A comprehensive study of text classification algorithms

Vijayan, Vikas K; Bindu, K. R.; Parameswaran, Latha

doi:10.1109/icacci.2017.8125990

Cited by 86 publications

(55 citation statements)

References 14 publications

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“…In [2,4] several algorithms of supervised classification are considered, such as Hotdeck, KNN, and Decision Trees. These methods require a labeled training set of data and they use similarity metrics to define the relations among the elements to classify.…”

Section: Related Workmentioning

confidence: 99%

“…These methods require a labeled training set of data and they use similarity metrics to define the relations among the elements to classify. Other popular techniques include mathematical models to approximate values and reduce errors based on training data, for example, Artificial Neural Networks and Support Vector Machines [4]. From all these algorithms, we considered the first group for this work since they were the best match for our problem by allowing the definition of similarity metrics.…”

Section: Related Workmentioning

confidence: 99%

“…Recent studies have evaluated text classification techniques mostly focused on classification algorithms [4] and distance measures [3]. There have also been works related to the comparison of preprocessing methods and document representations [15,14].…”

Section: Related Workmentioning

confidence: 99%

“…Under this context, this paper analyses the problem of text classification, where an algorithm classifies the observed text values within different categories. Existing literature proposes different machine learning and data mining algorithms to solve classification problems in a supervised way [2,3,4]. Algorithms such as K-Nearest-Neighbors (KNN), where text distance metrics can be used to classify elements, are relevant for documents classification problems [3,5].…”

Section: Introductionmentioning

confidence: 99%

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Evaluación de distintas técnicas de representación de texto y medidas de distancia de texto usando KNN para clasificación de documentos

Calvo-Valverde¹,

Mena-Arias²

2020

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Actualmente, los datos textuales constituyen una parte fundamental de las bases de datos de todo el mundo y uno de los mayores desafíos ha sido la extracción de información útil a partir de conjuntos grandes de documentos de texto. La literatura existente sobre métodos para resolver este problema es muy extensa, sin embargo, los métodos estadísticos (que utilizan métricas de similitud sobre vectores de palabras) han mostrado resultados muy favorables en el campo de la minería de texto durante los últimos 25 años. Adicionalmente, otros modelos han surgido como una prometedora alternativa para lograr reducción dimensional e incorporación de la semántica en la clasificación de documentos, tal como el modelado de temas. Este proyecto se enfoca en la evaluación de técnicas de representación y medidas de similitud de texto (Coseno, Jaccard y Kullback-Leibler) usando el algoritmo de Vecinos más Cercanos (KNN por sus siglas en inglés), con el fin de medir la efectividad del modelado de temas para reducción dimensional al clasificar texto. Los resultados muestran que la versión más tradicional del vector de palabras y la similitud Jaccard superaron al resto de las combinaciones en la mayoría de los casos de uso. Sin embargo, el análisis estadístico mostró que no hubo una diferencia significativa entre la exactitud obtenida al usar representaciones generadas por la Asignación de Dirichlet Latente (técnica de modelado de temas más conocida como LDA por sus siglas en inglés), y la obtenida usando técnicas tradicionales de clasificación de texto. LDA logró abstraer miles de palabras en menos de 60 temas para el primer conjunto de pruebas. Experimentos adicionales sugieren que el modelado de temas puede llegar a lograr un mejor rendimiento al ser usado para clasificar textos cortos y al incrementar el número de temas permitidos al momento de generar el modelo.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluación de distintas técnicas de representación de texto y medidas de distancia de texto usando KNN para clasificación de documentos

Calvo-Valverde¹,

Mena-Arias²

2020

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“…"f u c k e r"), learning how adjacent characters communicate with each other reveal more about the abusiveness of a comment as a whole. (Vijayan et al, 2017) surveyed the pros and cons of several techniques of machine learning and deep learning in their comprehensive study of text classification algorithms. (Malmasi and Zampieri, 2017) employed n-gram and skip gram based SVM classifier, to detect and classify hate-speech, into three categories: Hate, Offensive and Ok. (Gambäck and Sikdar, 2017) employed multiple CNN models totaling four for Hate-Speech Classification of Twitter posts into one of the following:sexism, racism, either(sexism and racism) and not hate speech.…”

Section: Related Workmentioning

confidence: 99%

Amrita School of Engineering - CSE at SemEval-2019 Task 6: Manipulating Attention with Temporal Convolutional Neural Network for Offense Identification and Classification

Sridharan¹,

Tr²

2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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With the proliferation and ubiquity of smart gadgets and smart devices, across the world, data generated by them has been growing at exponential rates, in particular social media platforms like Facebook, Twitter and Instagram have been generating voluminous data on a daily basis. According to Twitter's usage statistics, about 500 million tweets are generated each day. While the tweets reflect the users' opinions on several events across the world, there are tweets which are offensive in nature that need to be tagged under the hateful conduct policy of Twitter. Offensive tweets have to be identified, captured and processed further, for a variety of reasons, which include i) identifying offensive tweets in order to prevent violent/abusive behaviour in Twitter (or any social media for that matter), ii) creating and maintaining a history of offensive tweets for individual users (would be helpful in creating meta-data for user profile), iii) inferring the sentiment of the users on particular event/issue/topic. We (CodaLab Team/User Name: murali sr) have employed neural network models which manipulate attention with Temporal Convolutional Neural Network for the three shared subtasks i) ATT-TCN (ATTention based Temporal Convolutional Neural Network) employed for shared sub-task A that yielded a best macro-F1 score of 0.46, ii) SAE-ATT-TCN(Self Attentive Embedding-ATTention based Temporal Convolutional Neural Network) employed for shared sub-task B and sub-task C that yielded best macro-F1 score of 0.61 and 0.51 respectively. Among the two variants ATT-TCN and SAE-ATT-TCN, the latter performed better.

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Text analysis using deep neural networks in digital humanities and information science

Suissa

Elmalech

Zhitomirsky‐Geffet

2021

Asso for Info Science & Tech

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Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases presenting a super-human performance. DNNs are the state-of-the-art machine learning algorithms solving many NLP tasks that are relevant for Digital Humanities (DH) research, such as spell checking, language detection, entity extraction, author detection, ques-

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A comprehensive study of text classification algorithms

Cited by 86 publications

References 14 publications

Evaluación de distintas técnicas de representación de texto y medidas de distancia de texto usando KNN para clasificación de documentos

Evaluación de distintas técnicas de representación de texto y medidas de distancia de texto usando KNN para clasificación de documentos

Amrita School of Engineering - CSE at SemEval-2019 Task 6: Manipulating Attention with Temporal Convolutional Neural Network for Offense Identification and Classification

Text analysis using deep neural networks in digital humanities and information science

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