Enriching Lexicons with Ephemeral Words for Sentiment Analysis in Social Streams

verfasst von

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.

Organisationseinheit(en)

Fakultät für Elektrotechnik und Informatik
Forschungszentrum L3S

Externe Organisation(en)

Otto-von-Guericke-Universität Magdeburg

Typ

Aufsatz in Konferenzband

Anzahl der Seiten

8

Publikationsdatum

25.06.2018

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Mensch-Maschine-Interaktion, Maschinelles Sehen und Mustererkennung, Computernetzwerke und -kommunikation

Ziele für nachhaltige Entwicklung

SDG 16 – Frieden, Gerechtigkeit und starke Institutionen

Elektronische Version(en)

https://doi.org/10.1145/3227609.3227664 (Zugang: Geschlossen)