Ritika2001 / Word-Embedding-Models-for-Subjectivity-Analysis

An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks

Word Embedding Models for Subjectivity Analysis

Introduction

The files and codes uploaded to this repository are part of the paper "An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks", presented at the ICAECT 2021 conference and indexed in IEEE Xplore.

DOI: https://doi.org/10.1109/ICAECT49130.2021.9392437

Guide to Usage

• The folder "Code" contains the python files for all the models as described in the paper.
• The Evaluation.ipynb file outlines the metrics obtained after running each model.
• If you would like to use the checkpoints we've used for our work, feel free to contact Ritika Nandi, Shashank Shekhar or drop us a mail at [e-mail] and we'd be happy to help.
• You are free to reproduce, modify and pretty much do whatever you like with this work as long as you cite our work alongside it.
• Link to the dataset -> https://www.cs.cornell.edu/people/pabo/movie-review-data/
• Link for implementing Cyclic Learning Rates (CLR) -> https://github.com/bckenstler/CLR

Cite us as

R. Nandi, G. Maiya, P. Kamath and S. Shekhar, "An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks," 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2021, pp. 1-5, doi: 10.1109/ICAECT49130.2021.9392437.