Grant: Applied Research and Development Grants - Level 1

Root-cause analysis of user-generated content in social media helps to answer why customers dislike a productor service. However, identifying customer intent after the root-cause analysis is a much more valuable piece ofinformation in terms of marketing and customer service. This information allows companies to adapt theirproducts and policies to customer intentions. Kaypok Inc., the industry partner for this proposed appliedresearch project with Sheridan College, currently has a product that offers a variety of innovative solutions forroot-cause analysis.Through this proposed Engage grant, Kaypok aims to develop an add-on service that identifies customer intentvis-à-vis its product in user-generated content. The Sheridan research team proposes to develop the add-onservice by designing, implementing and testing an algorithm to identify customer intent in user-generatedcontent. Natural language processing will be used to predict customers' behaviour based on analyzing theiruser-generated content in social media. For example, the message: ""Hi, my smartphone is broken, guess I needa new one"" indicates an intent to buy. The algorithm targets various intents and determines the fingerprints(signs) of each one. The applied research to be undertaken during this project will add useful features toKaypok's product that will help the company to attract new customers. In addition, it will engage students inaddressing real-world problems, which will increase their potential for contributing to future innovation.