Date of Award
Master of Science (MS)
Electrical and Computer Engineering
Williams, Andrew B.
Povinelli, Richard J.
Socially interactive robots, especially those designed for entertainment and companionship, must be able to hold conversations with users that feel natural and engaging for humans. Two important components of such conversations include adherence to the topic of conversation and inclusion of affective expressions. Most previous approaches have concentrated on topic detection or sentiment analysis alone, and approaches that attempt to address both are limited by domain and by type of reply. This thesis presents a new approach, implemented on a humanoid robot interface, that detects the topic and sentiment of a user’s utterances from text-transcribed speech. It also generates domain-independent, topically relevant verbal replies and appropriate positive and negative emotional expressions in real time. The front end of the system is a smartphone app that functions as the robot’s face. It displays emotionally expressive eyes, transcribes verbal input as text, and synthesizes spoken replies. The back end of the system is implemented on the robot’s onboard computer. It connects with the app via Bluetooth, receives and processes the transcribed input, and returns verbal replies and sentiment scores. The back end consists of a topic-detection subsystem and a sentiment-analysis subsystem. The topic-detection subsystem uses a Latent Semantic Indexing model of a conversation corpus, followed by a search in the online database ConceptNet 5, in order to generate a topically relevant reply. The sentiment-analysis subsystem disambiguates the input words, obtains their sentiment scores from SentiWordNet, and returns the averaged sum of the scores as the overall sentiment score. The system was hypothesized to engage users more with both subsystems working together than either subsystem alone, and each subsystem alone was hypothesized to engage users more than a random control. In computational evaluations, each subsystem performed weakly but positively. In user evaluations, users reported a higher level of topical relevance and emotional appropriateness in conversations in which the subsystems were working together, and they reported higher engagement especially in conversations in which the topic-detection system was working. It is concluded that the system partially fulfills its goals, and suggestions for future work are presented.