Although many game-theoretic models replicate real interactions that often rely on natural language, explicit study of games where language is central to strategic interaction remains limited. This paper introduces the \emph{conversation game}, a multi-stage, extensive-form game based on linguistic strategic interaction. We focus on a subset of the games, called verdict games. In a verdict game, two players alternate to contribute to a conversation, which is evaluated at each stage by a non-strategic judge who may render a conclusive binary verdict, or a decision to continue the dialogue. The game ends once a limit is reached or a verdict is given. We show many familiar processes, such as interrogation or a court process fall under this category. We also, show that the Turing test is an instance of verdict game, and discuss the significance of a strategic view of the Turing test in the age of advanced AI deception. We show the practical relevance of the proposed concepts by simulation experiments, and show that a strategic agent outperforms a naive agent by a high margin.

"Sentiment" is a rather intriguing concept. It can mean attitude, feeling, bias, view, thought, and even something as deeply felt as emotion. Sentiment analysis that utilizes AI and machine learning has become a powerful tool for companies to understand how their customers and/or potential customers feel about their company. It can also be used for competitive analysis to take the temperature of a rival's products or services. "When you can't convince them with intellect, persuade them with sentiment," is author Amit Kalantri's recommendation.