A survey of artificial intelligence techniques for user perceptions’ extraction from social media data

Abstract

Measuring and analyzing user perceptions and behaviors in order to make user-centric decisions has been a topic of research for a long time even before the invention of social media platforms. In the past, the main approaches for measuring user perceptions were conducting surveys, interviewing experts and collecting data through questionnaires. But the main challenge with these methods was that the extracted perceptions were only able to represent a small group of people and not whole public. This challenge was resolved when social media platforms like Twitter and Facebook were introduced and users started to share their perceptions about any product, topic, event using these platforms. As these platforms became popular, the amount of data being shared on these platforms started to grow exponentially and this growth led to another challenge of analyzing this huge amount of data to understand or measure user perceptions. Computational techniques are used to address the challenge. This paper briefly describes the artificial intelligence (AI) techniques, which is one of the types of computational techniques available for analyzing social media data. Along with brief information about the AI techniques, this paper also shows state-of-the-art studies which utilize the AI techniques for measuring user perceptions from the social media data.