Sentiment analysis of Twitter corpus related to artificial intelligence assistants

Abstract

Providing an enhancing experience is one of the most significant current issues in the user's research. A process that improves user's experience should be required to evaluate the usability and emotion. Above all, sentiment analysis based on user's opinions can be used to understand user's tendency. This paper aims to make a criterion what artificial intelligence assistant is statistically better. User's opinions about three artificial intelligence assistants from Twitter were collected and classified into positive, negative, neutral opinions by a lexicon named Valence Aware Dictionary and sEntiment Reasoner (VADER). Also, we analyzed tweets through independent samples T-test, Kruskal-Wallis test, and Mann-Whitney test to show the statistical significance among groups. The results suggested the highest rank of three artificial intelligence assistants by using statistical analysis.