Using unsupervised clustering approach to train the Support Vector Machine for text classification

Shafiabady, Niusha; Lee, Lam Hong; Rajkumar, R.; Kallimani, Vish; Akram, Nik Ahmad; Isa, Dino

doi:10.1016/j.neucom.2015.10.137

Cited by 59 publications

(24 citation statements)

References 17 publications

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“…Shafiabady et al [ 76 ] suggested using an unsupervised clustering approach such as self-organizing maps (SOM) and correlation coefficient (CorrCoef) to group the unlabelled documents and use them as labelled data to train the SVM for text classification. However, their approach was applied on documents not on short text such as tweets.…”

Section: Literature Reviewmentioning

confidence: 99%

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

Alomari

Katib

Albeshri

et al. 2021

Sensors

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Digital societies could be characterized by their increasing desire to express themselves and interact with others. This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This paper brings a range of technologies together to detect road traffic-related events using big data and distributed machine learning. The most specific contribution of this research is an automatic labelling method for machine learning-based traffic-related event detection from Twitter data in the Arabic language. The proposed method has been implemented in a software tool called Iktishaf+ (an Arabic word meaning discovery) that is able to detect traffic events automatically from tweets in the Arabic language using distributed machine learning over Apache Spark. The tool is built using nine components and a range of technologies including Apache Spark, Parquet, and MongoDB. Iktishaf+ uses a light stemmer for the Arabic language developed by us. We also use in this work a location extractor developed by us that allows us to extract and visualize spatio-temporal information about the detected events. The specific data used in this work comprises 33.5 million tweets collected from Saudi Arabia using the Twitter API. Using support vector machines, naïve Bayes, and logistic regression-based classifiers, we are able to detect and validate several real events in Saudi Arabia without prior knowledge, including a fire in Jeddah, rains in Makkah, and an accident in Riyadh. The findings show the effectiveness of Twitter media in detecting important events with no prior knowledge about them.

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Section: Literature Reviewmentioning

confidence: 99%

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

Alomari

Katib

Albeshri

et al. 2021

Sensors

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“…Many times, expert knowledge required to label the samples is either non-existent or inadequate. In such case, self-organizing maps and correlation coefficient are used to cluster the documents and use it to label the documents for further classification (Shafiabady et al, 2016). It eliminates the curse of dimensionality and expert intervention as well.…”

Section: Unsupervised Learningmentioning

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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“…El clasificador SVC es una variante de las SVM, algoritmos ampliamente usado en problemas de clasificación y regresión, siendo una de sus aplicaciones prácticas, en problemas referentes a clasificación de textos (Ramesh & Sathiaseelan, 2015;Sebastiani, 2002;Shafiabady et al, 2016), razón por la cual fue seleccionado este clasificador. Por su parte SVC es la implementación de Scikit-learn para el clasificador SVM que emplea la librería LIBSVM la cual según Chang & Lin (2011) ha sido empleada en trabajos cuyo dominio es el PLN, entre otros.…”

Section: Experimentos Con Svcunclassified

“…Las máquinas de vectores de soporte lineales (SVM) fueron introducidas por Cortes & Vapnik (1995). Este algoritmo es ampliamente usado en problemas de clasificación y regresión, siendo una de sus aplicaciones prácticas, en problemas referentes a clasificación de textos (Ramesh & Sathiaseelan, 2015;Sebastiani, 2002;Shafiabady et al, 2016), razón por la cual fue seleccionado este clasificador. Para realizar las pruebas con el clasificador LSVC, en primera instancia se buscaron trabajos en el estado del arte que emplearan este algoritmo de ML y que hubieran estado sujetos, pero no limitados, a tareas de clasificación de textos.…”

Section: Experimentos Con Lsvcunclassified