This research describes improvements to an agent called the Intelligent Dialogue Agent (IDA) which aims to be a friendly intermediary between older adults and a preventive care system through performing natural dialogic interactions. The preventive care system consists of a fall prevention component, a cognitive training component, and the IDA, which promotes these ends by creating an environment in which those users (older adults) can participate in their own preventive care while having fun. To achieve this, the IDA uses reinforcement learning methods to learn the appropriate policy and provide speech topics depending on the preferences of the specific user, thereby motivating him or her to use the preventive care system on a daily basis. We extend the state-action definition of the IDA by introducing the results of sentiment analysis to improve its learning performance and make the IDA possible to always output fresh topics based on distributed text representation. Finally, we conduct several experiments with older adults to evaluate whether the IDA successfully captures their specific preferences, and to assess basic characteristics of the updated IDA incorporating sentiment analysis and distributed text representation.