Sentiment Analysis in Turkish Question Answering Systems: An Application of Human-Robot Interaction

Abstract

The use of the sentiment analysis technique, which aims to extract emotions and thoughts from texts, has become a remarkable research topic today, where the importance of human-robot interaction is gradually increasing. In this study, a new hybrid sentiment analysis model is proposed using machine learning algorithms to increase emotional performance for Turkish question and answer systems. In this context, as a first, we apply text preprocessing steps to the Turkish question-answer-emotion dataset. Subsequently, we convert the preprocessed question and answer texts into text vector form using Pretrained Turkish BERT Model and two different word representation methods, TF-IDF and word2vec. Additionally, we incorporate pre-determined polarity vectors containing the positive and negative scores of words into the question-answer text vector. As a result of this study, we propose a new hybrid sentiment analysis model. We separate vectorized and expanded question-answer text vectors into training and testing data and train and test them with machine learning algorithms. By employing this previously unused method in Turkish question-answering systems, we achieve an accuracy value of up to 91.05% in sentiment analysis. Consequently, this study contributes to making human-robot interactions in Turkish more realistic and sensitive.