Methods for Clustering Text Messages from Service Users

Student: Kondrashkin Denis

Educational Programme: Data Science (Master)

Year of Graduation: 2019

The thesis is about clustering problem of service users messages which key property is short length and vocabulary specificity. The thesis covers such classical methods as TD-IDF and LDA, but also considers modern neural network methods for text representation. Following steps were completed: selection of clustering algorithm, text similarity metric, comparative analysis of cluster quality for the variety of text representation models. As a results final clustering quality significantly outperforms basic standard methods. Also, several directions of presented approach improvement were proposed.

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