A survey on online Stock forum using subspace clustering

Financial stock Data Analysis and future prediction in terms of Sentiments is great challenge in the big data research. Among the unlabelled opinion, opinion classification in terms of unsupervised learning algorithm will lead to classification error as data is sparse and high dimensional. To overcome this problem, the sentiment analysis to extract the opinion of each word in the stock data has been proposed. Moreover the data size is large, hence the singular value decomposition to resolve the inconsistent constraints correlating to the large dimensions, and dimensionally reduced feature set is been used. The dimensionally reduced feature set is classified into clusters through employment of Principle component analysis with utilization of the domain knowledge. Cluster data which further inconsistent with the outlier probability can further reduced through subspace clustering. Experimental results prove that the proposed framework outperforms the state of art approaches in terms of precision, recall and Fmeasure.