Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | WIMS '18 |
| Untertitel | Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics |
| Herausgeber/-innen | Costin Badica, Rajendra Akerkar, Mirjana Ivanovic, Milos Savic, Milos Radovanovic, Sang-Wook Kim, Riccardo Rosati, Yannis Manolopoulos |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9781450354899 |
| Publikationsstatus | Veröffentlicht - 25 Juni 2018 |
| Veranstaltung | 8th International Conference on Web Intelligence, Mining and Semantics, WIMS 2018 - Novi Sad, Serbien Dauer: 25 Juni 2018 → 27 Juni 2018 |
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Informatik (insg.)
- Computernetzwerke und -kommunikation
Ziele für nachhaltige Entwicklung
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WIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. Hrsg. / Costin Badica; Rajendra Akerkar; Mirjana Ivanovic; Milos Savic; Milos Radovanovic; Sang-Wook Kim; Riccardo Rosati; Yannis Manolopoulos. 2018. 38.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Enriching Lexicons with Ephemeral Words for Sentiment Analysis in Social Streams
AU - Melidis, Damianos P.
AU - Campero, Alvaro Veizaga
AU - Iosifidis, Vasileios
AU - Ntoutsi, Eirini
AU - Spiliopoulou, Myra
N1 - Funding information: The work was funded by the German Research Foundation (DFG) project OSCAR (Opinion Stream Classification with Ensembles and Active leaRners) and by the European Commission for the ERC Advanced Grant ALEXANDRIA under grant No. 339233.
PY - 2018/6/25
Y1 - 2018/6/25
N2 - 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.
AB - 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.
KW - Dictionary-based approaches
KW - Ephemeral words
KW - Lexicon enrichment
KW - Sentiment classification
UR - http://www.scopus.com/inward/record.url?scp=85053484449&partnerID=8YFLogxK
U2 - 10.1145/3227609.3227664
DO - 10.1145/3227609.3227664
M3 - Conference contribution
AN - SCOPUS:85053484449
BT - WIMS '18
A2 - Badica, Costin
A2 - Akerkar, Rajendra
A2 - Ivanovic, Mirjana
A2 - Savic, Milos
A2 - Radovanovic, Milos
A2 - Kim, Sang-Wook
A2 - Rosati, Riccardo
A2 - Manolopoulos, Yannis
T2 - 8th International Conference on Web Intelligence, Mining and Semantics, WIMS 2018
Y2 - 25 June 2018 through 27 June 2018
ER -