The DOI matches its Crossref record; 194 of the 194 references it cites were found in a registry or an index.
A survey on classification techniques for opinion mining and sentiment analysis
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
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Baeza-Yates, R. A., & Ribeiro-Neto, B. A. (1999) · Cited by 11,505
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0026683 · Cited by 7,999
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
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
Research works printed but not linked: 50
- Ahmed S, Danti A (2016) Effective sentimental analysis and opinion mining of web reviews using rule based classifiers. In: Behera H, Mohapatra D (eds) Computational intelligence in data mining—volume 1. Advances in intelligent systems and computing, vol 410. Springer, New Delhi
- Arab M, Sohrabi MK (2017) Proposing a new clustering method to detect phishing websites. Turk J Electr Eng Comput Sci. https://doi.org/10.3906/elk-1612-279 · doi:10.3906/elk-1612-279
- Bastı E, Kuzey C, Delen D (2015) Analyzing initial public offerings’ short-term performance using decision trees and SVMs. Decis Support Syst 73:15–27 · doi:10.1016/j.dss.2015.02.011
- Carter D, Inkpen D (2015) Inferring aspect-specific opinion structure in product reviews using co-training. In: Gelbukh A · doi:10.1007/978-3-319-18117-2_17
- (ed) Computational linguistics and intelligent text processing. CICLing 2015. Lecture notes in computer science, vol 9042. Springer, Cham, pp 225-240
- Claypo N, Jaiyen S (2015) Opinion mining for thai restaurant reviews using K-means clustering and MRF feature selection. In: 7th international conference on knowledge and smart technology (KST), pp 105–108 · doi:10.1109/kst.2015.7051469
- De A, Kopparapu SK (2013) Unsupervised clustering technique to harness ideas from an Ideas Portal. In: International IEEE conference on advances in computing, communications and informatics (ICACCI), pp 1563–1568 · doi:10.1109/icacci.2013.6637413
- Feng S, Song K, Wang D, Yu G (2015) A word-emoticon mutual reinforcement ranking model for building sentiment lexicon from massive collection of microblogs. World Wide Web 18(4):949–967 · doi:10.1007/s11280-014-0289-x
- Ficamos P, Liu Y, Chen W (2017) A Naive Bayes and maximum entropy approach to sentiment analysis: capturing domain-specific data in Weibo. In: IEEE international conference on big data and smart computing (BigComp), pp 336–339 · doi:10.1109/bigcomp.2017.7881689
- Gao W, Li S, Xue Y, Wang M, Zhou G (2014) Semi-supervised sentiment classification with self-training on feature subspaces. In: Su X, He T (eds) Chinese Lexical Semantics. CLSW 2014. Lecture notes in computer science, vol 8922. Springer, Cham, pp 231–239 · doi:10.1007/978-3-319-14331-6_23
- Goel A, Gautam J, Kumar S (2016) Real time sentiment analysis of tweets using Naive Bayes. In: 2nd international conference on next generation computing technologies (NGCT), pp 257–261 · doi:10.1109/ngct.2016.7877424
- Grefenstette G, Qu Y, Shanahan JG, Evans DA (2004) Coupling niche browsers and affect analysis for an opinion mining application. In: Proceedings of RIAO ’04 Coupling approaches, coupling media and coupling languages for information retrieval, pp 186–194
- Gupta E, Rathee G, Kumar P, Chauhan DS (2015) Mood swing analyser: a dynamic sentiment detection approach. Proc Natl Acad Sci India Sect A Phys Sci 85(1):149–157 · doi:10.1007/s40010-014-0169-x
- Hasan KMA, Sabuj MS, Afrin Z (2015) Opinion mining using Naïve Bayes. In: IEEE International WIE conference on electrical and computer engineering (WIECON-ECE) · doi:10.1109/wiecon-ece.2015.7443981
- He Y, Zhou D (2011) Self-training from labeled features for sentiment analysis. Inf Process Manag 47(4):606–616 · doi:10.1016/j.ipm.2010.11.003
- Jain A, Jain M (2017) Location based Twitter opinion mining using common-sense information. Glob J Enterp Inf Syst 9(2):28–32
- Jeyapriya A, Selvi K (2015) Extracting aspects and mining opinions in product reviews using supervised learning algorithm. In: 2015 2nd international conference on electronics and communication systems (ICECS). IEEE, pp 548–552 · doi:10.1109/ecs.2015.7124967
- Jiménez-Zafra SM, Martín-Valdivia MT, Martínez-Cámara E, Ureña-López LA (2015) Combining resources to improve unsupervised sentiment analysis at aspect-level. J Inf Sci 42:213–229 · doi:10.1177/0165551515593686
- Keshtkar F, Inkpen D (2013) A bootstrapping method for extracting paraphrases of emotion expressions from texts. Comput Intell 29(3):417–435 · doi:10.1111/j.1467-8640.2012.00458.x
- Kisioglu P, Topcu YI (2011) Applying Bayesian belief network approach to customer churn analysis: a case study on the telecom industry of Turkey. Expert Syst Appl 38(6):7151–7157 · doi:10.1016/j.eswa.2010.12.045
- Kobayashi N, Inui K, Matsumoto Y (2007) Extracting aspect-evaluation and aspect-of relations in opinion mining. In: EMNLP-CoNLL, vol 7, pp 1065–1074
- Liu J, Seneff S, Zue V (2012) Harvesting and summarizing user-generated content for advanced speech-based HCI. IEEE J Sel Top Signal Process 6(8):982–992 · doi:10.1109/jstsp.2012.2229690
- Liu S, Li F, Li F, Cheng X, Shen H (2013a) Adaptive co-training SVM for sentiment classification on tweets. In: Proceedings of the 22nd ACM international conference on conference on information & knowledge management. ACM, pp 2079–2088 · doi:10.1145/2505515.2505569
- Liu S, Zhu W, Xu N, Li F, Cheng XQ, Liu Y, Wang Y (2013b) Co-training and visualizing sentiment evolvement for tweet events. In: Proceedings of the 22nd international conference on World Wide Web companion. International World Wide Web Conferences Steering Committee, pp 105–106 · doi:10.1145/2487788.2487836
- Lu TJ (2015) Semi-supervised microblog sentiment analysis using social relation and text similarity. In: 2015 international conference on big data and smart computing (BigComp). IEEE, pp 194–201
- Luo W, Zhuang F, Zhao W, He Q, Shi Z (2015) QPLSA: utilizing quad-tuples for aspect identification and rating. Inf Process Manag 51(1):25–41 · doi:10.1016/j.ipm.2014.08.004
- Ma H, Jia M, Zhang D, Lin X (2017) Combining tag correlation and user social relation for microblog recommendation. Inf Sci 385–386:325–337 · doi:10.1016/j.ins.2016.12.047
- Manek AS, Shenoy PD, Mohan MC, Venougopal KR (2017) Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web 20(2):135–154 · doi:10.1007/s11280-015-0381-x
- Mele I (2013) Web usage mining for enhancing search-result delivery and helping users to find interesting web content. In: Proceedings of the sixth ACM international conference on Web search and data mining. ACM, pp 765–770 · doi:10.1145/2433396.2433493
- Parveen H, Pandey S (2016) Sentiment analysis on Twitter data-set using Naive Bayes algorithm. In: 2nd international conference on applied and theoretical computing and communication technology (iCATccT), pp 416–419 · doi:10.1109/icatcct.2016.7912034
- Phu VN, Dat ND, Tran VTN, Chau VTN, Nguyen TA (2017) Fuzzy C-means for english sentiment classification in a distributed system. Appl Intell 46(3):717–738 · doi:10.1007/s10489-016-0858-z
- Rathan M, Hulipalled VR, Venugopal KR, Patnaik LM (2017) Consumer insight mining: aspect based Twitter opinion mining of mobile phone reviews. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2017.07.056 · doi:10.1016/j.asoc.2017.07.056
- Ren F, Kang X (2013) Employing hierarchical Bayesian networks in simple and complex emotion topic analysis. Comput Speech Lang 27(4):943–968 · doi:10.1016/j.csl.2012.07.012
- Shi B, Chang K (2008) Generating a concept hierarchy for sentiment analysis. In: IEEE international conference on systems, man and cybernetics, SMC 2008. IEEE, pp 312–317
- Sisodia DS, Verma S (2012) Web usage pattern analysis through web logs: a review. In: 2012 international joint conference on computer science and software engineering (JCSSE). IEEE, pp 49–53
- Sohrabi MK (2018) A gossip-based information fusion protocol for distributed frequent itemset mining. Enterp Inf Syst. https://doi.org/10.1080/17517575.2017.1405286 · doi:10.1080/17517575.2017.1405286
- Sohrabi MK, Azgomi H (2017b) TSGV: a table-like structure based greedy method for materialized view selection in data warehouse. Turk J Electr Eng Comput Sci 25(4):3175–3187 · doi:10.3906/elk-1608-112
- Sohrabi MK, Ghods V (2014) Top-down vertical itemset mining. In: Proceedings of the SPIE 9443 sixth international conference on graphic and image processing · doi:10.1117/12.2179150
- Sohrabi MK, Ghods V (2015) Top- materialized view selection for a data warehouse using frequent itemset mining. In: Proceedings of the ICACTE conference, Berlin, Germany
- Sohrabi MK, Ghods V (2016) CUSE: a novel cube-based approach for sequential pattern mining. In: Proceedings of the IEEE international symposium on computational business intelligence, Olten, Switzerland · doi:10.1109/iscbi.2016.7743281
- Sohrabi MK, Marzooni HH (2016) Association rule mining using new FP-linked list algorithm. J Adv Comput Res 7(01):23–34
- Tsagkalidou K, Koutsonikola V, Vakali A, Kafetsios K (2011) Emotional aware clustering on micro-blogging sources. In: D’Mello S, Graesser A, Schuller B, Martin JC (eds) Affective computing and intelligent interaction. ACII 2011. Lecture notes in computer science, vol 6974. Springer, Berlin, pp 387–396 · doi:10.1007/978-3-642-24600-5_42
- Vakali A, Kafetsios K (2012) Emotion aware clustering analysis as a tool for Web 2.0 communities detection: implications for curriculum development. In: World Wide Web Conference. WWW
- Wang J, Xue Y, Li S, Zhou G (2015c) Leveraging interactive knowledge and unlabeled data in gender classification with co-training. In: Liu A, Ishikawa Y, Qian T, Nutanong S, Cheema M (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture notes in computer science, vol 9052. Springer, Cham, pp 246–251 · doi:10.1007/978-3-319-22324-7_23
- Xia Y, Cambria E, Hussain A (2015) AspNet: aspect extraction by bootstrapping generalization and propagation using an aspect network. Cogn Comput 7(2):241–253 · doi:10.1007/s12559-014-9305-9
- Xu L, Lin J, Wang L, Yin C, Wang J (2017) Deep convolutional neural network based approach for aspect-based sentiment analysis. Adv Sci Technol Lett 143:199–204 · doi:10.14257/astl.2017.143.41
- Yin PY, Guo YM (2013) Optimization of multi-criteria website structure based on enhanced tabu search and web usage mining. Appl Math Comput 219(24):11082–11095
- Zhang X, Gong W, Kawamura Y (2004) Customer behavior pattern discovering with web mining. In: Yu JX, Lin X, Lu H, Zhang Y (eds) Advanced web technologies and applications. APWeb, Lecture notes in computer science, vol 3007. Springer, Berlin, pp 844–853 · doi:10.1007/978-3-540-24655-8_92
- Zimmermann M, Ntoutsi E, Spiliopoulou M (2016) Extracting opinionated (sub) features from a stream of product reviews using accumulated novelty and internal re-organization. Inf Sci 329:876–899 · doi:10.1016/j.ins.2015.06.050
- Zimmermann M, Ntoutsi E, Spiliopoulou M (2014) A semi-supervised self-adaptive classifier over opinionated streams. In: 2014 IEEE international conference on data mining workshop (ICDMW). IEEE, pp 425–432 · doi:10.1109/icdmw.2014.106
Cited by
Sentiment analysis using deep learning architectures: a review
Yadav, A., & Vishwakarma, D. K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09794-5 · Cited by 841
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00765-8 · Cited by 570
Systematic reviews in sentiment analysis: a tertiary study
Ligthart, A., Catal, C., & Tekinerdogan, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09973-3 · Cited by 303
Fuzzy logic applied to opinion mining: A review
Serrano-Guerrero, J., Romero, F. P., & Olivas, J. A. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107018 · Cited by 141
Metadata
- Authors
- Hemmatian, F., & Sohrabi, M. K.
- Venue
- Artificial Intelligence Review
- Published
- 2017-12-18
- Volume
- 52
- Issue
- 3
- Pages
- 1495-1545
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2779594574
- Cited by
- 371
- References
- 194
- FWCI
- 12.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A survey on classification techniques for opinion mining and sentiment analysis. https://app.cochat.ai/papers/10.1007/s10462-017-9599-6
- The paper
- Hemmatian, F., & Sohrabi, M. K. (2019). A survey on classification techniques for opinion mining and sentiment analysis. Artificial Intelligence Review. https://doi.org/10.1007/s10462-017-9599-6
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.