Understanding customers using Facebook Pages: Data mining users feedback using text analysis

Unlike large companies, start-up companies usually do not have the available resources to afford traditional mass marketing campaigns such as TV commercials or magazine advertisements. However, social networking services (e.g, Facebook, Twitter, Weibo, etc.) provide a more economically more viable opportunity for these new companies to directly communicate with their potential customers. Social networks are significant marketing communication tools for established, and in particular, start-up companies. Some FB Pages contain hundreds of responses and receive good opinions from the readers whereas some Pages do not. By interpreting these Pages, it is possible to generate the key factors that attract customers and for the young entrepreneurs to react to new postings. Text mining the Pages helps to better understand and manage their Pages and build a closer relationship with the target audience. This research proposes an analytical process to interpret the dialogue between young entrepreneurs and their audience of Facebook Pages. First, collect consumer feedback from social networks, like FB. The interpretation of the dialogues into meaningful statistics, especially when attempting to model, cluster, and analyze the critical elements of posted Internet content, requires new text analysis techniques and methodologies. CKIP (Chinese Knowledge and Information Processing) is applied to extract the key phrases from the Chinese language dialogues. Then clustering is used to generate the critical points that customers care about and then to explore key factors that attracts customers and resolves their needs. Therefore, entrepreneurs better understand how to post an interesting topic to strengthen their marketing communications and increase their market share.