The DOI matches its Crossref record; 189 of the 189 references it cites were found in a registry or an index.
Text Mining in Big Data Analytics
Open accessAbstract
Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
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
Machine learning in automated text categorization
Sebastiani, F. (2002) · ACM Computing Surveys · doi:10.1145/505282.505283 · Cited by 7,926
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
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,720
Opinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372
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
Cited by
Lee, I., & Mangalaraj, G. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6010017 · Cited by 176
Automatic classification of literature in systematic reviews on food safety using machine learning
van den Bulk, L. M., Bouzembrak, Y., Gavai, A., Liu, N., van den Heuvel, L. J., & Marvin, H. J. P. (2022) · Current Research in Food Science · doi:10.1016/j.crfs.2021.12.010 · Cited by 38
“When it comes to text as input, a specialized part of machine learning called text mining has been on the rise (Gupta and Lehal, 2009; Talib et al., 2016; Hassani et al., 2020; Jung and Lee, 2020).”
Background
Metadata
- Authors
- Hassani, H., Beneki, C., Unger, S., Mazinani, M. T., & Yeganegi, M. R.
- Venue
- Big Data and Cognitive Computing
- Publisher
- MDPI AG
- Published
- 2020-01-16
- Volume
- 4
- Issue
- 1
- Pages
- 1
- ISSN
- 2504-2289 (ISSN-L)
- OpenAlex
- W3000466293
- Cited by
- 317
- References
- 189
- FWCI
- 113.09
- Type
- journal article
- Language
- en
- Open access
- gold
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- Hassani, H., Beneki, C., Unger, S., Mazinani, M. T., & Yeganegi, M. R. (2020). Text Mining in Big Data Analytics. Big Data and Cognitive Computing. https://doi.org/10.3390/bdcc4010001
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.
