Neurosymbolic AI for Personalized Sentiment Analysis

Sentiment analysis is crucial in extracting valuable insights from vast amounts of textual data generated across various platforms, such as social media, customer reviews, news articles, etc. Over the years, researchers and business professionals have worked hard to refine sentiment analysis algorithms, but there is a limit to how accurate any algorithm can be without considering personalization. In this work, we propose a framework for personalized sentiment analysis that performs automatic user profiling by modeling users based on different levels of personalization, before performing sentiment analysis. In particular, such framework leverages seven levels of personalization (from bottom to top), namely: Entity, to distinguish between humans and other intelligent agents; Culture, to take into account how different cultures perceive the same concept as positive or negative; Religion, to consider how specific religious beliefs may affect an individual’s opinion about certain topics; Vocation, to better gauge people’s opinion based on their job and education level; Ideology, to take into account political beliefs as well as social, economic, or philosophical viewpoints; Personality, to better classify certain concepts as positive or negative based on personality traits; finally, Subjectivity, to take into account personal preferences and experiences.