Model advertising slogan selection system using collective wisdom on the web

Increased demand for web advertising has resulted in a corresponding increase in the need to develop personalized advertisements targeted at individuals online. We propose an automated advertising slogan selection system that can satisfy this requirement. Many customer reviews and comments are available publicly on online shopping sites. The proposed system uses content mining to extract favorable reports from the web and arranges the data into a specific knowledge representation structure to improve the advantage of the target product. For a particular business, the proposed system first extracts tuples, composed of elements that express the knowledge representation from each user-written review. Then, these tuples are selected using a frequency-based approach and emotion corpus. Subsequently, for each tuple, advertising slogans are chosen from the advertising slogan corpora using a neural network. For verification, we used data from an electronic commerce website for hotels to evaluate two aspects of our system (namely, quality of selected tuples and advertising slogans). The results of the experiments confirm that the proposed system can extract suitable tuples when the given data are sufficient. It can also retrieve slogans even when their meanings are convoluted.