Multimodal sentiment analysis has become a focus of research in recent years. However, most studies of multimodal sentiment analysis have considered only signals that are observable by humans, such as linguistic, audio and visual information, whereas the contribution of the multimodal fusion of such signals with unobservable signals, i.e., physiological signals, has not been comprehensively explored. In this study, we aim to investigate effects of physiological signals in multimodal sentiment analysis by evaluating all of the fusion models for different types of sentiment estimation in naturalistic human-agent interaction settings. Our results suggest that physiological features are effective in the unimodal model and that the fusion of linguistic representations with physiological features provides the best results for estimating self-sentiment labels as annotated by the users themselves. In contrast, the tensor fusion of linguistic representations with audiovisual features is effective for estimating sentiment labels as annotated by a third party in regression tasks, which can be derived from the corresponding signals that are observable by humans. A detailed analysis of the self-sentiment estimation results suggests that different modalities play different roles in sentiment estimation, and corresponding implications are discussed.