Evaluation of the performance of machine learning techniques for email classification
Abstract: Manual categorization of a mail inbox can often become time-consuming. Therefore many attempts have been made to use machine learning for this task. One essential Natural Language Processing (NLP) task is text classification, which is a big challenge since an NLP engine is not a native speaker of any human language. An NLP engine often fails at understanding sarcasm and underlying intent. One of the NLP challenges is to represent text. Text embeddings can be learned, or they can be generated from a pre-trained model. Google’s pre-trained model Sentence Bidirectional Encoder Representations from Transformers (SBERT) is state-of-the-art for generating pre-trained vector representation of longer text. In this project, different methods of classifying and clustering emails were studied. The performances of three supervised classification models were compared to each other. A Support Vector Machine (SVM) and a Neural Network (NN) were trained with SBERT embeddings, and the third model, a Recurrent Neural Network (RNN) was trained on raw data. The motivation for this experiment was to see whether SBERT embedding is an excellent choice of text representation when combined with simpler classification models in an email classification task. The results show that the SVM and NN perform higher than RNN in the email classification task. Since most real data is unlabeled, this thesis also evaluated how well unsupervised methods could perform in email clustering taking advantage of the available labels and using SBERT embeddings as text representations. Three unsupervised clustering models are reviewed in this thesis: K-Means (KM), Spectral Clustering (SC), and Hierarchical Agglomerative Clustering (HAC). The results show that the unsupervised models all had a similar performance in terms of precision, recall and F1-score, and the performances were evaluated using the available labeled dataset. In conclusion, this thesis gives evidence that in an email classification task, it is better for supervised models to train with pre-trained SBERT embeddings than to train on raw data. This thesis also showed that the output of the clustering methods compared on par with the output of the selected supervised learning techniques.
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