Weblogs for market research: finding more relevant opinion documents using system fusion

Purpose – The purpose of this paper is to examine the usefulness of fusion as a means of improving the precision of automated opinion detection. Design/methodology/approach – Five system fusion methods are proposed and tested using runs submitted by the Text REtrieval Conference (TREC) Blog06 participants as input. The methods include a voting method, an inverse rank method (IRM), a linear‐normalised score method and two weighted methods that use a weighted IRM score to rank the document. Findings – Mean average precision (MAP) is used as an indicator of the performance of the runs in this study. The best system fusion method achieves a 55.5 percent higher MAP result compared with the highest MAP result of any individual run submitted by the Blog06 participants. This equates to an increase in detection of 2,398 relevant opinion documents (21 percent). Practical implications – System fusion can be used to improve upon the results achieved by existing individual opinion detection systems. On the other hand, multiple opinion detection approaches can be combined into one system and fusion used to combine the results to build in diversity. Diversity within fusion inputs can increase the improvements achieved by fusion methods. The improved output from a diverse opinion detection system will then contain a higher number of relevant documents and reduce the incidence of high‐ranking non‐relevant documents and low‐ranking relevant documents. Originality/value – The fusion methods proposed in this study demonstrate that simple fusion of opinion detection systems can improve performance.