entiment analysis in dialogue aims at detecting the sentiment expressed in the utterances of a conversation, which may improve human-computer interaction in natural language. In this paper, we explore different approaches for sentiment analysis in written Portuguese dialogues, mainly related to customer support in Telecommunications. If integrated into a conversational agent, this will enable the automatic identification and a quick reaction upon clients manifesting negative sentiments, possibly with human intervention, hopefully minimising the damage. Experiments were performed in two manually annotated real datasets: one with dialogues from the call-center of a Telecommunications company (TeleComSA); another of Twitter conversations primarily involving accounts of Telecommunications companies. We compare the performance of different machine learning approaches, from traditional to more recent, with and without considering previous utterances. The Finetuned BERT achieved the highest F1 Scores in both datasets, 0.87 in the Twitter dataset, without context, and 0.93 in the TeleComSA, considering context. These are interesting results and suggest that automated customer-support may benefit from sentiment detection. Another interesting finding was that most models did not benefit from using previous utterances, suggesting that, in this scenario, context does not contribute much, and classifying the current utterance can be enough.