Online financial textual information containing a large amount of investor sentiment is growing rapidly and an effective solution to automate the sentiment classification of such large amounts of text would be extremely beneficial. A novel approach to sentiment classification is the application of multi-objective optimization combined with v-SVM to improve the overall accuracy and hence we present a Multi-Objective Genetic Algorithm (MOGA) based approach to automatically adjust the free parameters of a v-SVM classifier to optimise sentiment classification performance. The approach is implemented and tested using two online financial textual datasets and experimental results show that the overall classification accuracy has improved (4%-7%) compared with other baseline approaches.
|Title of host publication||Unknown Host Publication|
|Number of pages||7|
|Publication status||Published - 6 Dec 2015|
|Event||2015 IEEE Symposium Series on Computational Intelligence - Cape Town|
Duration: 6 Dec 2015 → …
|Conference||2015 IEEE Symposium Series on Computational Intelligence|
|Period||6/12/15 → …|