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Sentence level Classification through machine learning with effective feature extraction using deep learning

By
Savitha D ,
Savitha D

Research Scholar, Asst. Professor of Information Technology, Vellalar College for Women (Autonomous), Erode – 12

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Sudha L ,
Sudha L

Research Supervisor, Associate Professor, School of Computer Science, VET Institute of Arts and Science College, Erode – 12

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Abstract

Social networking website usage has increased dramatically during the past few years. Users can read other users' views, which are categorized into several sentiment classes on this medium with an array of data. These opinions are becoming more and more important while making decisions. To address the above-mentioned issues and improve the sentence-level classification's classification rate, this work introduces a new extensive pinball loss function based twin support vector machine with Deep Learning the (EPLF-TSVM-DL) to identify the polarity (negative and positive) of sentiment sentences. There are four primary components of this technique: The first portion consists of pre-processing the data to minimize noise and improve quality; the second part utilizes word embedding techniques to transform textual data into numerical data. The third part is the CNN for an efficient automatic method of extracting the features-based feature extraction and final is EPLF-TSVM-DL is used for sentence level classification that forms two classes such as Negative and Positive. The findings demonstrated that the EPLF-TSVM-DL outperforms the other classifiers with respect to of time consumption, convergence, complexity, and stability as well as true negative, true positive, error rate, false positive, precision, false negative, and classification rate.

How to Cite

1.
D S, L S. Sentence level Classification through machine learning with effective feature extraction using deep learning. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Apr. 17 [cited 2024 May 21];3:702. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/702

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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