Enriching Lexicons with Ephemeral Words for Sentiment Analysis in Social Streams

authored by: Damianos P. Melidis, Alvaro Veizaga Campero, Vasileios Iosifidis, Eirini Ntoutsi, Myra Spiliopoulou
Abstract: Lexical approaches for sentiment analysis like SentiWordNet rely upon a fixed dictionary of words with fixed sentiment, i.e., sentiment that does not change. With the rise of Web 2.0 however, what we observe more and more often is that words that are not sentimental per se, are often associated with positive/negative feelings, for example, “refugees”, “Trump”, “iphone”. Typically, those feelings are temporary as responses to external events; for example, “iphone” sentiment upon latest iphone version release or “Trump” sentiment after USA withdraw from Paris climate agreement. In this work, we propose an approach for extracting and monitoring what we call ephemeral words from social streams; these are words that convey sentiment without being sentimental and their sentiment might change with time. Such sort of words cannot be part of a lexicon like SentiWordNet since their sentiment has an ephemeral character, however detecting such words and estimating their sentiment can significantly improve the performance of lexicon-based approaches, as our experiments show.
Organisation(s): Faculty of Electrical Engineering and Computer Science
L3S Research Centre
External Organisation(s): Otto-von-Guericke University Magdeburg
Type: Conference contribution
No. of pages: 8
Publication date: 25.06.2018
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Software, Human-Computer Interaction, Computer Vision and Pattern Recognition, Computer Networks and Communications
Sustainable Development Goals: SDG 16 - Peace, Justice and Strong Institutions
Electronic version(s): https://doi.org/10.1145/3227609.3227664 (Access: Closed)