Topic Classification from Text Using Decision Tree, K-NN and Multinomial Naïve Bayes
One of the central motivations behind Natural Language Processing is detecting patterns. Given a text document, the task of identifying the context is known to be as topic classification. This paper explores the performance of three different classifiers namely Decision Tree, K-Nearest Neighbors, and Multinomial Naive Bayes on a topic classification task (with six topic classes). The evaluation is done on the basis of accuracy, precision, recall, and f1-score based results. Among those three aforementioned classifiers, we have selected the Multinomial Naïve Bayes a sour best model using which we achieved 91.8% accuracy.
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Datasets
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Topic Classification | Amazon Product Data | Multinomial Naive Bayes | Answer F1 | 0.918 | # 1 |
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