Text Classification and Classifiers:A Survey

Korde, Vishakha; Mahender, C. Namrata

doi:10.5121/ijaia.2012.3208

Cited by 184 publications

(117 citation statements)

References 46 publications

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“…In some situations, defining a set of logical rules using knowledgeengineering techniques and based on expert opinions to classify documents helps to automate the classification task. Text classification could be divided into three categories: supervised text classification, unsupervised text classification, and semi-supervised text classification based on the learning principle followed by the data model (Korde & Mahender, 2012).…”

Section: Machine Learning Approachmentioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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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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Section: Machine Learning Approachmentioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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“…-Dataset I, P I , P II , P III and I 1, P 1 , P 2, P 3 We used the comparison based on document classification method [17]. Then we checked some methods classifying documents such as Naive Bayes Text Classification [2], Support Vector Machines [16], and Vector Space Model [9].…”

Section: Inputmentioning

confidence: 99%

“…Then we set up the following model to describe the encoding of every document and the creation of a vector for every encoded document [17]: …”

Section: Inputmentioning

confidence: 99%

Assessment Model for Language Learners’ Writing Practice (in Preparing for TOEFL iBT) Based on Comparing Structure, Vocabulary, and Identifying Discrepant Essays

Pham¹,

Nguyen²

2016

ijacsa

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Abstract-This study aims to investigate if learners of English can improve computer-assisted writing skills through the analysis of the data from the post test. In this study, the focus was given to intermediate-level students of English taking final writing tests (integrated and independent responses) in preparation for TOEFL iBT. We manually scored and categorized the students' writing responses into five-point levels for the data to make the software. The results of the study showed that the model could be suitable for computerized scoring for language instructors to grade in a fair and exact way and for students to improve their writing performance through practice on the computer.

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“…This increase of data on the Web has produced the need for methods to extract the required information from text documents, and therefore, generating unique difficulties for the text classification problem especially when considering applications requiring analysis of big data [2], [14].…”

Section: Introductionmentioning

confidence: 99%

Classifying and Segmenting Classical and Modern Standard Arabic using Minimum Cross-Entropy

Alkhazi¹,

Teahan²

2017

ijacsa

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Further experiments with the same corpora showed that a more accurate picture of the contents of the corpora was possible using the PPM-based segmentation method. Tag-based compression experiments (using tags produced by parts-of-speech Arabic taggers) also showed that the quality of the tagging (as measured by compression quality) is significantly affected when tagging either CA and MSA text. The conclusion is that NLP applications (such as taggers) should treat these texts separately and use different training data for each or process them differently.

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Text Classification and Classifiers:A Survey

Cited by 184 publications

References 46 publications

Text Classification Techniques: A Literature Review

Text Classification Techniques: A Literature Review

Assessment Model for Language Learners’ Writing Practice (in Preparing for TOEFL iBT) Based on Comparing Structure, Vocabulary, and Identifying Discrepant Essays

Classifying and Segmenting Classical and Modern Standard Arabic using Minimum Cross-Entropy

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