We can feel free to post the information such as personal events using Twitter one of the popular micro-blogging service. However, the collection of information is limited by the human power only, therefore, the method of collecting trends automatically is important. Existing web services focus on the number of tweets for getting trends. However, a time lag was occurred for extracting the trends. In this paper, we propose the trend extraction system for twitter in real time by paying attention to the co-occurrence patterns. Our system can learn the new key patterns at the same time not only using the picked up trend biterms, previously. Furthermore, we evaluate the efficiency of the proposed method of extracting the trends from twitter by the comparative experiments. We demonstrate that our proposed method can extract accurately and widely without time-lags compared with the existing service (Realtime Yahoo Search).

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Existing research in the field of automated negotiation considers a negotiation architecture in which some of the negotiation components are designed separately by reinforcement learning (RL), but comprehensive negotiation strategy design has not been achieved. In this study, we formulated an RL model based on a Markov decision process (MDP) for bilateral multi-issue negotiations. We propose a versatile negotiating agent that can effectively learn various negotiation strategies and domains through comprehensive strategies using deep RL. We show that the proposed method can achieve the same or better utility than existing negotiation agents.

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