Sentiment prediction for comments of web news articles

Urška Kosec (2014) Sentiment prediction for comments of web news articles. EngD thesis.

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Abstract

The Thesis dealt with machine learning-based classification of the sentimental impact of the comments posted with news articles on the web. In the past years sentiment analysis has become an important research topics with substantial number of publications for texts in English, while for the Slovene, except in the recent thesis at the University of Ljubljana, Faculty of Computer and Information science, the topic has not been explored well. In relation to all the features of the Slovenian language this represented an additional challenge. Our goal was to correctly classify these comments as positive or negative. We examined how this problem differs from the topical classification of texts. Our work shows that the problem is hard and that a typical application of machine learning based on k-mer representation of text does not yield the expected results. A possible reason for poor performance may be lack of semantic information in such representation and short length of the texts.

Item Type:

Thesis (EngD thesis)

Keywords:

sentiment prediction, opinion mining, data mining, machine learning, k-mer, classification methods, logloss, accuracy score, logistic regression, support vector machines, k-nearest neighbours, random forests, stacking

Number of Pages:

Language of Content:

Slovenian

Mentor / Comentors:

Name and Surname	ID	Function
prof. dr. Blaž Zupan	106	Mentor

Link to COBISS:

http://www.cobiss.si/scripts/cobiss?command=search&base=50070&select=(ID=00010634324)

Institution:

University of Ljubljana

Department:

Faculty of Computer and Information Science

Item ID:

2568

Date Deposited:

06 Jun 2014 12:54

Last Modified:

19 Jun 2014 10:49

URI:

http://eprints.fri.uni-lj.si/id/eprint/2568

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