  In the last several years, Twitter is being adopted by the companies as an
alternative platform to interact with the customers to address their concerns.
With the abundance of such unconventional conversation resources, push for
developing effective virtual agents is more than ever. To address this
challenge, a better understanding of such customer service conversations is
required. Lately, there have been several works proposing a novel taxonomy for
fine-grained dialogue acts as well as develop algorithms for automatic
detection of these acts. The outcomes of these works are providing stepping
stones for the ultimate goal of building efficient and effective virtual
agents. But none of these works consider handling the notion of negation into
the proposed algorithms. In this work, we developed an SVM-based dialogue acts
prediction algorithm for Twitter customer service conversations where negation
handling 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- art
third party machine learning based solutions. Empirically we show model's
performance gain while handling negation compared to when we don't. Our
experiments show that for the informal text such as tweets, the heuristic-based
approach is more effective.
