# Twenty Years of Machine-Learning-Based Text Classification: A Systematic Review
## 1 Introduction  
Machine learning models provide the best alternative to traditional methods in the field of text classification. Text-classification-based research has been conducted extensively in recent years to improve the performance of machine learning models [1]. Text classification can be done manually, but it is time-consuming and has a high cost. Manual classification comes with lots of errors and is less accurate because of human error and a lack of domain knowledge understanding. There was a huge revolution in text classification when machine learning models, such as the Support Vector Machines (SVM), Naive Bayes (NB), and Random Forest (RF) started to replace manual work, because these models not only reduce the time and cost but also are highly accurate for classification [2]. Since the inception of machine learning models, numerous studies have been conducted to enhance, optimize, and refine the text classification process. With the recent increase in demand for various Natural Language Processing (NLP) technologies, such as chatbots [3], content classification [4], Sentiment Analysis [5; 6; 7], hate speech detection [8; 9], authorship recognition and attribution [10], product and service recommenders [11; 12], text summarization [13; 14], email spam detection [15] and phishing detection [16], intent detection [17], and search optimization [18], ML models have presented a huge advantage and have created many opportunities for researchers in the field of text classification. Text classification is done in four stages: (a) pre-processing, (b) text representation, (c) feature selection, and finally, (d) classification [19]. The first stage is pre-processing inwhich the input is cleaned and shaped according to the need for classification. The noise present in the input is removed [20]. Next, the input text is converted into a format, such as bag of words or n-gram. Feature selection is an optional step that involves identifying and picking important features. The size of the input is largely reduced at this stage [21]. Machine learning models have been applied to text classification and have produced promising results in various fields, such as finance [22], tourism [23], healthcare [24], and online news analysis [25]. This paper reviews how, when, and where these models have been successful in text classification. We studied the pros and cons of many models separately and determined how they perform in each dataset. We considered papers from 2003, and the findings of the paper will help the research community to improve the classification in the future. Furthermore, we present a few gaps noticed in the literature along with a summary of some interesting future works in the text classification domain. Previous survey papers on text classification [2; 26; 27; 28; 29; 30; 31] discussed various aspects of text classification, such as feature extraction techniques, algorithms, evaluation methods, and limitations. They also provided an overview of deep-learning-based text classification models, popular datasets, future research directions, and a comparison of different methodologies. The papers also highlighted the strengths and limitations of traditional text classification methods and suggested directions for future work. [PERSON] et al. [26] examined articles on text classification techniques in Artificial Intelligence (AI) written between 2010 and 2017 and grouped the techniques according to the algorithms involved. The results were visualized as a tree structure to show the relationship between learning procedures and algorithms, and the paper identified the strengths, limitations, and current research trends in text classification. [PERSON] et al. [31] presented an overview of the state-of-the-art of text classification by identifying and studying key and recent studies and objectives in this discipline. The paper covered six fundamental parts of text classification and analyzed the connected works qualitatively and quantitatively. [PERSON] et al.