Sentiment Analysis for Open Domain Conversational Agent

Sentiment analysis analysis models to open domain human continues to be highly challenging with the research robot interaction is investigated within this community attempting many sub-problems paper. The models are used on a dataset that have not been completely solved (Pozzi et al., specific to user interaction with the Alana 2017b). With this in mind, it is expected that system (a Alexa prize system) in order scripted conversations between two humans like to determine which would be more appropriate what is done in movies, unscripted conversations for the task of identifying sentiment between two humans, and human-machine interaction when a user interacts with a nonhuman systems will contain a varying amount of driven socialbot. With the identification sentiment with very different dialogue. of a model, various improvements Working with a large dataset in the area of are attempted and detailed prior to human-machine interaction systems allows the integration into the Alana system. The evaluation of already existing tools and machine study showed that a Random Forest Model learning techniques to better optimise development with 25 trees trained on the dataset specific within this area. The model is integrated to user interaction with the Alana system into Alana (a 2017 Alexa prize system (Ram et al., combined with the dataset present in 2017) consisting of an ensemble of bots, combining NLTK Vader outperforms other models.