  The applicability of common sentiment analysis models to open domain human
robot interaction is investigated within this paper. The models are used on a
dataset specific to user interaction with the Alana system (a Alexa prize
system) in order to determine which would be more appropriate for the task of
identifying sentiment when a user interacts with a non-human driven socialbot.
With the identification of a model, various improvements are attempted and
detailed prior to integration into the Alana system. The study showed that a
Random Forest Model with 25 trees trained on the dataset specific to user
interaction with the Alana system combined with the dataset present in NLTK
Vader outperforms other models. The new system (called 'Rob') matches it's
output utterance sentiment with the user's utterance sentiment. This method is
expected to improve user experience because it builds upon the overall
sentiment detection which makes it seem that new system sympathises with user
feelings. Furthermore, the results obtained from the user feedback confirms our
expectation.
