Recognition of stance strength and polarity in spontaneous speech

From activities as simple as scheduling a meeting to those as complex as balancing a national budget, people take stances in negotiations and decision making. While the related areas of subjectivity and sentiment analysis have received significant attention, work has focused almost exclusively on text, and much stance-taking activity is carried out verbally. This paper investigates automatic recognition of stance-taking in spontaneous speech. It first presents a new annotated corpus of spontaneous, conversational speech designed to elicit high densities of stance-taking at different strengths. Speaker spurts are annotated both for strength of stance-taking behavior and polarity of stance. Based on this annotated corpus, we develop classifiers for automatic recognition of stance-taking behavior in speech. We employ a range of lexical, speaking style, and prosodic features in a boosting framework. The classifiers achieve strong accuracies on both binary detection of stance and four-way recognition of stance strength, well above most common class assignment. Finally, we classify the polarity of stance-taking spurts, obtaining accuracies around 80%. The best classifiers rely primarily on word unigram features, with speaking style and prosodic features yielding lower accuracies but still well above common class assignment.