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In the last several years, Twitter is being adopted by the companies as analternative platform to interact with the customers to address their concerns.With the abundance of such unconventional conversation resources, push fordeveloping effective virtual agents is more than ever. To address thischallenge, a better understanding of such customer service conversations isrequired. Lately, there have been several works proposing a novel taxonomy forfine-grained dialogue acts as well as develop algorithms for automaticdetection of these acts. The outcomes of these works are providing steppingstones for the ultimate goal of building efficient and effective virtualagents. But none of these works consider handling the notion of negation intothe proposed algorithms. In this work, we developed an SVM-based dialogue actsprediction algorithm for Twitter customer service conversations where negationhandling is an integral part of the end-to-end solution. For negation handling,we propose several efficient heuristics as well as adopt recent state-of- artthird party machine learning based solutions. Empirically we show model'sperformance gain while handling negation compared to when we don't. Ourexperiments show that for the informal text such as tweets, the heuristic-basedapproach is more effective.

Don't get Lost in Negation: An Effective Negation Handled Dialogue Acts Prediction Algorithm for Twitter Customer Service Conversations