<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Despite the rapid growth of other types of social media, Internet discussion forums remain a highly popular communication channel and a useful source of text data for analyzing user interests and sentiments. Being suited to richer, deeper, and longer discussions than microblogging services, they particularly well reflect topics of long-term, persisting involvement and areas of specialized knowledge or experience. Discovering and characterizing such topics and areas by text mining algorithms is therefore an interesting and useful research direction. This work presents a case study in which selected classification algorithms are applied to posts from a Polish discussion forum devoted to psychoactive substances received from home-grown plants, such as hashish or marijuana. The utility of two different vector text representations is examined: the simple bag of words representation and the more refined embedded global vectors one. While the former is found to work well for the multinomial naive Bayes algorithm, the latter turns out more useful for other classification algorithms: logistic regression, SVMs, and random forests. The obtained results suggest that post-classification can be applied for measuring publication intensity of particular topics and, in the case of forums related to psychoactive substances, for monitoring the risk of drug-related crime.</p></abstract>

Despite the rapid growth of other types of social media, Internet discussion forums remain a highly popular communication channel and a useful source of text data for analyzing user interests and sentiments. Being suited to richer, deeper, and longer discussions than microblogging services, they particularly well reflect topics of long-term, persisting involvement and areas of specialized knowledge or experience. Discovering and characterizing such topics and areas by text mining algorithms is therefore an interesting and useful research direction. This work presents a case study in which selected classification algorithms are applied to posts from a Polish discussion forum devoted to psychoactive substances received from home-grown plants, such as hashish or marijuana. The utility of two different vector text representations is examined: the simple bag of words representation and the more refined embedded global vectors one. While the former is found to work well for the multinomial naive Bayes algorithm, the latter turns out more useful for other classification algorithms: logistic regression, SVMs, and random forests. The obtained results suggest that post-classification can be applied for measuring publication intensity of particular topics and, in the case of forums related to psychoactive substances, for monitoring the risk of drug-related crime.

A Case Study in Text Mining of Discussion Forum Posts: Classification with Bag of Words and Global Vectors

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Despite the rapid growth of other types of social media, Internet discussion forums remain a highly popular communication channel and a useful source of text...

A Case Study in Text Mining of Discussion Forum Posts: Classification with Bag of Words and Global Vectors

Publié en ligne: 11 janv. 2019

Pages: 787 - 801

Reçu: 22 sept. 2017

Accepté: 02 juin 2018

DOI: https://doi.org/10.2478/amcs-2018-0060

Mots clés
text mining, discussion forums, text representation, document classification, word embedding

© 2019 Paweł Cichosz, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A Case Study in Text Mining of Discussion Forum Posts: Classification with Bag of Words and Global Vectors

Publié en ligne: 11 janv. 2019

Pages: 787 - 801

Reçu: 22 sept. 2017

Accepté: 02 juin 2018

DOI: https://doi.org/10.2478/amcs-2018-0060

Mots cléstext mining, discussion forums, text representation, document classification, word embedding

© 2019 Paweł Cichosz, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
text mining, discussion forums, text representation, document classification, word embedding