Text Classification Algorithms: A Survey
By Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura E. Barnes, Donald E. Brown. 2019
1,537 citations recorded by the source corpus.
In recent years, there has been an exponential growth in the number of complex documentsand texts that require a deeper understanding of machine learning methods to be able to accuratelyclassify texts in many applications. Many machine learning approaches have achieved surpassingresults in natural language processing. The success of these learning algorithms relies on their capacityto understand complex models and non-linear relationships within data. However, finding suitablestructures, architectures, and techniques for text classification is a challenge for researchers. In thispaper, a brief overview of text classification algorithms is discussed. This overview covers differenttext feature extractions, dimensionality reduction methods, existing algorithms and techniques, andevaluations methods. Finally, the limitations of each technique and their application in real-worldproblems are discussed.
Subfields: Artificial Intelligence, Information Systems
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Citations in the corpus
- Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System (2021, neutral): “Text classification has achieved great success in the past decades with the development of deep learning techniques (Kowsari et al., 2019; Li et al., 2020).”
- A Survey on Text Classification: From Shallow to Deep Learning (2020, methodology): “In the literature, Kowsari et al. [13] surveyed different text feature extraction, dimensionality reduction methods, basic model structure for text classification, and evaluation methods.”
- Deep Learning--based Text Classification (2020, background): “, [19–21], this survey is unique in that it presents a comprehensive review on more than 150 deep learning models developed for various text classification tasks, including sentiment analysis, news categorization, topic classification, question answering (QA), and natural language inference (NLI), o”
- A Survey on Text Classification: From Traditional to Deep Learning (2020, methodology): “[12] surveyed different text feature extraction, dimensionality reduction methods, basic model structure for text classification, and evaluation methods.”
- COVID-19 Public Sentiment Insights and MachineLearning for Tweets Classification (2020, methodology): “, does not construct a general internal model but just stores an instance of the data) [37,39].”
- Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification (2021, background): “, 2021) due to their superior performance on a wide range of languagerelated downstream tasks such as text classification (Kowsari et al., 2019), question answering (Rajpurkar et al.”
- NewsEmbed: Modeling News through Pre-trained Document Representations (2021, methodology): “With the development of deep learning, convolutional neural networks [62], recurrent neural networks [33, 64], and transformer [55] are used to represent contextual information [39, 48].”
- Pre-trained Language Models Can be Fully Zero-Shot Learners (2022, background): “By fine-tuning on taskspecific labeled data, PLMs exploit such knowledge and gain impressive accuracy on a wide range of NLP tasks, such as text classification (Kowsari et al., 2019), question answering (Rajpurkar et al.”
- Text Graph Transformer for Document Classification (2020, background): “Text classification is a widely studied problem in natural language processing and has been addressed in many real-world applications such as news filtering, spam detection, and health record systems (Kowsari et al., 2019; Che et al., 2015; Zhang et al., 2018).”