Using Latent Semantic Analysis of Email to Detect Change in Social Groups
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چکیده
Email text data is a rich resource that, when properly used, may enhance warning of economically material events in commercial enterprises. Armed with the temporal text classification of an email time stamp on a large data set, we combine latent semantic analysis with statistical process control, to detect potential change in sentiment. A novel approach to identifying the causes of change is proposed by averaging concept scores across statistically significant time periods and then performing an inverse singular valued decomposition. The resulting term by significant time period matrix, is used to explore potential causes of change and are compared to historical events. Our findings suggest that causes for significant change in semantic intent can be identified in some circumstances. On a dataset of 50,000 unique emails from Enron, we demonstrate this novel approach to investigate semantic change in email text data.
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تاریخ انتشار 2008