Umigon: Sentiment Analysis For Tweets Based On Lexicons And Heuristics
Open accessAbstract
Umigon is developed since December 2012 as a web application providing a service of sentiment detection in tweets. It has been designed to be fast and scalable. Umigon also provides indications for additional semantic features present in the tweets, such as time indications or markers of subjectivity. Umigon is in continuous development, it can be tried freely at www.umigon.com. Its code is open sourced at: https://github.com/seinecle/Umigon
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Umigon is a dictionary-based method that uses a lexicon with heuristics for sentiment detection in Twitter reviews.
“Umigon is another dictionary -based method, which uses a lexicon with heuristics for sentiment detection in Twitter reviews (Levallois, 2013).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Levallois, 2013
Umigon provides additional semantic features, such as time or subjectivity.
“Umigon provides additional semantic features, such as time or subjectivity (Levallois, 2013).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Levallois, 2013
The method proposed by Levallois (2013) provided the best results in the binary classification problem.
“The method proposed by Levallois, (2013) has provided the best results in the binary classification problem.”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Levallois, 2013
The Accuracy section reports results for a three-class task—positive, negative, or neutral—and says another participant performed better on that task. Our copy does not report a binary-classification comparison, so it cannot establish whether Levallois’s method produced the best binary results. (LLM, conf=0.75)
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Research works printed but not linked: 4
- Davidov, D., Tsur, O., and Rappoport, A. 2010. Enhanced sentiment learning using twitter hashtags and smileys. Proceedings of Coling.
- Kouloumpis, E., Wilson, T., and Moore, J. 2011. Twitter Sentiment Analysis: The Good the Bad and the OMG! Proceedings of ICWSM.
- Mohammad, S., Kiritchenko, S. and Zhu, X. 2013. NRC- Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets. In Proceedings of the International Workshop on Semantic Evaluation, SemEval '13, June 2013, Atlanta, Georgia.
- Wilson, T., Kozareva, Z., Nakov, P., Rosenthal, S. Stoyanov, V. and Alan Ritter. 2013. SemEval-2013 Task 2: Sentiment Analysis in Twitter. In Proceedings of the International Workshop on Semantic Evaluation, SemEval '13, June 2013, Atlanta, Georgia.
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Sentiment Analysis in Tourism: Capitalizing on Big Data
Alaei, A. R., Becken, S., & Stantic, B. (2019) · Journal of Travel Research · doi:10.1177/0047287517747753 · Cited by 622 · see citing works
- Authors
- Levallois, C.
- Affiliations
- EMLYON Business School
- Venue
- Zenodo
- Publisher
- Zenodo
- Published
- 2013-06-01
- OpenAlex
- W1503130737
- Cited by
- 13
- References
- 0
- Citations by year
- 2021: 1 · 2020: 2 · 2018: 1 · 2017: 5 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 1
- Type
- reference entry
- Language
- en
- Open access
- green
- repub.eur.nl
- Licence
- Locations
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- sentiment analysis, tweets, heuristics, web application, scalability, temporal expressions, social media text
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