A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
SVM-based sentiment classification: a comparative study against state-of-the-art classifiers
2017
International Journal of Computational Intelligence Studies
Transforming the unstructured textual information contained in various social media streams into useful business knowledge is an extremely difficult computational task, mainly, due to the underlying hard pattern classification problem of sentiment analysis, especially within the context of the Greek language. In this paper, we address the pattern classification problem of sentiment analysis through the utilisation of support vector machines (SVMs). In particular, we conducted an extensive
doi:10.1504/ijcistudies.2017.086063
fatcat:e5kntbcq4jgfpee6bkd7myvkvm