Mining online community data: The nature of ideas in online communities

Our study is based on two datasets from dialogue between members of two distinct online communities. The first community is related to beer. The second is related to Lego. We generate machine learning classifiers based on Support Vector Machines and Partial Least Squares that can detect ideas from each respective online community. We use partial least squares to investigate what are the words and expressions that allows for automatic classification of ideas. We conclude that ideas from the two online communities, contains suggestion/solution words and expressions and it is these that make automatic idea detection possible. In addition we conclude that the nature of the ideas in the beer community seems to be related to the brewing process. The nature of the ideas in the Lego community seems to be related to new products that consumers would want.