In this paper, we describe a set of reusable text processing components for extracting opinionated information from social media, rating it for interestingness, and for detecting opinion events. We have developed applications in GATE to extract named entities, terms and events and to detect opinions about them, which are then used as the starting point for opinion event detection. The opinions are then aggregated over larger sections of text, to give some overall sentiment about topics and documents, and also some degree of information about interestingness based on opinion diversity. We go beyond traditional opinion mining techniques in a number of ways: by focusing on specific opinion-target extraction related to key terms and events, by examining and dealing with a number of specific linguistic phenomena, by analysing and visualising opinion dynamics over time, and by aggregating the opinions in different ways for a more flexible view of the information contained in the documents.
People have proposed methods for opinion mining of specific entities from Twitter content REF .
Should I Care about Your Opinion? Detection of Opinion Interestingness and Dynamics in Social Media
Future Internet
Future Internet
Computer Science