This paper explores the hypothesis that sentiment in text is closely related to emotions in speech in terms of features needed for successful detection. We use a Croatian emotional speech corpus (CrES) and a Croatian social network textual sentiment corpus SentHR. We first perform emotional state estimation based on acoustic speech features using support vector machines in the first case and random forest in second. Accuracy between 60% and 70% was achieved for five discrete emotion classification task. Subsequently, we trained a positive naive Bayes classifier for textual sentiment, reporting an accuracy of around 70% (with a pronounced bias towards the complement). Finally, we used the trained sentiment classifier for two classification experiments on the transcripts of the CrES dataset for classifying anger and sadness. Across several iterations, the results showed that accuracy on the transcripts was around 50% for both sadness and anger, reporting a slightly higher (albeit consistently higher) accuracy on emotional state "anger".