techniques for text classification literature review and current trends

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Volume 12, No 2, 2015

Techniques for text classification: Literature review and current trends

Rajni jindal, ruchika malhotra and abha jain.

Automated classification of text into predefined categories has always been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. This kind of web information, popularly known as the digital/electronic information is in the form of documents, conference material, publications, journals, editorials, web pages, e-mail etc. People largely access information from these online sources rather than being limited to archaic paper sources like books, magazines, newspapers etc. But the main problem is that this enormous information lacks organization which makes it difficult to manage. Text classification is recognized as one of the key techniques used for organizing such kind of digital data. In this paper we have studied the existing work in the area of text classification which will allow us to have a fair evaluation of the progress made in this field till date. We have investigated the papers to the best of our knowledge and have tried to summarize all existing information in a comprehensive and succinct manner. The studies have been summarized in a tabular form according to the publication year considering numerous key perspectives. The main emphasis is laid on various steps involved in text classification process viz. document representation methods, feature selection methods, data mining methods and the evaluation technique used by each study to carry out the results on a particular dataset.

Pages : 1-28

Keywords : Machine learning; Text classification; Feature selection; Bag-of-words; Vector space model

A Systematic Literature Review of Text Classification: Datasets and Methods

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Review on sentiment analysis for text classification techniques from 2010 to 2021

• 1199: Computational Intelligence Revolution in Multimedia Data Analytics and Business Management
• Published: 01 December 2022
• Volume 82 , pages 8137–8193, ( 2023 )

Cite this article

• Arif Ullah   ORCID: orcid.org/0000-0002-7740-2206 1 ,
• Sundas Naqeeb Khan 2 &
• Nazri Mohd Nawi 2  

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Progression in the popularity of social media activities had provided huge amount of data in the form of text that can immeasurably augment its specialty. This textual data offers a platform for the reviewers to share their comments about any product, service or event on social media. These types of discussions among the reviewers boost the demand and supply in business and industry field. Furthermore, for every passing day the textual data is also increasing in amount which makes data mining especially sentiment analysis or opinion mining, a research hungry area. This is mainly because of data is represented in the form of calculations about reviewers’ comments, assessment, attitudes, behavior and emotions to individual issues, events, topics, services and attributes. Previously, researchers focus on systems to recognize and categorize sentiments from the written material where opinions are extremely unstructured, assorted and classified. In this paper, authors try to presents a meticulous survey on sentiment analysis with classification, in which one hundred and forty three articles were reviewed regarding important activities, approaches, applications with multilingual and cross domain jobs. This systematic survey considers published literature during 2010-2021, organized based on machine learning, lexicon and hybrid approaches with multilingual and cross domain knowledge.

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Data availability

No specific Data are used because it is review papers all those paper which are study and used in this paper are cited in the paper.

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Ullah, A., Khan, S.N. & Nawi, N.M. Review on sentiment analysis for text classification techniques from 2010 to 2021. Multimed Tools Appl 82 , 8137–8193 (2023). https://doi.org/10.1007/s11042-022-14112-3

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Received : 01 October 2020

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Accepted : 25 October 2022

Published : 01 December 2022

Issue Date : March 2023

DOI : https://doi.org/10.1007/s11042-022-14112-3

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Text Classification Techniques: A Literature Review

Aim/Purpose The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining.

Background Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet.

Methodology For this study, various articles written between 2010 and 2017 on “text classification techniques in AI”, selected from leading journals of computer science, were analyzed. Each article was completely read. The research problems related to text classification techniques in the field of AI were identified and techniques were grouped according to the algorithms involved. These algorithms were divided based on the learning procedure used. Finally, the findings were plotted as a tree structure for visualizing the relationship between learning procedures and algorithms.

Contribution This paper identifies the strengths, limitations, and current research trends in text classification in an advanced field like AI. This knowledge is crucial for data scientists. They could utilize the findings of this study to devise customized data models. It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making.

Findings It has been found more important to study and understand the nature of data before proceeding into mining. The automation of text classification process is required, with the increasing amount of data and need for accuracy. Another interesting research opportunity lies in building intricate text data models with deep learning systems. It has the ability to execute complex Natural Language Processing (NLP) tasks with semantic requirements.

Recommendations for Practitioners Frame analysis, deception detection, narrative science where data expresses a story, healthcare applications to diagnose illnesses and conversation analysis are some of the recommendations suggested for practitioners.

Recommendation for Researchers Developing simpler algorithms in terms of coding and implementation, better approaches for knowledge distillation, multilingual text refining, domain knowledge integration, subjectivity detection, and contrastive viewpoint summarization are some of the areas that could be explored by researchers.

Impact on Society Text classification forms the base of data analytics and acts as the engine behind knowledge discovery. It supports state-of-the-art decision making, for example, predicting an event before it actually occurs, classifying a transaction as ‘Fraudulent’ etc. The results of this study could be used for developing applications dedicated to assisting decision making processes. These informed decisions will help to optimize resources and maximize benefits to the mankind.

Future Research In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery. The role of streaming data processing is still rarely explored when it comes to text classification.

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Techniques for text classification: Literature review and current trends

Automated classification of text into predefined categories has always been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. This kind of web information, popularly known as the digital/electronic information is in the form of documents, conference material, publications, journals, editorials, web pages, e-mail etc. People largely access information from these online sources rather than being limited to archaic paper sources like books, magazines, newspapers etc. But the main problem is that this enormous information lacks organization which makes it difficult to manage. Text classification is recognized as one of the key techniques used for organizing such kind of digital data. In this paper we have studied the existing work in the area of text classification which will allow us to have a fair evaluation of the progress made in this field till date. We have investigated the papers to the ...

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The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems. However, the complexity of natural languages and the extremely high dimensionality of the feature space of documents have made this classification problem very difficult. We investigate four different methods for document classification: the naive Bayes classifier, the nearest neighbour classifier, decision trees and a subspace method. These were applied to seven-class Yahoo news groups (business, entertainment, health, international, politics, sports and technology) individually and in combination. We studied three classifier combination approaches: simple voting, dynamic classifier selection and adaptive classifier combination. Our experimental results indicate that the naive Bayes classifier and the subspace method outperform the other two classifiers on our data sets. Combinations of multiple classifiers did not always improve the classification accuracy compared to the best individual classifier. Among the three different combination approaches, our adaptive classifier combination method introduced here performed the best. The best classification accuracy that we are able to achieve on this seven-class problem is approximately 83%, which is comparable to the performance of other similar studies. However, the classification problem considered here is more difficult because the pattern classes used in our experiments have a large overlap of words in their corresponding documents.

Khairullah Khan

IJFRCSCE Journal

–Text classification is used to classify the document of similar types. Text classification can be also performed under supervision i.e. it is an supervised leaning technique Text classification is a process in which documents are sorted spontaneously into different classes using predefined set. The main issue is that large scale of information lacks organization which makes it difficult to manage. Text classification is identified as one of the key methods used for recognizing such types of digital information. Text classification have various applications such as in information retrieval, natural language processing, automatic indexing, text filtering, image processing, etc. Text classification is also used to process the big data and it can also be used to predict the class labels for newly added data. Text classification is also being used in academic and industries to classify the unstructured data. There are various types of the text classification approaches such as decision tree, SVM, Naïve Bayes etc. In this survey paper, we have analysed the various text classification techniques such as decision tree, SVM, Naïve Bayes etc. These techniques have their individual set of advantages which make them suitable in almost all classification jobs. In this paper we have also analysed evaluation parameters such as F-measure, G-measure and accuracy used in various research works. .

IJRISE Journal

The printed transformation has seen a gigantic change in the accessibility of online data. Discovering data for pretty much any need has never been more programmed. Content arrangement (otherwise called content classification or point spotting) is the errand of naturally sorting an arrangement of archives into classifications from a predefined set. This assignment has a few applications, including computerized ordering of logical articles, recording licenses into patent indexes, particular spread of data to data purchasers, robotized populace of various leveled inventories of Web assets, spam separating, and recognizable proof of report class. Computerized content characterization is appealing in light of the fact that it liberates associations from the need of physically sorting out report bases, which can be excessively costly, or essentially not plausible since time is running short imperatives of the application or the quantity of records included. The exactness of present day content characterization frameworks equals that of prepared human experts, on account of a blend of information retrieval (IR) innovation and machine learning (ML) innovation. The point of this paper is to highlight the essential calculations that are utilized in content archives grouping, while in the meantime making familiarity with a portion of the fascinating difficulties that stay to be fathomed.

Malaysian Journal of Computer Science

Moe Htet Min

Due to the mass availability of textual data on Web, text classification (TC), classifying texts into predetermined sets becomes a spotlight for researchers. A number of TC applications have been proposed yet very few studies reported an overview of TC research area in a proper and systematic manner. This paper aims to provide an overview of TC research trends and gaps by structuring and analyzing research patterns, encountered problems and problem-solving methods in TC. In other words, this study highlights problem types, data sources, choice of language of text and types of applied techniques in TC. An intensive systematic study is conducted by applying guidelines proposed by Petersen and colleagues in 2007. In this paper, ninety-six literatures from five electronic databases from 2006 to 2017 were systematically reviewed and followed each and every step properly in accordance with systematic mapping study. Nine main problems in TC research area were identified and significant fin...

ACM Computing Surveys

Aidana Darkenova

— With the increasing availability of electronic documents and the rapid growth of the World Wide Web, the task of automatic categorization of documents became the key method for organizing the information and know-ledge discovery. Proper classification of e-documents, online news, blogs, e-mails and digital libraries need text mining, machine learning and natural language processing tech-niques to get meaningful knowledge. The aim of this paper is to highlight the important techniques and methodologies that are employed in text documents classification, while at the same time making awareness of some of the interesting challenges that remain to be solved, focused mainly on text representation and machine learning techniques. This paper provides a review of the theory and methods of document classification and text mining, focusing on the existing litera-ture.

International Journal of Computer Applications

Mukesh Zaveri

Web Intelligence

Xiaohui Tao

Text classification (a.k.a text categorisation) is an effective and efficient technology for information organisation and management. With the explosion of information resources on the Web and corporate intranets continues to increase, it has being become more and more important and has attracted wide attention from many different research fields. In the literature, many feature selection methods and classification algorithms have been proposed. It also has important applications in the real world. However, the dramatic increase in the availability of massive text data from various sources is creating a number of issues and challenges for text classification such as scalability issues. The purpose of this report is to give an overview of existing text classification technologies for building more reliable text classification applications, to propose a research direction for addressing the challenging problems in text mining.

IRJET Journal

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