Text Classification Algorithms: A Survey
Kamran Kowsari,Kiana Jafari Meimandi,Mojtaba Heidarysafa,Sanjana Mendu,Laura E. Barnes,Donald E. Brown +5 more
TLDR
An overview of text classification algorithms is discussed, which covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods.Abstract:
In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in the real-world problem are discussed.read more
Citations
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Deep Learning--based Text Classification: A Comprehensive Review
TL;DR: This paper provided a comprehensive review of more than 150 deep learning-based models for text classification developed in recent years, and discussed their technical contributions, similarities, and strengths, and provided a quantitative analysis of the performance of different deep learning models on popular benchmarks.
Combining Instance-Based Learning and Logistic Regression for Multilabel Classification.
Weiwei Cheng,Eyke Hüllermeier +1 more
TL;DR: This paper proposes a new approach to multilabel classification, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases, and allows one to capture interdependencies between labels and to combine model-based and similarity-based inference for multILabel classification.
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COVID-19 public sentiment insights and machine learning for tweets classification
Jim Samuel,G. G. Md. Nawaz Ali,Md. Mokhlesur Rahman,Md. Mokhlesur Rahman,Ek Esawi,Yana Samuel +5 more
TL;DR: Insight is provided into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations and two essential machine learning classification methods are provided.
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COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification
Jim Samuel,G. G. Md. Nawaz Ali,Md. Mokhlesur Rahman,Md. Mokhlesur Rahman,Ek Esawi,Yana Samuel +5 more
TL;DR: Insight is provided into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations and a methodological overview of two essential machine learning classification methods.
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A Survey on Text Classification: From Shallow to Deep Learning
TL;DR: A taxonomy for text classification according to the text involved and the models used for feature extraction and classification is created, dealing with both the technical developments and benchmark datasets that support tests of predictions.
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