An Advanced Multi Class Instance Selection based Support Vector Machine for Text Classification

Ramesh, Balaji Polepalli; Sathiaseelan, J. G. R.

doi:10.1016/j.procs.2015.07.400

Cited by 29 publications

(15 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It has to be verified that data is thoroughly preprocessed to increase the performance of classifiers. To achieve a better data for analysis efficient instance selection based SVM is suggested (Ramesh & Sathiaseelan, 2015). It has shown remarkable accuracy with multi-dataset analysis.…”

Section: Support Vector Machinementioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

View full text Add to dashboard Cite

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.

show abstract

Section: Support Vector Machinementioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

View full text Add to dashboard Cite

show abstract

“…It is mostly used to solve classification problems [ 26 ]. Support vector machine (SVM) is used to separate data belonging to two classes in a most suitable way, and to implement this procedure, hyperplanes are specified [ 27 ].…”

Section: Classificationmentioning

confidence: 99%

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

Kırelli

Arslankaya

2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

As the usage of social media has increased, the size of shared data has instantly surged and this has been an important source of research for environmental issues as it has been with popular topics. Sentiment analysis has been used to determine people's sensitivity and behavior in environmental issues. However, the analysis of Turkish texts has not been investigated much in literature. In this article, sentiment analysis of Turkish tweets about global warming and climate change is determined by machine learning methods. In this regard, by using algorithms that are determined by supervised methods (linear classifiers and probabilistic classifiers) with trained thirty thousand randomly selected Turkish tweets, sentiment intensity (positive, negative, and neutral) has been detected and algorithm performance ratios have been compared. This study also provides benchmarking results for future sentiment analysis studies on Turkish texts.

show abstract

“…Beberapa penelitian sebelumnya mengenai ulasan film menggunakan klasifikasi Support Vector Machine (SVM) dan Particle Swarm Optimization (PSO) atau sentimen opini analisis (Basari, Hussin, Ananta, & Zeniarja, 2013). Machine Learning yang memperkenalkan klasifikasi teks seperti Naive Bayes, K-NN, SVM dan Rocchio Classification (Ramesh & Sathiaseelan, 2015).…”

Section: Pendahuluanunclassified

Komparasi Algoritma Support Vector Machine Dan Naïve Bayes Dengan Algotima Genetika Pada Analisis Sentimen Calon Gubernur Jabar 2018-2023

Gunawan

Riana

Ardiansyah

et al. 2020

View full text Add to dashboard Cite

Abstrak – Kontestasi politik dalam penentuan menjadi pemimpin tingkat provinsi dalam hal ini gubernur jawa barat 2018-2023. Masyarakat yang memberikan opininya berupa tweet pada media sosial twitter menentukan bentuk dukungan atau tidaknya, sehingga perlu adanya analisis sentimen terhadap calon Gubernur agar mengetahui tingkat kepercayaan masyarakat serta terbentuk citra kepada calon Gubernur Jawa Barat 2018-2023. Akan tetapi membaca keseluruhan tweet yang tersebar dalam twitter yang berkaitan dengan masing-masing calon gubernur akan memakan waktu dan membingungkan dalam pengambilan keputusan. Klasifikasi sentimen akan mengurai masalah mengenai opini, pendapat, emosi dan prilaku dengan studi komputasi. Metode klasifikasi yang akan dibahas dalam penelitian yaitu dengan algoritma Naïve Bayes serta Support Vector Machine. Penentuan fitur menentuka hasil akurasi, dalam penentuan fitur seleksi digunakan Genetic Algorithm agar dapat meningkatan akurasi pengklasifikasian pada Support Vector Machine dan Naive Bayes. Perolehan penelitian ini yaitu klasifikasi teks dalam pola negatif atau positif dari tweet calon gubernur jawa barat 2018-2023. Pada dataset tidak seimbang Support Vector Machine menghasilkan rata-rata akurasi 92.61% dengan AUC 0,950, Naive Bayes menghasilkan rata-rata akurasi 93,29% dengan AUC 0,525, Support Vector Machine berbasis Genetic Algorithm menghasilkan rata-rata akurasi 93,03% dengan AUC 0,869, Naive Bayes berbasis Genetic Algorithm menghasilkan rata-rata akurasi 92,85% dengan AUC 0,543. Hasil ini menunjukan bahwa Support Vector Machine dapat digunakan untuk membangun deteksi tweet klasifikasi positif dan negatif dengan tingkat akurasi yang tinggi. Kebaruan dari penelitian ini adalah bahwa Support Vector Machine dapat digunakan untuk mendeteksi tweet pada dataset twitter berbahasa indonesia penulis.

show abstract

An Advanced Multi Class Instance Selection based Support Vector Machine for Text Classification

Cited by 29 publications

References 6 publications

Text Classification Techniques: A Literature Review

Text Classification Techniques: A Literature Review

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

Komparasi Algoritma Support Vector Machine Dan Naïve Bayes Dengan Algotima Genetika Pada Analisis Sentimen Calon Gubernur Jabar 2018-2023

Contact Info

Product

Resources

About