Aspect-Based Extraction and Analysis of Affective Knowledge from Social Media Streams

Abstract

Extracting and analyzing affective knowledge from social media in a structured manner is a challenging task. Decision makers require insights into the public perception of a company's products and services, as a strategic feedback channel to guide communication campaigns, and as an early warning system to quickly react in the case of unforeseen events. The approach presented in this paper goes beyond bipolar metrics of sentiment. It combines factual and affective knowledge extracted from rich public knowledge bases to analyze emotions expressed towards specific entities (targets) in social media. We obtain common and common-sense domain knowledge from DBpedia and ConceptNet to identify potential sentiment targets. We employ affective knowledge about emotional categories available from SenticNet to assess how those targets and their aspects (e.g. specific product features) are perceived in social media. An evaluation shows the usefulness and correctness of the extracted domain knowledge, which is used in a proof-of-concept data analytics application to investigate the perception of car brands on social media in the period between September and November 2015.