Mining consumers’ opinions on the web

Comparing consumer’s opinion concerning own products and those of the competitors to find their strengths and weaknesses is a crucial activity for marketing specialists in the production industry to overcome the requirements of marketing intelligence and product benchmarking. Hence, web-forums, blogs and product review websites provide valuable findings and discussions that record the public voice. Therefore, a huge variety of opinions and commentary about consumer products is woven into the web, which offers a new opportunity for companies to understand and respond to the consumer by analyzing this raw feedback. This paper presents an approach that combines results and understand-ings from several procedures to encounter the challenge of opinion mining. The proposed architecture includes a wide variety of state-of-the-art text mining and natural language processing techniques. Fur-thermore, the key elements of applications for mining large volumes of textual data for marketing intelli-gence are reasoned: a suite of powerful mining and visualization technologies and an interactive analy-sis environment that allows for rapid generation and testing of hypothesis. The concluding results show that recent technologies look promising, but are still far away from a semantically correct text under-standing. Furthermore, this paper presents the results of a proof-of-concept of Text Mining with SPSS software. It is argued that SPSS Text Mining cannot meet the requirements to perform opinion mining as request by market research in the moment.