Opinion mining and relationship discovery using CopeOpi opinion analysis system

We present CopeOpi, an opinion‐analysis system, which extracts from the Web opinions about specific targets, summarizes the polarity and strength of these opinions, and tracks opinion variations over time. Objects that yield similar opinion tendencies over a certain time period may be correlated due to the latent causal events. CopeOpi discovers relationships among objects based on their opinion‐tracking plots and collocations. Event bursts are detected from the tracking plots, and the strength of opinion relationships is determined by the coverage of these plots. To evaluate opinion mining, we use the NTCIR corpus annotated with opinion information at sentence and document levels. CopeOpi achieves sentence‐ and document‐level f‐measures of 62% and 74%. For relationship discovery, we collected 1.3M economics‐related documents from 93 Web sources over 22 months, and analyzed collocation‐based, opinion‐based, and hybrid models. We consider as correlated company pairs that demonstrate similar stock‐price variations, and selected these as the gold standard for evaluation. Results show that opinion‐based and collocation‐based models complement each other, and that integrated models perform the best. The top 25, 50, and 100 pairs discovered achieve precision rates of 1, 0.92, and 0.79, respectively.