Cited by 1,062Sentiment Analysis and Opinion MiningEmotion and Mood RecognitionHate Speech and Cyberbullying Detection

The DOI matches its Crossref record; 59 of the 65 references it cites were found in a registry or an index.

Sentiment strength detection for the social web

Thelwall, Mike, Buckley, Kevan, Paltoglou, Georgios·2012·Journal of the American Society for Information Science and Technology

DOI 10.1002/asi.21662

Open this paper in CoChat — free account

Abstract

Sentiment analysis is concerned with the automatic extraction of sentiment‐related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine‐learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news‐related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.

Integrity signals

Registry

Crossref · 10.1002/asi.21662

Cites

59 of 59 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Content Analysis: An Introduction to its Methodology.
Shelley, M., & Krippendorff, K. (1984) · Journal of the American Statistical Association · doi:10.2307/2288384 · Cited by 24,346
Data Mining
Witten, I. H. (2008) · Cited by 9,143
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957
Opinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372
Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809
Making Large-Scale SVM Learning PracticalPreprint
Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,517
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
Peter, P., & Turney (2002) · Cited by 3,646
Lexicon-based methods for sentiment analysis
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011) · Computational Linguistics · doi:10.1162/coli_a_00049 · Cited by 3,301
Twitter as a Corpus for Sentiment Analysis and Opinion Mining
Pak, A., & Paroubek, P. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/47akcj8foo4d · Cited by 2,784

Show all 65 references

Research works printed but not linked: 11

Andreevskaia A., 2008, Proceedings of the Association for Computational Linguistics: Human Language Technologies (ACL '08: HLT), 290
Aue A., 2005, Proceedings of the International Conference on Recent Advances in Natural Language Processing
Baccianella S., 2010, Proceedings of the Seventh conference on International Language Resources and Evaluation
Diakopoulos N.A., 2010, Proceedings of the 28th International Conference on Human factors in Computing Systems, 1195
Kramer A.D.I., 2010, Proceedings of ACM Conference on Human Factors in Computing Systems, 287
Mishne G.(2005).Experiments with mood classification in Blog posts. In Proceedings of the First Workshop on Stylistic Analysis of Text for Information. Retrieved fromhttp://staff.science.uva.nl/∼gilad/pubs/style2005‐blogmoods.pdf
Mishne G., 2006, Proceedings of the AAAI Spring Symposium on Computational Approaches to Analysing Weblogs (AAAI‐CAAW), 145
O'Connor B., 2010, Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 122
Strapparava C., 2004, Proceedings of the Fourth International Conference on Language Resources and Evaluation, 1083
Taboada M., 2004, Proceedings of AAAI Spring Symposium on Exploring Attitude and Affect in Text (AAAI Technical Report SS‐04–07), 158
Turney P.D., 2002, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL '02), 417

Sorted by citations

Lexicon-based methods for sentiment analysis
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011) · Computational Linguistics · doi:10.1162/coli_a_00049 · Cited by 3,301
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications
Ravi, K., & Ravi, V. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.06.015 · Cited by 1,376
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1146 · Cited by 1,181
Survey on Aspect-Level Sentiment Analysis
Schouten, K., & Frăsincar, F. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2485209 · Cited by 856
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., & Lehmann, S. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1169 · Cited by 894
A Survey on Text Classification: From Traditional to Deep Learning
Li, Q., Peng, H. M., Li, J., Xia, C., Yang, R., Sun, L., Yu, P. S., & He, L. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3495162 · Cited by 791
How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews
Guzmán, E., & Maalej, W. (2014) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2014.6912257 · Cited by 611
The evolution of sentiment analysis—A review of research topics, venues, and top cited papers
Mäntylä, M. V., Graziotin, D., & Kuutila, M. (2017) · Computer Science Review · doi:10.1016/j.cosrev.2017.10.002 · Cited by 651
Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics
Salehan, M., & Kim, D. J. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.10.006 · Cited by 588
Like It or Not
Giachanou, A., & Crestani, F. (2016) · ACM Computing Surveys · doi:10.1145/2938640 · Cited by 562
Good debt or bad debt: Detecting semantic orientations in economic texts
Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23062 · Cited by 513
Deep Convolution Neural Networks for Twitter Sentiment Analysis
Zhao, J., Xiaolin, G., & Zhang, X. (2018) · IEEE Access · doi:10.1109/access.2017.2776930 · Cited by 498
Bug report, feature request, or simply praise? On automatically classifying app reviews
Maalej, W., & Nabil, H. (2015) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2015.7320414 · Cited by 463
Contextual semantics for sentiment analysis of Twitter
Saif, H., He, Y., Fernández, M., & Alani, H. (2015) · Information Processing & Management · doi:10.1016/j.ipm.2015.01.005 · Cited by 437
The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms
van Atteveldt, W., van der Velden, M. A. C. G., & Boukes, M. (2021) · Communication Methods and Measures · doi:10.1080/19312458.2020.1869198 · Cited by 470
Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis
Zhao, J., & Xiaolin, G. (2017) · IEEE Access · doi:10.1109/access.2017.2672677 · Cited by 394
Quantifying controversy on social media
Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2018) · ACM Transactions on Social Computing · doi:10.1145/3140565 · Cited by 348
Digital Emotion Contagion
Goldenberg, A., & Gross, J. J. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.01.009 · Cited by 348
Arabic sentiment analysis: Lexicon-based and corpus-based
Abdulla, N. A., Ahmed, N. A., Shehab, M. A., & Al‐Ayyoub, M. (2013) · IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies · doi:10.1109/aeect.2013.6716448 · Cited by 313
Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies
Kim, Y., Kim, J. G., Kim, W., Im, J., Kim, T. H., Kang, S.-J., & Kim, C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0161197 · Cited by 303
Is Neutral Really Neutral? The Effects of Neutral User-Generated Content on Product Sales
Tang, T., Fang, E., & Wang, F. (2014) · Journal of Marketing · doi:10.1509/jm.13.0301 · Cited by 284
On the automatic classification of app reviews
Maalej, W., Kurtanović, Z., Nabil, H., & Stanik, C. (2016) · Requirements Engineering · doi:10.1007/s00766-016-0251-9 · Cited by 284
WASSA-2017 Shared Task on Emotion Intensity
Mohammad, S. M., & Bravo-Márquez, F. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5205 · Cited by 303
A survey of state-of-the-art approaches for emotion recognition in text
Alswaidan, N., & Menaï, M. E. B. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-020-01449-0 · Cited by 292
Sentiment Analysis on Multi-View Social Data
Niu, T., Zhu, S., Pang, L., & Saddik, A. E. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27674-8_2 · Cited by 272
Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons
Khoo, C. S. G., & Johnkhan, S. B. (2017) · Journal of Information Science · doi:10.1177/0165551517703514 · Cited by 287
Network Issue Agendas on Twitter During the 2012 U.S. Presidential Election
Vargo, C. J., Гуо, Л., McCombs, M., & Shaw, D. L. (2014) · Journal of Communication · doi:10.1111/jcom.12089 · Cited by 259
An Integrated Text Analytic Framework for Product Defect Discovery
Abrahams, A. S., Fan, W., Wang, G. A., Zhang, Z., & Jiao, J. (2014) · Production and Operations Management · doi:10.1111/poms.12303 · Cited by 258
Sentiment analysis of commit comments in GitHub: an empirical study
Guzmán, E., Azócar, D., & Yang, L. (2014) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/2597073.2597118 · Cited by 253
Survey on sentiment analysis: evolution of research methods and topics
Cui, J., Wang, Z., Ho, S.-B., & Cambria, E. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10386-z · Cited by 278
An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief
Beigi, G., Hu, X., Maciejewski, R., & Liu, H. (2016) · Studies in computational intelligence · doi:10.1007/978-3-319-30319-2_13 · Cited by 237
Emotion Intensities in Tweets
Mohammad, S. M., & Bravo-Márquez, F. (2017) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s17-1007 · Cited by 236
A novel social media competitive analytics framework with sentiment benchmarks
He, W., Wu, H., Yan, G., Akula, V., & Shen, J. (2015) · Information & Management · doi:10.1016/j.im.2015.04.006 · Cited by 233
The Effect of Social Media Usage Characteristics on e-WOM, Trust, and Brand Equity: Focusing on Users of Airline Social Media
Seo, E. J., Park, J.-W., & Choi, Y. J. (2020) · Sustainability · doi:10.3390/su12041691 · Cited by 232
A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis
Symeonidis, S., Effrosynidis, D., & Arampatzis, A. (2018) · Expert systems with applications · doi:10.1016/j.eswa.2018.06.022 · Cited by 214
A sentiment-enhanced personalized location recommendation system
Yang, D., Zhang, D., Yu, Z., & Wang, Z. (2013) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2481492.2481505 · Cited by 228
Lisbon Emoji and Emoticon Database (LEED): Norms for emoji and emoticons in seven evaluative dimensions
Rodrigues, D. L., Prada, M., Gaspar, R., Garrido, M. V., & Lopes, D. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0878-6 · Cited by 228
Combining lexicon and learning based approaches for concept-level sentiment analysis
Mudinas, A., Zhang, D., & Mark Levene, A. (2012) · International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) · doi:10.1145/2346676.2346681 · Cited by 223
Meta-level sentiment models for big social data analysis
Bravo-Márquez, F., Mendoza, M., & Poblete, B. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.05.016 · Cited by 221
Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data
Kumar, A., Srinivasan, K., Cheng, W.-H., & Zomaya, A. Y. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102141 · Cited by 219
Sentiment analysis of political communication: combining a dictionary approach with crowdcoding
Haselmayer, M., & Jenny, M. (2016) · Quality & Quantity · doi:10.1007/s11135-016-0412-4 · Cited by 231
A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm
Liu, Y., Bi, J.-W., & Fan, Z. (2017) · Information Sciences · doi:10.1016/j.ins.2017.02.016 · Cited by 211
Deep learning for sentiment analysis: successful approaches and future challenges
Tang, D., Qin, B., & Liu, T. (2015) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1171 · Cited by 214
Sentiment Polarity Detection for Software Development
Calefato, F., Lanubile, F., Maiorano, F., & Novielli, N. (2017) · Empirical Software Engineering · doi:10.1007/s10664-017-9546-9 · Cited by 211
Analyzing and predicting viral tweets
Jenders, M., Kasneci, G., & Naumann, F. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2487788.2488017 · Cited by 206
Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold.
Saif, H., Fernández, M., He, Y., & Alani, H. (2013) · Open Research Online - ORO (The Open University) · Cited by 206
Analytical mapping of opinion mining and sentiment analysis research during 2000–2015
Piryani, R., Madhavi, D., & Singh, V. K. (2016) · Information Processing & Management · doi:10.1016/j.ipm.2016.07.001 · Cited by 205
Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms
Liu, Y., Bi, J.-W., & Fan, Z. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.03.042 · Cited by 204
Multilingual Twitter Sentiment Classification: The Role of Human Annotators
Mozetič, I., Grćar, M., & Smailović, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155036 · Cited by 215
The Heart and Soul of the Web? Sentiment Strength Detection in the Social Web with SentiStrength
Thelwall, M. A. (2016) · Understanding complex systems · doi:10.1007/978-3-319-43639-5_7 · Cited by 213
Self-citation
Understanding online health information: Evaluation, tools, and strategies
Beaunoyer, E., Arsenault, M., Lomanowska, A. M., & Guitton, M. J. (2016) · Patient Education and Counseling · doi:10.1016/j.pec.2016.08.028 · Cited by 205
Refining Word Embeddings for Sentiment Analysis
Yu, L.-C., Wang, J., Lai, K.-H. R., & Zhang, X. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1056 · Cited by 195
Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews
Mcilroy, S., Ali, N., Khalid, H., & Hassan, A. E. (2015) · Empirical Software Engineering · doi:10.1007/s10664-015-9375-7 · Cited by 193
Application of Machine Learning Methods in Mental Health Detection: A Systematic Review
Rahman, R. A., Omar, K., Noah, S. A. M., Danuri, M. S. N. M., & Al-Garadi, M. A. (2020) · IEEE Access · doi:10.1109/access.2020.3029154 · Cited by 195
Collective Emotions
Goldenberg, A., García, D., Halperin, E., & Gross, J. J. (2020) · Current Directions in Psychological Science · doi:10.1177/0963721420901574 · Cited by 187
Measuring User Engagement
Lalmas, M., O’Brien, H., & Yom-Tov, E. (2015) · Synthesis Lectures on Information Concepts, Retrieval, and Services · doi:10.1007/978-3-031-02289-0 · Cited by 192
Recognizing emotions in text using ensemble of classifiers
Perikos, I., & Hatzilygeroudis, I. (2016) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2016.01.012 · Cited by 176
Sentiment Analysis: From Opinion Mining to Human-Agent Interaction
Clavel, C., & Callejas, Z. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2444846 · Cited by 187
Refining Word Embeddings Using Intensity Scores for Sentiment Analysis
Yu, L.-C., Wang, J., Lai, K.-H. R., & Zhang, X. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2788182 · Cited by 177
Parallel Aspect‐Oriented Sentiment Analysis for Sales Forecasting with Big Data
Lau, R. Y. K., Zhang, W., & Xu, W. X. (2017) · Production and Operations Management · doi:10.1111/poms.12737 · Cited by 176
Social media content strategy for sport clubs to drive fan engagement
Annamalai, B., Yoshida, M., Varshney, S., Pathak, A. A., & Venugopal, P. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102648 · Cited by 201
What are the characteristics of high-rated apps? A case study on free Android Applications
Tian, Y., Nagappan, M., Lo, D., & Hassan, A. E. (2015) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsm.2015.7332476 · Cited by 170
A linguistic signaling model of social support exchange in online health communities
Chen, L., Baird, A. M., & Straub, D. W. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.113233 · Cited by 175
Big Data and the brave new world of social media research
Schroeder, R. (2014) · Big Data & Society · doi:10.1177/2053951714563194 · Cited by 167
On negative results when using sentiment analysis tools for software engineering research
Jongeling, R., Sarkar, P., Datta, S., & Serebrenik, A. (2017) · Empirical Software Engineering · doi:10.1007/s10664-016-9493-x · Cited by 172
Lexicon based feature extraction for emotion text classification
Bandhakavi, A., Wiratunga, N., Padmanabhan, D., & Massie, S. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.12.009 · Cited by 150
Three-way enhanced convolutional neural networks for sentence-level sentiment classification
Zhang, Y., Zhang, Z., Miao, D., & Wang, J. (2018) · Information Sciences · doi:10.1016/j.ins.2018.10.030 · Cited by 159
Modelling Valence and Arousal in Facebook posts
Preotiuc-Pietro, D., Schwartz, H. A., Park, G. J., Eichstaedt, J., Kern, M. L., Ungar, L., & Shulman, E. (2016) · WASSA@NAACL-HLT · doi:10.18653/v1/w16-0404 · Cited by 187
Investigating the Healthiness of Internet-Sourced Recipes
Trattner, C., & Elsweiler, D. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052573 · Cited by 156
Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter
Abid, F., Alam, M., Yasir, M., & Chen, L. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2018.12.018 · Cited by 158
Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs
Koto, F., & Rahmaningtyas, G. Y. (2017) · International Conference on Asian Language Processing · doi:10.1109/ialp.2017.8300625 · Cited by 153
A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks
Dashtipour, K. K., Gogate, M., Li, J., Jiang, F., Kong, B., & Hussain, A. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.009 · Cited by 150
Fostering Participant Health Knowledge and Attitudes: An Econometric Study of a Chronic Disease-Focused Online Health Community
Chen, L., Baird, A. M., & Straub, D. W. (2019) · Journal of Management Information Systems · doi:10.1080/07421222.2018.1550547 · Cited by 149
Context-Based Feature Technique for Sarcasm Identification in Benchmark Datasets Using Deep Learning and BERT Model
Eke, C. I., Norman, A. A., & Shuib, L. M. (2021) · IEEE Access · doi:10.1109/access.2021.3068323 · Cited by 156
Social media management strategies for organizational impression management and their effect on public perception
Benthaus, J., Risius, M., & Beck, R. (2016) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2015.12.001 · Cited by 143
Social media analytics for YouTube comments: potential and limitations
Thelwall, M. A. (2017) · International Journal of Social Research Methodology · doi:10.1080/13645579.2017.1381821 · Cited by 143
Self-citation
When Can Social Media Lead Financial Markets?
Zheludev, I., Smith, R. E., & Aste, T. (2014) · Scientific Reports · doi:10.1038/srep04213 · Cited by 144
Using natural language processing to analyse text data in behavioural science
Feuerriegel, S., Maarouf, A., Bär, D., Geissler, D., Schweisthal, J., Pröllochs, N., Robertson, C. E., Rathje, S., Hartmann, J., Mohammad, S. M., et al. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-024-00392-z · Cited by 140
Social Collaborative Viewpoint Regression with Explainable Recommendations
Ren, Z., Liang, S., Li, P., Wang, S., & de Rijke, M. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018686 · Cited by 140
Contextual sentiment analysis for social media genres
Muhammad, A., Wiratunga, N., & Lothian, R. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.05.032 · Cited by 138
Over a decade of social opinion mining: a systematic review
Cortis, K., & Davis, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10030-2 · Cited by 151
Topic‐based sentiment analysis for the social web: The role of mood and issue‐related words
Thelwall, M. A., & Buckley, K. A. (2013) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.22872 · Cited by 140
Self-citation
Visualizing theme park visitors’ emotions using social media analytics and geospatial analytics
Park, S. B., Kim, J., Lee, Y. K., & Ok, C. “. (2020) · Tourism Management · doi:10.1016/j.tourman.2020.104127 · Cited by 139
Using Twitter trust network for stock market analysis
Ruan, Y., Arjan Durresi, A., & Alfantoukh, L. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.01.016 · Cited by 133
User engagement in online N ews: Under the scope of sentiment, interest, affect, and gaze
Arapakis, I., Lalmas, M., Cambazoglu, B. B., Marcos, M., & Jose, J. M. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23096 · Cited by 131
How to ask for technical help? Evidence-based guidelines for writing questions on Stack Overflow
Calefato, F., Lanubile, F., & Novielli, N. (2017) · Information and Software Technology · doi:10.1016/j.infsof.2017.10.009 · Cited by 133
‘Going negative’: An APPRAISAL analysis of the rhetoric of Donald Trump on Twitter
Ross, A. S., & Caldwell, D. (2019) · Language & Communication · doi:10.1016/j.langcom.2019.09.003 · Cited by 130
Combining strengths, emotions and polarities for boosting Twitter sentiment analysis
Bravo-Márquez, F., Mendoza, M., & Poblete, B. (2013) · International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) · doi:10.1145/2502069.2502071 · Cited by 129
Emotional persistence in online chatting communities
Garas, A., García, D., Skowron, M., & Schweitzer, F. (2012) · Scientific Reports · doi:10.1038/srep00402 · Cited by 128
Making sense of comments on YouTube educational videos: a self-directed learning perspective
Lee, C. S., Osop, H., Goh, D. H., & Kelni, G. (2017) · Online Information Review · doi:10.1108/oir-09-2016-0274 · Cited by 127
A topic-enhanced word embedding for Twitter sentiment classification
Ren, Y., Wang, R., & Ji, D. (2016) · Information Sciences · doi:10.1016/j.ins.2016.06.040 · Cited by 128
EmoTxt: A toolkit for emotion recognition from text
Calefato, F., Lanubile, F., & Novielli, N. (2017) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw.2017.8272591 · Cited by 123
Valence-based homophily on Twitter: Network Analysis of Emotions and Political Talk in the 2012 Presidential Election
Himelboim, I., Sweetser, K. D., Tinkham, S. F., Cameron, K., Danelo, M., & West, K. E. (2014) · New Media & Society · doi:10.1177/1461444814555096 · Cited by 122
Cyberbullying severity detection: A machine learning approach
Talpur, B. A., & O’Sullivan, D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240924 · Cited by 122
Application of social media analytics: a case of analyzing online hotel reviews
He, W., Tian, X., Tao, R., Zhang, W., Yan, G., & Akula, V. (2017) · Online Information Review · doi:10.1108/oir-07-2016-0201 · Cited by 128
SentiStrength-SE: Exploiting domain specificity for improved sentiment analysis in software engineering text
Islam, S., & Zibran, M. F. (2018) · Journal of Systems and Software · doi:10.1016/j.jss.2018.08.030 · Cited by 121
Social emotion classification of short text via topic-level maximum entropy model
Rao, Y., Xie, H., Li, J., Jin, F., Wang, F. L., & Li, Q. (2016) · Information & Management · doi:10.1016/j.im.2016.04.005 · Cited by 120
Leveraging Automated Sentiment Analysis in Software Engineering
Islam, M. R., & Zibran, M. F. (2017) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2017.9 · Cited by 114
SentiTurkNet: a Turkish polarity lexicon for sentiment analysis
Dehkharghani, R., Saygın, Y., Yanikoglu, B. A., & Oflazer, K. (2015) · Language Resources and Evaluation · doi:10.1007/s10579-015-9307-6 · Cited by 112
Company event popularity for financial markets using Twitter and sentiment analysis
Daniel, M., Neves, R. F., & Horta, N. C. G. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.11.022 · Cited by 111

Authors: Thelwall, M., Buckley, K., & Paltoglou, G.
Affiliations: University of Wolverhampton (GB)
ORCID: Thelwall, Mike
Venue: Journal of the American Society for Information Science and Technology
Publisher: Wiley
Published: 2011-10-13
Volume: 63
Issue: 1
Pages: 163-173
ISSN: 1532-2882 (ISSN-L)
1532-2890
DOI: 10.1002/asi.21662
OpenAlex: W1987425720
Cited by: 1,062
References: 65
Citations by year: 2026: 18 · 2025: 40 · 2024: 35 · 2023: 60 · 2022: 64 · 2021: 90 · 2020: 99 · 2019: 94 · 2018: 123 · 2017: 108 · 2016: 101 · 2015: 96 · 2014: 64 · 2013: 50 · 2012: 19
FWCI: 39.19
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Journal of the American Society for Information Science and Technology (publishedVersion)
Funders: Seventh Framework Programme
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: SentiStrength, Web 2.0, Twitter, unsupervised sentiment analysis, online forums, user-generated content

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

Sentiment strength detection for the social web

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks