge from this vast amount of data gave rise to Text Mining. One of the challenges in this field is to transform a text corpus into a numerical database. This process, called document vectorization, is crucial for automating information extraction. The goal of this work is to compare the performance of four document vectorization methods when used for classification purposes. The compared vectorization methods were Bag of Words (BoW), TF-IDF, and two different architectures of doc2vec, CBOW and skip-gram. The classification methods applied were Logistic Regression, Decision Tree, Random Forest, XGBoost, and Perceptron. The dataset used was the publicly available Women's E-Commerce Clothing Reviews dataset, which consists of 10 attributes, with three of them considered in this work: the item review text, the review title, and a categorical variable indicating whether the customer recommends the product or not. A balanced random sample of 8,000 documents was selected, with 4,000 documents having positive recommendations and 4,000 with negative recommendations. This dataset was split into training (70\%) and testing (30\%) sets. The performance comparison metric was the area under the ROC curve (AUC). When comparing the document vectorization methods, both architectures of doc2vec outperformed the other vectorization methods across all tested classification methods.
Citas
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