Dialogue Acts have been well studied in linguistics and attracted computational linguistics research for a long time: they constitute the basis of everyday conversations and can be identified with the communicative goal of a given utterance (e.g. asking for information, stating facts, expressing opinions, agreeing or disagreeing). Even if not constituting any deep understanding of the dialogue, automatic dialogue act labeling is a task that can be relevant for a wide range of applications in both human-computer and human-human interaction. We present a qualitative analysis of the lexicon of Dialogue Acts: we explore the relationship between the communicative goal of an utterance and its affective content as well as the salience of specific word classes for each speech act. The experiments described in this paper fit in the scope of a research study whose long-term goal is to build an unsupervised classifier that simply exploits the lexical semantics of utterances for automatically annotate dialogues with the proper speech acts.