Qualification: 
M.Tech
remyar@am.amrita.edu

Ramya R. currently serves as the Assistant Professor at the Department of Computer Science Engineering at Amrita School of Engineering, Amritapuri. She  has completed an M. Tech in Computer Information Systems.

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

R. Rafeek and R. Remya, “Detecting contextual word polarity using aspect based sentiment analysis and logistic regression”, in 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Chennai, India, 2017.[Abstract]


Sentiment analysis (SA) is a process done computationally for detecting opinion as well as determining their polarity. Context dependent opinion words remains as a challenge for SA since their polarity changes according to the context in which they are used. This work proposes a new approach for solving this problem using (aspect, opinion) pairs and logistic regression (LR) model. Syntactic rules are used for obtaining (aspect, opinion) pairs. LR model is employed as the classifier for classifying the sentiment words into positive class or negative class. In this work we have considered product review dataset and the evaluation results obtained showed an improved classification accuracy.

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2015

Conference Paper

K. D. Adeena and R. Remya, “Extraction of relevant dataset for support vector machine training: A comparison”, in 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, 2015, pp. 222-227.[Abstract]


Support Vector Machine (SVM) is a popular machine learning technique for classification. SVM is computationally infeasible with large dataset due to its large training time. In this paper we compare three different methods for training time reduction of SVM. Different combination of Decision Tree (DT), Fisher Linear Discriminant (FLD), QR Decomposition (QRD) and Modified Fisher Linear Discriminant (MFLD) makes reduced dataset for SVM training. Experimental results indicates that SVM with QRD and MFLD have good classification accuracy with significantly smaller training time. © 2015 IEEE.

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2014

Conference Paper

R. Remya, R.Chandran Lekshmi, and L, N. J., “Design and Simulation of a Single-Stage Half-Bridge AC-DC Converter for Power Factor Correction”, in International Conference On Computation Of Power, Energy, Information and Communication (ICCPEIC), 2014.

Faculty Research Interest: