The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Opinion Mining and Sentiment Analysis
Abstract
An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. Opinion Mining and Sentiment Analysis covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. The focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. The survey includes an enumeration of the various applications, a look at general challenges and discusses categorization, extraction and summarization. Finally, it moves beyond just the technical issues, devoting significant attention to the broader implications that the development of opinion-oriented information-access services have: questions of privacy, vulnerability to manipulation, and whether or not reviews can have measurable economic impact. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided. Opinion Mining and Sentiment Analysis is the first such comprehensive survey of this vibrant and important research area and will be of interest to anyone with an interest in opinion-oriented information-seeking systems.
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.
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
The Market for "Lemons": Quality Uncertainty and the Market Mechanism
Akerlof, G. A. (1970) · The Quarterly Journal of Economics · doi:10.2307/1879431 · Cited by 23,016
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003
The Role of Market Forces in Assuring Contractual Performance
Klein, B., & Leffler, K. B. (1981) · Journal of Political Economy · doi:10.1086/260996 · Cited by 3,653
The political blogosphere and the 2004 U.S. election: divided they blog
Adamic, L. A., & Glance, N. (2005) · Proceedings of the 3rd international workshop on Link discovery · doi:10.1145/1134271.1134277 · Cited by 2,619
Resnick, P., Kuwabara, K., Zeckhauser, R., & Friedman, E. (2000) · Communications of the ACM · doi:10.1145/355112.355122 · Cited by 2,190
Jindal, N., & Liu, B. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341560 · Cited by 1,542
Extracting Policy Positions from Political Texts Using Words as Data
LAVER, M., BENOIT, K., & GARRY, J. (2003) · American Political Science Review · doi:10.1017/s0003055403000698 · Cited by 1,377
The value of reputation on eBay: A controlled experiment
Resnick, P., Zeckhauser, R., Swanson, J., & Lockwood, K. (2006) · Experimental Economics · doi:10.1007/s10683-006-4309-2 · Cited by 1,118
Cited by
(2011) · Cited by 6,993
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
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
Beyond the hype: Big data concepts, methods, and analytics
Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226
A review on the attention mechanism of deep learning
Niu, Z., Zhong, G., & Yu, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.091 · Cited by 3,379
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
What Makes Online Content Viral?
Berger, J., & Milkman, K. L. (2012) · Journal of Marketing Research · doi:10.1509/jmr.10.0353 · Cited by 3,178
Predicting the future with social media
Asur, S., & Huberman, B. A. (2010) · 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology · doi:10.1109/wi-iat.2010.63 · Cited by 2,081
Tensor Fusion Network for Multimodal Sentiment Analysis
Zadeh, A., Chen, M., Poria, S., Cambria, E., & Morency, L.-P. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1115 · Cited by 2,102
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893
Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior
Stieglitz, S., & Dang-Xuan, L. (2013) · Journal of Management Information Systems · doi:10.2753/mis0742-1222290408 · Cited by 1,604
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
Sentiment strength detection for the social web
Thelwall, M., Buckley, K., & Paltoglou, G. (2012) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21662 · Cited by 1,062
What can big data and text analytics tell us about hotel guest experience and satisfaction?
Xiang, Z., Schwartz, Z., Gerdes, J. H., & Uysal, M. (2015) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2014.10.013 · Cited by 953
Large-scale visual sentiment ontology and detectors using adjective noun pairs
Borth, D., Ji, R., Chen, T., Breuel, T., & Chang, S.-F. (2013) · Proceedings of the 21st ACM international conference on Multimedia · doi:10.1145/2502081.2502282 · Cited by 851
Processing Social Media Messages in Mass Emergency: A Survey
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015) · ACM Computing Surveys · doi:10.1145/2771588 · Cited by 804
Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776
A Method of Automated Nonparametric Content Analysis for Social Science
Hopkins, D. J., & King, G. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2009.00428.x · Cited by 765
(2011) · Cited by 724
Ensemble of feature sets and classification algorithms for sentiment classification
Xia, R., Zong, C., & Li, S. (2011) · Information Sciences · doi:10.1016/j.ins.2010.11.023 · Cited by 583
"I regretted the minute I pressed share": a qualitative study of regrets on Facebook
Wang, Y., Norcie, G., Komanduri, S., Acquisti, A., Leon, P. G., & Cranor, L. F. (2011) · Proceedings of the Seventh Symposium on Usable Privacy and Security · doi:10.1145/2078827.2078841 · Cited by 479
Chae, B. (. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.12.037 · Cited by 481
A review of verbal and non-verbal human–robot interactive communication
Mavridis, N. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.09.031 · Cited by 453
Stance and Sentiment in Tweets
Mohammad, S. M., Sobhani, P., & Kiritchenko, S. (2017) · ACM Transactions on Internet Technology · doi:10.1145/3003433 · Cited by 449
Survey on mining subjective data on the web
Tsytsarau, M., & Palpanas, T. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-011-0238-6 · Cited by 443
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality
Yu, W., Xu, H., Meng, F., Zhu, Y., Ma, Y., Wu, J., Zou, J., & Yang, K. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.343 · Cited by 482
Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies
Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0388-3 · Cited by 434
The Effects of Twitter Sentiment on Stock Price Returns
Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138441 · Cited by 395
Big Data: Deep Learning for financial sentiment analysis
Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018) · Journal of Big Data · doi:10.1186/s40537-017-0111-6 · Cited by 390
Large language models (LLMs): survey, technical frameworks, and future challenges
Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395
Mohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391
Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing
Vinciarelli, A., Pantic, M., Heylen, D., Pelachaud, C., Poggi, I., D'Errico, F., & Schroeder, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.27 · Cited by 381
Are they different? Affect, feeling, emotion, sentiment, and opinion detection in text
Munezero, M., Montero, C. S., Sutinen, E., & Pajunen, J. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2317187 · Cited by 374
Large-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges
Wilkerson, J., & Casas, A. (2017) · Annual Review of Political Science · doi:10.1146/annurev-polisci-052615-025542 · Cited by 300
Gaining customer experience insights that matter
McColl-Kennedy, J. R., Zaki, M., Lemon, K. N., Urmetzer, F., & Neely, A. (2019) · Journal of Service Research · doi:10.1177/1094670518812182 · Cited by 283
Value in Informational Capitalism and on the Internet
Arvidsson, A., & Colleoni, E. (2012) · The Information Society · doi:10.1080/01972243.2012.669449 · Cited by 281
Emotional Dynamics in the Age of Misinformation
Zollo, F., Novak, P. K., Del Vicario, M., Bessi, A., Mozetič, I., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0138740 · Cited by 273
Explaining Data-Driven Document Classifications1
Martens, D., & Provost, F. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.1.04 · Cited by 264
Search engine marketing is not all gold: Insights from Twitter and SEOClerks
Aswani, R., Kar, A. K., Ilavarasan, P. V., & Dwivedi, Y. K. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.07.005 · Cited by 262
Dynabench: Rethinking Benchmarking in NLP
Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.324 · Cited by 254
Public microblogging on climate change: One year of Twitter worldwide
Kirilenko, A. P., & Stepchenkova, S. O. (2014) · Global Environmental Change · doi:10.1016/j.gloenvcha.2014.02.008 · Cited by 254
Fuzzy bag-of-words model for document representation
Zhao, R., & Mao, K. (2018) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2017.2690222 · Cited by 252
Villarroel Ordenes, F., Ludwig, S., de Ruyter, K., Grewal, D., & Wetzels, M. (2017) · Journal of Consumer Research · doi:10.1093/jcr/ucw070 · Cited by 245
Artificial Intelligence Techniques in Human Resource Management—A Conceptual Exploration
Strohmeier, S., & Piazza, F. (2015) · Intelligent Systems Reference Library · doi:10.1007/978-3-319-17906-3_7 · Cited by 224
Text mining for information systems researchers: An annotated topic modeling tutorial
University of Liechtenstein, Debortoli, S., Müller, O., IT University of Copenhagen, Junglas, I., Florida State University, vom Brocke, J., & University of Liechtenstein (2016) · Communications of the Association for Information Systems · doi:10.17705/1cais.03907 · Cited by 199
Measuring and Managing Consumer Sentiment in an Online Community Environment
Homburg, C., Ehm, L., & Artz, M. (2015) · Journal of Marketing Research · doi:10.1509/jmr.11.0448 · Cited by 196
The Evolution and Future of Retailing and Retailing Education
Grewal, D., Motyka, S., & Levy, M. (2018) · Journal of Marketing Education · doi:10.1177/0273475318755838 · Cited by 192
A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing
Liu, X., Singh, P. V., & Srinivasan, K. (2016) · Marketing Science · doi:10.1287/mksc.2015.0972 · Cited by 184
Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102447 · Cited by 171
Efficient Large-Scale Multi-Modal Classification
Kiela, D., Grave, E., Joulin, A., & Mikolov, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11945 · Cited by 177
The science of sharing and the sharing of science
Milkman, K. L., & Berger, J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317511111 · Cited by 154
Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2017.10.021 · Cited by 155
Tampering with Twitter’s Sample API
Pfeffer, J., Mayer, K., & Morstatter, F. (2018) · EPJ Data Science · doi:10.1140/epjds/s13688-018-0178-0 · Cited by 135
Addressing age-related bias in sentiment analysis,
Diaz, M., Johnson, I., Lazar, A., Piper, A. M., & Gergle, D. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173986 · Cited by 138
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
Classification of Customer Reviews based on Sentiment Analysis
Gräbner, D., Zanker, M., Fliedl, G., & Fuchs, M. (2012) · Information and Communication Technologies in Tourism 2012 · doi:10.1007/978-3-7091-1142-0_40 · Cited by 136
Bad News or Mad News? Sentiment Scoring of Negativity, Fear, and Anger in News Content
Soroka, S., Young, L., & Balmas, M. (2015) · The ANNALS of the American Academy of Political and Social Science · doi:10.1177/0002716215569217 · Cited by 128
Detecting Stance in Tweets And Analyzing its Interaction with Sentiment
Sobhani, P., Mohammad, S., & Kiritchenko, S. (2016) · Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s16-2021 · Cited by 130
Jiang, S., & Wilson, C. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274351 · Cited by 125
Toward human-centered algorithm design
Baumer, E. P. (2017) · Big Data & Society · doi:10.1177/2053951717718854 · Cited by 125
Sentiment analysis on tweets for social events
Zhou, X., Tao, X., Yong, J., & Yang, Z. (2013) · Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2013.6581022 · Cited by 124
Network Structure and Patterns of Information Diversity on Twitter1
Shore, J., Baek, J., & Dellarocas, C. (2018) · MIS Quarterly · doi:10.25300/misq/2018/14558 · Cited by 117
Lingiardi, V., Carone, N., Semeraro, G., Musto, C., D’Amico, M., & Brena, S. (2020) · Behaviour & Information Technology · doi:10.1080/0144929x.2019.1607903 · Cited by 113
Artificial intelligence for anti-money laundering: a review and extension
Han, J., Huang, Y., Liu, S., & Towey, K. (2020) · Digital Finance · doi:10.1007/s42521-020-00023-1 · Cited by 113
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables
Lakkaraju, H., Kleinberg, J., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098066 · Cited by 138
That is your evidence?: Classifying stance in online political debate
Walker, M. A., Anand, P., Abbott, R., Tree, J. E. F., Martell, C., & King, J. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.05.032 · Cited by 104
A survey on sentiment analysis of scientific citations
Yousif, A., Niu, Z., Tarus, J. K., & Ahmad, A. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-017-9597-8 · Cited by 104
General Sentiment: How Value and Affect Converge in the Information Economy
Arvidsson, A. (2011) · The Sociological Review · doi:10.1111/j.1467-954x.2012.02052.x · Cited by 103
Phrases that signal workplace hierarchy
Gilbert, E. (2012) · Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work · doi:10.1145/2145204.2145359 · Cited by 98
Analytical sociology and computational social science
Keuschnigg, M., Lovsjö, N., & Hedström, P. (2018) · Journal of Computational Social Science · doi:10.1007/s42001-017-0006-5 · Cited by 90
Authorship Attribution with Topic Models
Seroussi, Y., Zukerman, I., & Bohnert, F. (2014) · Computational Linguistics · doi:10.1162/coli_a_00173 · Cited by 89
Park, S., Ko, M., Kim, J., Liu, Y., & Song, J. (2011) · Proceedings of the ACM 2011 conference on Computer supported cooperative work · doi:10.1145/1958824.1958842 · Cited by 88
Systematic literature review on context-based sentiment analysis in social multimedia
Kumar, A., & Garg, G. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7346-5 · Cited by 86
Extractive Opinion Summarization in Quantized Transformer Spaces
Angelidis, S., Amplayo, R. K., Suhara, Y., Wang, X., & Lapata, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00366 · Cited by 85
Using Opinion Mining Techniques in Tourism
Bucur, C. (2015) · Procedia Economics and Finance · doi:10.1016/s2212-5671(15)00471-2 · Cited by 84
Gender bias in sentiment analysis
Thelwall, M. (2018) · Online Information Review · doi:10.1108/oir-05-2017-0139 · Cited by 78
Checking and bootstrapping lexical norms by means of word similarity indexes
Bestgen, Y., & Vincze, N. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0195-z · Cited by 75
Visualizing Student Opinion Through Text Analysis
Cunningham-Nelson, S., Baktashmotlagh, M., & Boles, W. (2019) · IEEE Transactions on Education · doi:10.1109/te.2019.2924385 · Cited by 75
Using rhetorical structure in sentiment analysis
Hogenboom, A., Frasincar, F., de Jong, F., & Kaymak, U. (2015) · Communications of the ACM · doi:10.1145/2699418 · Cited by 71
Raman, N., Cao, M., Tsvetkov, Y., Kästner, C., & Vasilescu, B. (2020) · Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: New Ideas and Emerging Results · doi:10.1145/3377816.3381732 · Cited by 70
Identifying helpful reviews based on customer’s mentions about experiences
Min, H.-J., & Park, J. C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.116 · Cited by 66
Using sentiment analysis to predict opinion inversion in Tweets of political communication
Matalon, Y., Magdaci, O., Almozlino, A., & Yamin, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-86510-w · Cited by 64
Aspect-based Sentiment Analysis of Scientific Reviews
Chakraborty, S., Goyal, P., & Mukherjee, A. (2020) · Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 · doi:10.1145/3383583.3398541 · Cited by 63
Review of Extracting Information From the Social Web for Health Personalization
Fernandez-Luque, L., Karlsen, R., & Bonander, J. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1432 · Cited by 61
Lai, M., Tambuscio, M., Patti, V., Ruffo, G., & Rosso, P. (2019) · Data & Knowledge Engineering · doi:10.1016/j.datak.2019.101738 · Cited by 61
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic
Badaro, G., El Jundi, O., Khaddaj, A., Maarouf, A., Kain, R., Hajj, H., & El-Hajj, W. (2018) · Proceedings of The 12th International Workshop on Semantic Evaluation · doi:10.18653/v1/s18-1036 · Cited by 60
Automating Risk of Bias Assessment for Clinical Trials
Marshall, I. J., Kuiper, J., & Wallace, B. C. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2431314 · Cited by 59
Kross, E., Verduyn, P., Boyer, M., Drake, B., Gainsburg, I., Vickers, B., Ybarra, O., & Jonides, J. (2019) · Emotion · doi:10.1037/emo0000416 · Cited by 54
DynaSent: A Dynamic Benchmark for Sentiment Analysis
Potts, C., Wu, Z., Geiger, A., & Kiela, D. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.186 · Cited by 56
(2012) · International Conference on Information Technology · Cited by 50
Hagendorff, T. (2021) · Minds and Machines · doi:10.1007/s11023-021-09573-8 · Cited by 47
Recommending products to customers using opinion mining of online product reviews and features
Rajeev, P. V., & Rekha, V. S. (2015) · 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015] · doi:10.1109/iccpct.2015.7159433 · Cited by 46
Tourism, travel and tweets: algorithmic text analysis methodologies in tourism
Claster, W., Pardo, P., Cooper, M., & Tajeddini, K. (2013) · Middle East J. of Management · doi:10.1504/mejm.2013.054071 · Cited by 45
Feature-Based Visual Sentiment Analysis of Text Document Streams
Rohrdantz, C., Hao, M. C., Dayal, U., Haug, L.-E., & Keim, D. A. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2089094.2089102 · Cited by 43
Generating Product Descriptions from User Reviews
Novgorodov, S., Guy, I., Elad, G., & Radinsky, K. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313532 · Cited by 41
Data work in context: Value, risks, and governance
Foster, J., McLeod, J., Nolin, J., & Greifeneder, E. (2018) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24105 · Cited by 40
The application and comparison of web services for sentiment analysis in tourism
Gao, S., Hao, J., & Fu, Y. (2015) · 2015 12th International Conference on Service Systems and Service Management (ICSSSM) · doi:10.1109/icsssm.2015.7170341 · Cited by 41
RevMiner: an extractive interface for navigating reviews on a smartphone
Huang, J., Etzioni, O., Zettlemoyer, L., Clark, K., & Lee, C. (2012) · Proceedings of the 25th annual ACM symposium on User interface software and technology · doi:10.1145/2380116.2380120 · Cited by 37
Metadata
- Authors
- Pang, B., & Lee, L.
- Venue
- now publishers, Inc. eBooks
- Publisher
- now Publishers Inc
- Published
- 2008
- OpenAlex
- W2097726431
- Cited by
- 6,720
- References
- 13
- FWCI
- 88.52
- Type
- book
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.