Showing items 1 - 2 of 2

Your selections:

Using psycholinguistic features for profiling first language of authors

- Torney, Rosemary, Vamplew, Peter, Yearwood, John

  • Authors: Torney, Rosemary , Vamplew, Peter , Yearwood, John
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Journal of the American Society for Information Science and Technology Vol. 63, no. 6 (2012), p. 1256-1269
  • Full Text: false
  • Reviewed:
  • Description: This study empirically evaluates the effectiveness of different feature types for the classification of the first language of an author. In particular, it examines the utility of psycholinguistic features, extracted by the Linguistic Inquiry and Word Count (LIWC) tool, that have not previously been applied to the task of author profiling. As LIWC is a tool that has been developed in the psycholinguistic field rather than the computational linguistics field, it was hypothesized that it would be effective, both as a single type feature set because of its psycholinguistic basis, and in combination with other feature sets, because it should be sufficiently different to add insight rather than redundancy. It was found that LIWC features were competitive with previously used feature types in identifying the first language of an author, and that combined feature sets including LIWC features consistently showed better accuracy rates and average F measures than were achieved by the same feature sets without the LIWC features. As a secondary issue, this study also examined how effectively first language classification scaled up to a larger number of possible languages. It was found that the classification scheme scaled up effectively to the entire 16 language collection from the International Corpus of Learner English, when compared with results achieved on just 5 languages in previous research. 2012 ASIS&T.

Cyberbullying detection on social networks using machine learning approaches

- Islam, Md Manowarul, Uddin, Md Ashraf, Islam, Linta, Akter, Arnisha, Sharmin, Selina, Acharjee, Uzzal

  • Authors: Islam, Md Manowarul , Uddin, Md Ashraf , Islam, Linta , Akter, Arnisha , Sharmin, Selina , Acharjee, Uzzal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
  • Full Text: false
  • Reviewed:
  • Description: The use of social media has grown exponentially over time with the growth of the Internet and has become the most influential networking platform in the 21st century. However, the enhancement of social connectivity often creates negative impacts on society that contribute to a couple of bad phenomena such as online abuse, harassment cyberbullying, cybercrime and online trolling. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and even sometimes force them to attempt suicide. Online harassment attracts attention due to its strong negative social impact. Many incidents have recently occurred worldwide due to online harassment, such as sharing private chats, rumours, and sexual remarks. Therefore, the identification of bullying text or message on social media has gained a growing amount of attention among researchers. The purpose of this research is to design and develop an effective technique to detect online abusive and bullying messages by merging natural language processing and machine learning. Two distinct freatures, namely Bag-of Words (BoW) and term frequency-inverse text frequency (TFIDF), are used to analyse the accuracy level of four distinct machine learning algorithms. © 2020 IEEE.
  • 1