Emotion is a complex behavioral phenomenon, which is expressed and perceived through various modalities, such as language, vocal and facial expressions. Psychiatric research has suggested that the lack of emotional alignment between modalities is a symptom of emotion disorders. In this work, we quantify the mismatch between emotion expressed through language and acoustics, which we refer to as Emotional MisMatch (EMM), as an intermediate step for mood identification. We use a longitudinal dataset collected from people with Bipolar Disorder (BP) and show that symptomatic mood episodes show significantly more EMM, compared to euthymic moods. We propose a fully automatic mood identification pipeline with automatic speech transcription, emotion recognition, and EMM feature extraction. We find that EMM features, although smaller in size, outperform a language-based baseline, and consistently provide improvement when combined with language and/or raw emotion features on mood classification.