The DOI matches its Crossref record; 41 of the 45 references it cites were found in a registry or an index.
VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text
Open accessAbstract
The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a gold-standard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, the VADER (Hutto and Gilbert, 2014) delivered the best results in the multi-class classification case.”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Hutto and Gilbert, 2014
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Additional consistency checks were performed using the established VADER lexicon (Hutto & Gilbert, 2014), providing further assurance against algorithmic biases and misclassifications.”
From Generative AI in Design Thinking Pedagogy: Enhancing Creativity, Critical Thinking, and Ethical Reasoning in Higher Education (2025) · cites this paper as Hutto & Gilbert, 2014
The cited source says what the claim attributes to it.
In the citing paper
“Comment-level sentiment is measured using VADER (Valence Aware Dictionary and sEntiment Reasoner) [14], a rule-based sentiment analysis tool validated for social media text.”
From OpenClaw AI Agents as Informal Learners at Moltbook: Characterizing an Emergent Learning Community at Scale (2026) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“VADER is chosen for its rule-based, deterministic character: the same text always produces the same score.”
From DMF: A Deterministic Memory Framework for Conversational AI Agents (2026) · cites this paper as [5]
The cited source says what the claim attributes to it.
In the citing paper
“Emotional valences, measured with tools such as the LIWC, VADER 51 LabMT, or NRC emotion lexicon were often associated with PTSD symptoms or severity.”
From Linguistic markers for identifying post-traumatic stress disorder and associated symptoms: a systematic literature review (2025) · cites this paper as [51]
The cited source says what the claim attributes to it.
In the citing paper
“Valence Aware Dictionary for Sentiment Reasoning (VADER) is a method that has provided promising results on Twitter data (Hutto & Gilbert, 2014).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Hutto & Gilbert, 2014
The cited source says what the claim attributes to it.
In the citing paper
“The VADER sentiment lexicon is composed of more than 7,000 items, along with their associated sentiment intensity measures, validated by humans and specifically adapted to sentiment in microblog-like contexts, such as Twitter (Hutto & Gilbert, 2014).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Hutto & Gilbert, 2014
The cited source says what the claim attributes to it.
In the citing paper
“We also conducted robustness checks with other sentiment classification tools95–98, which show broad agreement with the RoBERTa results (Supplementary Sections 5 and 6).”
From Generative language models exhibit social identity biases (2024) · cites this paper as [95]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Responses skew positive, with most students expressing net-positive sentiment about AI's effect on learning and a minority expressing negative sentiment.”
From Experimental Evidence on the Learning Impact of Generative AI (2026) · cites this paper as Hutto and Gilbert, 2014
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Panel B shows that students who express more positive views about AI's effect on learning use AI somewhat more for academic work during the semester ( ˆ β = 0 . 107, p = 0 . 096).”
From Experimental Evidence on the Learning Impact of Generative AI (2026) · cites this paper as Hutto and Gilbert, 2014
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“A regression of AI use on the sentiment score yields a positive but statistically insignificant coefficient ( ˆ β = 0 . 041, p = 0 . 647).”
From Experimental Evidence on the Learning Impact of Generative AI (2026) · cites this paper as Hutto and Gilbert, 2014
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Sentiment is weakly related to both measures.”
From Experimental Evidence on the Learning Impact of Generative AI (2026) · cites this paper as Hutto and Gilbert, 2014
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Basics of Qualitative Research
Ozanne, J. L., Strauss, A., & Corbin, J. M. (1992) · Journal of Marketing Research · doi:10.2307/3172751 · Cited by 17,852
Kilgarriff, A. (2000) · Language · doi:10.2307/417141 · Cited by 11,529
(1990) · Cited by 11,110
Mining and summarizing customer reviews
Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878
Sentiment Analysis and Opinion Mining
Liu, B. (2012) · Synthesis lectures on human language technologies · doi:10.2200/s00416ed1v01y201204hlt016 · Cited by 3,995
Pang, B., & Lee, L. (2004) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1218955.1218990 · Cited by 3,376
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2017) · Springer eBooks · doi:10.1007/978-1-4899-7687-1_907 · Cited by 3,307
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Tumasjan, A., Sprenger, T. O., Sandner, P., & Welpe, I. M. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14009 · Cited by 2,647
Research works printed but not linked: 9
- [3]3. Baccianella, S., Esuli, A., & Sebastiani, F. (2010). SentiWordNet 3.0. In Proc. of LREC-10.
- [5]5. Cambria, E., Havasi, C., & Hussain, A. (2012). SenticNet 2. In Proc. AAAI IFAI RSC-12.
- [6]6. Cambria, E., Speer, R., Havasi, C., & Hussain, A. (2010). SenticNet. In Proc. of AAAI SCK-10.
- [18]18. Kamps, J., Mokken, R. J., Marx, M., & de Rijke, M. (2004). Us- ing WordNet to measure semantic orientation. In Proc. LREC-04.
- [31]31. Snow, R., O'Connor, B., Jurafsky, D., & Ng, A. Y. (2008). Cheap and Fast -But is it Good?. In Proc. EMNLP-08.
- [33]33. Stone, P. J., Dunphy, D. C., Smith, M. S., & Ogilvie, D. M. (1966). General Inquirer. Cambridge, MA: MIT Press.
- [34]34. Strauss, A. L., & Corbin, J. (1998). Basics of Qualitative Re- search. Thousand Oaks, CA: Sage Publications.
- [36]36. Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting Elections with Twitter:. In Proc. ICWSM-10.
- [38]38. Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012). A system…real-time Twitter sentiment analysis. ACL-12.
Cited by
Sorted by citations
Items 601–700 of 1,026
Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media
Pamungkas, E. W., Basile, V., & Patti, V. (2020) · Institutional Research Information System University of Turin (University of Turin) · Cited by 41
Adding Chit-Chat to Enhance Task-Oriented Dialogues
Sun, K., Moon, S., Crook, P., Roller, S., Silvert, B., Liu, B., Wang, Z., Liu, H., Cho, E., & Cardie, C. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.124 · Cited by 55
Fake News Detection Techniques on Social Media: A Survey
Ali, I., Ayub, M. N. B., Shivakumara, P., & Noor, N. F. B. M. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/6072084 · Cited by 41
Leveraging hierarchical language models for aspect-based sentiment analysis on financial data
Lengkeek, M., van der Knaap, F., & Frăsincar, F. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103435 · Cited by 42
Collective events and individual affect shape autobiographical memory
Rouhani, N., Stanley, D. A., COVID-Dynamic Team, Adolphs, R., Rouhani, N., Stanley, D., Adolphs, R., Maoz, U., Paul, L. K., & Rusch, T. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2221919120 · Cited by 54
Zander, K. K., Nguyen, D., Mirbabaie, M., & Garnett, S. T. (2023) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2023.103936 · Cited by 49
Tourism Development through the Sense of UNESCO World Heritage: The Case of Hegra, Saudi Arabia
Sampieri, S., Saoualih, A., Safaa, L., Calzada, F. M. D. C., Ramazzotti, M., & Peláez, A. M. (2024) · Heritage · doi:10.3390/heritage7040104 · Cited by 41
Mahmoud, A. B., Fuxman, L., Asaad, Y., & Solakis, K. (2024) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-09-2023-1515 · Cited by 46
Nawawi, I., Ilmawan, K. F., Maarif, M. R., & Syafrudin, M. (2024) · Information · doi:10.3390/info15080499 · Cited by 80
Alabduljabbar, R. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2421 · Cited by 41
“The Vader model ( Hutto & Gilbert, 2014 ), a sentiment analysis tool, from NLTK provided polarity scores reflecting the sentiment expressed in each review, with the scoring scale ranging from − 1 (negative sentiment) to +1 (positive sentiment).”
Mitchell, J. J., & Jeon, M. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2445100 · Cited by 51
“…al. 2016), perception of communication competency and interaction quality (Croes & Antheunis, 2021; Demeure et al., 2011; Berry & Hansen, 2000), comfort (Clark et al., 1998) Objective: Self-disclosure duration (Jadoul et al., 2018), disclosure length, disclosure sentiment (Hutto & Gilbert, 2014).”
Atoum, I. (2018) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2018.04.012 · Cited by 42
Exploring the emergence of influential users on social media during natural disasters
Yang, Y., Zhang, C., Fan, C. H., Yao, W., Huang, R., & Mostafavi, A. (2019) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2019.101204 · Cited by 40
Twitter vs News: Concern Analysis of the 2018 California Wildfire Event
Du, H., Nguyen, L., Yang, Z., Abu-gellban, H., Zhou, X., Xing, W., Cao, G., & Jin, F. (2019) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2019.10208 · Cited by 41
Corpus-Based Techniques for Sentiment Lexicon Generation: A Review
Darwich, M., Noah, S. A. M., Omar, N., & Osman, N. A. (2019) · Journal of Digital Information Management · doi:10.6025/jdim/2019/17/5/296-305 · Cited by 41
Learning and Evaluating Emotion Lexicons for 91 Languages
Buechel, S., Rücker, S., & Hahn, U. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.112 · Cited by 50
Unlink the Link Between COVID-19 and 5G Networks: An NLP and SNA Based Approach
Bahja, M., & Safdar, G. A. (2020) · IEEE Access · doi:10.1109/access.2020.3039168 · Cited by 41
Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic
Yeung, N., Lai, J., & Luo, J. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378114 · Cited by 41
Stock Price Prediction Based on LSTM Neural Network: the Effectiveness of News Sentiment Analysis
Guo, Y. (2020) · International Conference on Economic Management and Model Engineering (ICEMME) · doi:10.1109/icemme51517.2020.00206 · Cited by 41
Sentiment Analysis of Political Tweets From the 2019 Spanish Elections
Rodríguez-Ibáñez, M., Gimeno-Blanes, F.-J., Cuenca-Jiménez, P.-M., Soguero-Ruíz, C., & Rojo‐Álvarez, J. L. (2021) · IEEE Access · doi:10.1109/access.2021.3097492 · Cited by 46
Opinion mining for app reviews: an analysis of textual representation and predictive models
Araújo, A. F. D., Gôlo, M. P. S., & Marcacini, R. M. (2021) · Automated Software Engineering · doi:10.1007/s10515-021-00301-1 · Cited by 45
Fredström, A., Parida, V., Wincent, J., Sjödin, D., & Oghazi, P. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121716 · Cited by 50
Ali, M. R., Sen, T., Kane, B., Bose, S., Carroll, T. M., Epstein, R., Schubert, L., & Hoque, E. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3054717 · Cited by 40
Transformer-based language models for mental health issues: A survey
Greco, C. M., Simeri, A., Tagarelli, A., & Zumpano, E. (2023) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.02.016 · Cited by 47
LSTM based stock prediction using weighted and categorized financial news
Usmani, S., & Shamsi, J. A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0282234 · Cited by 40
Knowledge-Enriched Two-Layered Attention Network for Sentiment Analysis
Abhishek, K., Kawahara, D., & Kurohashi, S. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2041 · Cited by 40
Meisheri, H., & Dey, L. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1043 · Cited by 40
Sentiment Analysis on Social Media for Emotion Classification
Tanna, D., Dudhane, M., Sardar, A., Deshpande, K., & Deshmukh, N. S. (2020) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs48265.2020.9121057 · Cited by 40
Deep learning based sentiment analysis of public perception of working from home through tweets
Vohra, A., & Garg, R. (2022) · Journal of Intelligent Information Systems · doi:10.1007/s10844-022-00736-2 · Cited by 40
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial NewsPreprint
Li, X., Li, Y., Yang, H., Yang, L., & Liu, X. (2019) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1912.10806 · Cited by 40
Habib, H., Srinivasan, P., & Nithyanand, R. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555551 · Cited by 68
Length and sentiment analysis of reviews about top-level video game genres on the steam platform
Guzsvinecz, T., & Szűcs, J. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107955 · Cited by 39
Anderson, T., Sarkar, S., & Kelley, R. R. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100097 · Cited by 52
Generative AI in Education: Perspectives Through an Academic Lens
Întorsureanu, I., Oprea, S., Bârã, A., & VESPAN, D. M. (2025) · Electronics · doi:10.3390/electronics14051053 · Cited by 63
Investigating boredom and engagement during writing using multiple sources of information
Allen, L. K., Mills, C., Jacovina, M. E., Crossley, S. A., D’Mello, S. K., & McNamara, D. S. (2016) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2883851.2883939 · Cited by 39
Lexical TF-IDF: An n-gram Feature Space for Cross-Domain Classification of Sentiment Reviews
Dey, A., Jenamani, M., & Thakkar, J. J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69900-4_48 · Cited by 39
E2SAM: Evolutionary ensemble of sentiment analysis methods for domain adaptation
López, M., Valdivia, A., Martínez‐Cámara, E., Luzón, M. V., & Herrera, F. (2018) · Information Sciences · doi:10.1016/j.ins.2018.12.038 · Cited by 39
Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review
Sundermann, C. V., Domingues, M. A., Sinoara, R. A., Marcacini, R. M., & Rezende, S. O. (2019) · Information · doi:10.3390/info10020042 · Cited by 39
Contextual Sentiment Neural Network for Document Sentiment Analysis
Ito, T., Tsubouchi, K., Sakaji, H., Yamashita, T., & Izumi, K. (2020) · Data Science and Engineering · doi:10.1007/s41019-020-00122-4 · Cited by 39
CluHTM - Semantic Hierarchical Topic Modeling based on CluWords
Viegas, F. A. R., da Cunha, W. L. M., Gomes, C., Pereira, A., da Rocha, L. C. D., & Gonçalves, M. A. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.724 · Cited by 39
Bots as Active News Promoters: A Digital Analysis of COVID-19 Tweets
Al‐Rawi, A., & Shukla, V. (2020) · Information · doi:10.3390/info11100461 · Cited by 39
Talbot, J., Charron, V., & Konkle, A. T. M. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18020393 · Cited by 42
D’Orazio, M., Giuseppe, E. D., & Bernardini, G. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104068 · Cited by 39
Rita, P., António, N., & Afonso, A. P. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01048-1 · Cited by 53
Long, C., Lucey, B. M., & Yarovaya, L. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3822315 · Cited by 39
Natural Language Processing in Marketing
Hartmann, J., & Netzer, O. (2023) · Artificial Intelligence in Marketing · doi:10.1108/s1548-643520230000020011 · Cited by 38
Don't miss out on NFTs?! A sentiment-based analysis of the early NFT market
Horky, F. M., Dubbick, L., Rhein, F., & Fidrmuc, J. (2023) · International Review of Economics & Finance · doi:10.1016/j.iref.2023.07.016 · Cited by 39
Cryptocurrency Price Prediction Algorithms: A Survey and Future Directions
John, D. L., Binnewies, S., & Stantić, B. (2024) · Forecasting · doi:10.3390/forecast6030034 · Cited by 55
Emotional states vs. emotional words in social media
Beasley, A., & Mason, W. (2015) · Proceedings of the ACM Web Science Conference · doi:10.1145/2786451.2786473 · Cited by 38
Cross-Lingual Propagation for Deep Sentiment Analysis
Dong, X., & de Melo, G. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12071 · Cited by 38
Zubiaga, A., Procter, R., & Maple, C. (2018) · PLOS ONE · doi:10.1371/journal.pone.0209472 · Cited by 38
Li, L., Wu, Y., Zhang, Y., & Zhao, T. (2019) · IEEE Access · doi:10.1109/access.2019.2895897 · Cited by 38
Towards Fine-grained Text Sentiment Transfer
Luo, F., Li, P., Yang, P., Zhou, J., Tan, Y., Chang, B., Sui, Z., & Sun, X. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1194 · Cited by 38
The Conundrum of Success in Music: Playing it or Talking About it?
Cosimato, A., Prisco, R. D., Guarino, A., Malandrino, D., Lettieri, N., Sorrentino, G., & Zaccagnino, R. (2019) · IEEE Access · doi:10.1109/access.2019.2937743 · Cited by 40
The individual dynamics of affective expression on social media
Pellert, M., Schweighofer, S., & García, D. (2020) · EPJ Data Science · doi:10.1140/epjds/s13688-019-0219-3 · Cited by 41
Chen, L., Lu, X., Yuan, J., Luo, J., Luo, J., Xie, Z., & Li, D. (2020) · Journal of Medical Internet Research · doi:10.2196/17496 · Cited by 38
Cheng, I., Heyl, J., Lad, N. N., Facini, G. J., & Grout, Z. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-98396-9 · Cited by 40
Deas, N., Kowalski, R. M., Finnell, S., Radovic, E., Carroll, H., Robbins, C. E., Cook, A., Hurley, K. J., Cote, N., Evans, K., et al. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107499 · Cited by 39
Hanley, H. W. A., Kumar, D., & Durumeric, Z. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22150 · Cited by 52
Extracting revisit intentions from social media big data: a rule-based classification model
Liu, Y., & Beldona, S. (2021) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-06-2020-0592 · Cited by 37
Deciphering Public Voices in the Digital Era
Fu, X., Sanchez, T. W., Li, C., & Junqueira, J. R. (2024) · Journal of the American Planning Association · doi:10.1080/01944363.2024.2309259 · Cited by 40
Shao, Z., Yao, X., Chen, F., Wang, Z., & Gao, J. (2024) · Decision Support Systems · doi:10.1016/j.dss.2024.114362 · Cited by 37
Predicting altcoin returns using social media
Steinert, L., & Herff, C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0208119 · Cited by 37
Twitter-based measures of neighborhood sentiment as predictors of residential population health
Gibbons, J., Malouf, R., Spitzberg, B. H., Martinez, L. S., Appleyard, B. S., Thompson, C. A., Nara, A., & Tsou, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219550 · Cited by 38
Semantic homophily in online communication: Evidence from Twitter
Šćepanović, S., Mishkovski, I., Gonçalves, B., Nguyen, H. T., & Hui, P. (2016) · Online Soc. Networks Media · doi:10.1016/j.osnem.2017.06.001 · Cited by 42
Tweet Summarization of News Articles: An Objective Ordering-Based Perspective
Chakraborty, R., Bhavsar, M., Dandapat, S. K., & Chandra, J. (2019) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2019.2926144 · Cited by 37
Sentiment Analysis by Fusing Text and Location Features of Geo-Tagged Tweets
Lim, W. L., Ho, C. C., & Ting, C. (2020) · IEEE Access · doi:10.1109/access.2020.3027845 · Cited by 38
An AutoML-based Approach to Multimodal Image Sentiment Analysis
Lopes, V., Gaspar, A., Alexandre, L. A., & Cordeiro, J. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533552 · Cited by 37
A Regularized Model to Trade-off between Accuracy and Diversity in a News Recommender System
Raza, S., & Ding, C. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378340 · Cited by 37
Bahamazava, K., & Nanda, R. (2022) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2022.301377 · Cited by 37
Predicting NEPSE index price using deep learning models
Pokhrel, N. R., Dahal, K. R., Rimal, R., Bhandari, H. N., Khatri, R. K. C., Rimal, B., & Hahn, W. E. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100385 · Cited by 44
Public discourse and sentiment during Mpox outbreak: an analysis using natural language processing
Anoop, V. S., & Sreelakshmi, S. (2023) · Public Health · doi:10.1016/j.puhe.2023.02.018 · Cited by 37
Global Sentiment Analysis Of COVID-19 Tweets Over TimePreprint
Mansoor, M., Gurumurthy, K., U, A. R., & Prasad, V. R. B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.14234 · Cited by 58
Jin, F., Peng, X., Sun, L., Song, Z., Zhou, K., & Lin, C. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70004 · Cited by 40
Dar, S. S., Rehman, M. Z. U., Bais, K., Haseeb, M. A., & Kumar, N. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125337 · Cited by 36
“We use EmoLex [49] to quantitatively assess emotional content within textual data.”
The E-Tour Facilitator Platform Supporting an Innovative Health Tourism Marketing Strategy
Halkiopoulos, C., Dimou, E., Kompothrekas, A., Telonis, G., & Boutsinas, B. (2021) · Springer proceedings in business and economics · doi:10.1007/978-3-030-72469-6_39 · Cited by 36
Gasteiger, N., Lim, J., Hellou, M., MacDonald, B. A., & Ahn, H. S. (2022) · International Journal of Social Robotics · doi:10.1007/s12369-022-00913-x · Cited by 36
A Decision-Support System to Analyse Customer Satisfaction Applied to a Tourism Transport Service
Ramos, C. M. Q., Cardoso, P. J. S., Fernandes, H. C. L., & Rodrigues, J. M. F. (2022) · Multimodal Technologies and Interaction · doi:10.3390/mti7010005 · Cited by 36
Li, W., Haunert, J., Knechtel, J., Zhu, J., Zhu, Q., & Dehbi, Y. (2023) · Transactions in GIS · doi:10.1111/tgis.13097 · Cited by 39
A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets
Ebrahimi, J., Dou, D., & Lowd, D. (2016) · International Conference on Computational Linguistics · Cited by 36
Understanding Diverse Interpretations of Animated GIFs
Jiang, J. A., Brubaker, J. R., & Fiesler, C. L. (2017) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3027063.3053139 · Cited by 37
Sentiment Analysis of Malay Social Media Text
Chekima, K., & Alfred, R. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-8276-4_20 · Cited by 36
Colladon, A. F., Grippa, F., & Innarella, R. (2019) · Tourism Management Perspectives · doi:10.1016/j.tmp.2019.100588 · Cited by 36
Using social media to measure impacts of named storm events in the United Kingdom and Ireland
Spruce, M., Arthur, R., & Williams, H. T. P. (2020) · Meteorological Applications · doi:10.1002/met.1887 · Cited by 36
Roy, K. C., Hasan, S., & Mozumder, P. (2020) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12573 · Cited by 36
Darwich, M., Noah, S. A. M., Omar, N., Osman, N. A., & Ahmad, I. S. (2020) · Journal of Advances in Information Technology · doi:10.12720/jait.11.3.109-118 · Cited by 36
Twitter Sentiment Analysis Using Lexical or Rule Based Approach: A Case Study
Zahoor, S., & Rohilla, R. (2020) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito48877.2020.9197910 · Cited by 36
Gbashi, S., Adebo, O. A., Doorsamy, W., & Njobeh, P. B. (2020) · JMIR Medical Informatics · doi:10.2196/22916 · Cited by 37
Assessing Public Opinions of Products Through Sentiment Analysis
Ng, C. Y., Law, K. M. Y., & Ip, W. H. (2021) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.20210701.oa6 · Cited by 36
Investigating Public Discourses Around Gender and COVID-19: a Social Media Analysis of Twitter Data
Al‐Rawi, A., Grépin, K. A., Li, X., Morgan, R., Wenham, C., & Smith, J. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-021-00102-x · Cited by 39
CoUX: Collaborative Visual Analysis of Think-Aloud Usability Test Videos for Digital Interfaces
Soure, E. J., Kuang, E., Fan, M., & Zhao, J. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114822 · Cited by 36
Costello, F. J., & Lee, K. C. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113695 · Cited by 36
Salamone, F., Barozzi, B., Bellazzi, A., Belussi, L., Danza, L., Devitofrancesco, A., Ghellere, M., Meroni, I., Scamoni, F., & Scrosati, C. (2021) · Buildings · doi:10.3390/buildings11120660 · Cited by 36
Park, J., Choi, W., & Jung, S.-U. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.890435 · Cited by 36
The Effect of Social Feedback in a Reddit Weight Loss Community
Cunha, T. O., Weber, I. G., Haddadi, H., & Pappa, G. L. (2016) · International Conference on Digital Health · doi:10.1145/2896338.2897732 · Cited by 36
Park, J., Ciampaglia, G. L., & Ferrara, E. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2820065 · Cited by 36
Wang, S. A., Pang, M., & Pavlou, P. A. (2022) · MIS Quarterly · doi:10.25300/misq/2022/16296 · Cited by 50
Sentiment analysis of COVID-19 cases in Greece using Twitter data
Samaras, L., García‐Barriocanal, E., & Sicilia, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120577 · Cited by 38
Metadata
- Authors
- Hutto, C., & Gilbert, E.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2014-05-16
- Volume
- 8
- Issue
- 1
- Pages
- 216-225
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2099813784
- Semantic Scholar
- bcdc102c04fb0e7d4652e8bcc7edd2983bb9576d
- Cited by
- 6,087
- References
- 45
- Citations by year
- 2026: 676 · 2025: 846 · 2024: 877 · 2023: 885 · 2022: 731 · 2021: 714 · 2020: 526 · 2019: 332 · 2018: 245 · 2017: 148 · 2016: 76 · 2015: 27 · 2014: 4
- FWCI
- 50.06
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- sentiment analysis, VADER, rule-based sentiment analysis, social media text, tweets, lexical features, sentiment intensity, microblogging, human raters
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
