Machine Learning Techniques for Topic Spotting

Nadia Shakir, Erum Iftikhar, Imran Sarwar Bajwa


Automatically choosing topics for text documents that describe the document contents, is a useful technique for text categorization. For example queries sent on the web can use this technique to identify the query topic and accordingly forward query to small group of people. Similarly online blogs can be categorized according to the topics they are related to. In this paper we applied machine learning techniques to the problem of topic spotting. We used supervised learning techniques which are highly dependent on training data and the particular training algorithm used. Our approach differs from automatic text clustering which uses unsupervised learning for clustering the text. Secondly the topics are known in advance and come from an exhaustive list of words. The machine learning techniques we applied are 1) neural network., 2) Naïve Bayes Classifier, 3) Instance based learning using k-nearest neighbours and 4) Decision Tree method. We used Reuters-21578 text categorization dataset for our experiments.


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Paper Citation

in Harvard Style

Shakir N., Iftikhar E. and Bajwa I. (2014). Machine Learning Techniques for Topic Spotting . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-027-7, pages 450-455. DOI: 10.5220/0004881604500455

in Bibtex Style

author={Nadia Shakir and Erum Iftikhar and Imran Sarwar Bajwa},
title={Machine Learning Techniques for Topic Spotting},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Machine Learning Techniques for Topic Spotting
SN - 978-989-758-027-7
AU - Shakir N.
AU - Iftikhar E.
AU - Bajwa I.
PY - 2014
SP - 450
EP - 455
DO - 10.5220/0004881604500455