Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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  present a Multi-Objective Genetic Algorithm (MOGA) based approach to automatically adjust the free parameters of a &#x220A;-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.
['Fan Sun', 'Ammar Belatreche', 'Sonya A. Coleman', 'Tm McGinnity', 'Yuhua Li']
I am writing a research paper on a idea Sentiment Classification in the Financial Domain Using? SVM and Multi-Objective Optimisation
[{"from": "human", "value": "I am writing a research paper on a idea Sentiment Classification in the Financial Domain Using? SVM and Multi-Objective Optimisation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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  present a Multi-Objective Genetic Algorithm (MOGA) based approach to automatically adjust the free parameters of a &#x220A;-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."}]