Document Clustering for Event Identification and Trend Analysis in Market News

In this paper we have proposed a stock market analysis system that analyzes financial news items to identify and characterize major events that impact the market. The events have been identified using Latent Dirichlet Allocation(LDA) based topic extraction mechanism. The topic-document data is then clustered using kernel k means algorithm. The clusters are analyzed jointly with the SENSEX raw data to extract major events and their effects. The system has been implemented on capital market news about the Indian share market of the past three years.