Software Engineering Comments Sentiment Analysis Using LSTM with Various Padding Sizes

Sanidhya Vijayvargiya, Lov Kumar, Lalita Murthy, Sanjay Misra, Aneesh Krishna, Srinivas Padmanabhuni

2023

Abstract

Sentiment analysis for software engineering(SA4SE) is a research domain with huge potential, with applications ranging from monitoring the emotional state of developers throughout a project to deciphering user feedback. There exist two main approaches to sentiment analysis for this purpose: a lexicon-based approach and a machine learning-based approach. Extensive research has been conducted on the former; hence this work explores the efficacy of the ML-based approach through an LSTM model for classifying the sentiment of the text. Three different data sets, StackOverflow, JIRA, and AppReviews, have been used to ensure consistent performance across multiple applications of sentiment analysis. This work aims to analyze how LSTM models perform sentiment prediction across various kinds of textual content produced in the software engineering industry to improve the predictive ability of the existing state-of-the-art models.

Paper Citation

in Harvard Style

Vijayvargiya S., Kumar L., Murthy L., Misra S., Krishna A. and Padmanabhuni S. (2023). Software Engineering Comments Sentiment Analysis Using LSTM with Various Padding Sizes. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-647-7, SciTePress, pages 396-403. DOI: 10.5220/0011845100003464

in Bibtex Style

@conference{enase23,
author={Sanidhya Vijayvargiya and Lov Kumar and Lalita Murthy and Sanjay Misra and Aneesh Krishna and Srinivas Padmanabhuni},
title={Software Engineering Comments Sentiment Analysis Using LSTM with Various Padding Sizes},
booktitle={Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2023},
pages={396-403},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011845100003464},
isbn={978-989-758-647-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Software Engineering Comments Sentiment Analysis Using LSTM with Various Padding Sizes
SN - 978-989-758-647-7
AU - Vijayvargiya S.
AU - Kumar L.
AU - Murthy L.
AU - Misra S.
AU - Krishna A.
AU - Padmanabhuni S.
PY - 2023
SP - 396
EP - 403
DO - 10.5220/0011845100003464
PB - SciTePress