A Survey of Text Classification Algorithms

By Charu C. Aggarwal, Chengxiang Zhai. 2012

1,038 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Liang Yao
2. Kang Liu
3. Jun Zhao
4. Chengsheng Mao
5. Philip S. Yu
6. Michalis Vazirgiannis
7. Yuan Luo
8. Yangqiu Song

In Information Systems

1. Chuan Shi
2. Jun Zhao
3. Fabrizio Silvestri
4. Qiang Yang
5. Yuan Luo
6. Michalis Vazirgiannis
7. Sujian Li
8. Sotiris Tasoulis

In Natural Language Processing

1. Liang Yao
2. Chengsheng Mao
3. Michalis Vazirgiannis
4. Jun Zhao
5. Kang Liu
6. Yuan Luo
7. Lianzhe Huang
8. Ves Stoyanov

Citations in the corpus

• Graph Convolutional Networks for Text Classification (2018, background): “There are numerous applications of text classification such as document organization, news filtering, spam detection, opinion mining, and computational phenotyping (Aggarwal and Zhai 2012; Zeng et al. 2018).”
• Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN (2018, methodology): “, extracting features beyond BOW) and feature selection to obtain good features for text classification [1].”
• Text Categorization as a Graph Classification Problem (2015, background): “We refer to (Sebastiani, 2002) for an in-depth review of the earliest works in the field and (Aggarwal and Zhai, 2012) for a survey of the more recent works that capitalize on additional metainformation.”
• Recurrent Convolutional Neural Networks for Text Classification (2015, background): “Text classification is an essential component in many applications, such as web searching, information filtering, and sentiment analysis (Aggarwal and Zhai 2012).”
• Annotating Twitter Data from Vulnerable Populations: Evaluating Disagreement Between Domain Experts and Graduate Student Annotators (2018, methodology): “In the context of machine learning, the annotation process typically involves assigning categories to items, which are then used to build computational models for detecting these categories [1, 9].”
• Recommender Systems (2016, neutral): “A recent text classification survey [21] discusses feature selection algorithms for the specific case of text data.”
• Preserving integrity in online social networks (2020, background): “Building on the long history of text classification research in the literature, many studies have been proposed to address different aspects in understanding text [11, 53, 65, 78].”
• CNN-RNN Based Intelligent Recommendation for Online Medical Pre-Diagnosis Support (2020, background): “automatic one is a key means in many natural language processing applications [43].”
• Convolutional Neural Networks for Toxic Comment Classification (2018, background): “Text classification is a classic topic for natural language processing and an essential component in many applications, such as web searching, information filtering, topic categorization and sentiment analysis [1].”
• Text Level Graph Neural Network for Text Classification (2019, background): “Text classification is a fundamental problem of natural language processing (NLP), which has lots of applications like SPAM detection, news filtering, and so on (Jindal and Liu, 2007; Aggarwal and Zhai, 2012).”
• Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification (2019, background): “Short text classification can be widely applied in many domains, ranging from sentiment analysis to news tagging/categorization and query intent classification (Aggarwal and Zhai, 2012; Meng et al., 2018).”
• Bayesian Naïve Bayes classifiers to text classification (2018, background): “Text classification [1] is known as the task of assigning one or more predefined categories to natural language documents.”