Explore the Effects of Emoticons on Twitter Sentiment Analysis
Open accessAbstract
In recent years, Twitter Sentiment Analysis (TSA) has become a hot research topic.The target of this task is to analyse the sentiment polarity of the tweets.There are a lot of machine learning methods specifically developed to solve TSA problems, such as fully supervised method, distantly supervised method and combined method of these two.Considering the specialty of tweets that a limitation of 140 characters, emoticons have important effects on TSA.In this paper, we compare three emoticon pre-processing methods: emotion deletion (emoDel), emoticons 2-valued translation (emo2label) and emoticon explanation (emo2explanation).Then, we propose a method based on emoticon-weight lexicon, and conduct experiments based on Naive Bayes classifier, to validate the crucial role emoticons play on guiding emotion tendency in a tweet.Experiments on real data sets demonstrate that emoticons are vital to TSA.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several authors have been interested in the use of emoticons to complete sentiment analysis.
“Several authors have been interested in the use of emoticons to complete the sentiment analysis.”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [29]
Authors in [29] explored the influence of emoticons on Twitter sentiment analysis.
“The authors in [29] explored the influence of emoticons on TSA.”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [29]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Thumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024 · see citing works
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957 · see citing works
Thumbs up? Sentiment Classification using Machine Learning TechniquesPreprint
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0205070 · Cited by 2,211 · see citing works
Twitter power: Tweets as electronic word of mouth
Jansen, B. J. J., Zhang, M., Sobel, K., & Chowdury, A. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21149 · Cited by 1,664 · see citing works
Target-dependent Twitter Sentiment Classification
Jiang, L., Yu, M., Zhou, M., Liu, X., & Zhao, T. (2011) · Cited by 897 · see citing works
Ghiassi, M., Skinner, J., & Zimbra, D. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.057 · Cited by 485 · see citing works
Classifying sentiment in microblogs
Bermingham, A., & Smeaton, A. F. (2010) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1871437.1871741 · Cited by 328 · see citing works
Emoticon Smoothed Language Models for Twitter Sentiment Analysis
Liu, K.-L., Li, W.-J., & Guo, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v26i1.8353 · Cited by 254 · see citing works
Exploiting emoticons in polarity classification of text
Hogenboom, A., Bal, D., Frăsincar, F., et al. (2015) · EUR Research Repository (Erasmus University Rotterdam) · doi:10.5555/2871254.2871257 · Cited by 67 · see citing works
Tweets mining for French Presidential Election
Węgrzyn-Wolska, K. M., & Bougueroua, L. (2012) · International Conference on Computational Aspects of Social Networks (CASoN) · doi:10.1109/cason.2012.6412392 · Cited by 19 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 6
- Dziczkowski, G., & Wegrzyn-Wolska, K. 2007b. Rcss -rating critics support system purpose built for movies recommendation. In: Advances in Intelligent Web Mastering. Springer.
- Dziczkowski, G., & Wegrzyn-Wolska, K. 2008a. An autonomous system designed for automatic detection and rating of film. Extraction and linguistic analysis of sentiments. IN Proceedings of WIC, Sydney.
- Janik Lthi, Lamine Bougueroua and K. Wegrzyn-Wolska, Sentiment Polarity on Twitter messages with geolocation, in proceedings of the International Workshop on Computational Social Networks (IWCSN 2014) within the 15th International Conference on Web Information System Engineering WISE 2014, Thessalonique, Greece, October 2014, Springer Lecture Notes in Computer Science (LNCS).
- Dziczkowski, G., & Wegrzyn-Wolska, K. 2008b. Tool of the intelligence economic: Recognition function of reviews critics. In: ICSOFT 2008 Proceedings. INSTICC Press.
- Go, Alec and Bhayani, Richa and Huang, Lei, Twitter sentiment classification using distant supervision, journal CS224N Project Report, Stanford, volume 1, pages 12, 2009.
- AUTHORS Katarzyna Węgrzyn-Wolska received M.Sc. from the Silesian Technical University of Gliwice (Poland), and a further M.Sc. in Computer Science from the University of Val Essonne (France), her Ph.D. (2001) in Automatics, Real Time Computing and Computer Science from the Ecole Superieur des Mines de Paris, France and the habilitation (H.D.R), to become Full Professor in 2012. She is the Principal Professor and head of an SITR team at ESIGETEL, France. She is editor-in-chief of the International Journal on Social Informatics edited in ICST Transactions Series. She is involved in the organization of several International Conference as well as an expert for the group Information Society Technologies (IST) active in the European Community. Her main interests are Information Retrieval, Search Engines, Web Based Support Systems, Web Intelligence and Social Networks.
Cited by
Sorted by citations
Survey on AI-Based Multimodal Methods for Emotion Detection
Marechal, C., Mikołajewski, D., Tyburek, K., Prokopowicz, P., Bougueroua, L., Ancourt, C., & Węgrzyn-Wolska, K. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-16272-6_11 · Cited by 137 · see citing works
Self-citation
- Authors
- Wegrzyn-Wolska, K., Bougueroua, L., Yu, H., & Zhong, J.
- Venue
- Computer Science & Information Technology ( CS & IT )
- Published
- 2016-08-27
- Pages
- 65-77
- OpenAlex
- W2510059946
- Cited by
- 13
- References
- 10
- Citations by year
- 2025: 2 · 2022: 1 · 2021: 2 · 2020: 3 · 2019: 4 · 2018: 1
- FWCI
- 0.90
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- 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
- emoticons, Twitter sentiment analysis, sentiment polarity, Naive Bayes classifier
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
