Deep-Learning Approach for Sentiment Analysis in Software Engineering Domain

Aneesah Abdul Kadhar, Smitha S. Kumar*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Sentiment analysis (SA) is an automated process that is increasingly being applied on software engineering (SE) text to study developers’ sentiments and to improve productivity. Prior studies have reported varying performance for software engineering datasets depending on the techniques employed. In this paper, we propose deep-learning approach to perform sentiment analysis to identify the sentiment polarities of Jira [20] dataset. We developed and compared the performance of stacked CNN, stacked LSTM and stacked BiLSTM classifiers and found that the stacked BiLSTM classifier outperformed the other classifiers developed on the above-mentioned dataset. Additionally, this study investigated the impact of domain-customization on the performance of the classifiers by employing SE-specific word embeddings learnt from StackOverflow posts and compared with the pre-trained open-domain word embeddings learnt from google news posts. The classifiers employing Google News (GN) embeddings outperformed the SE-customized based classifiers. The better performance of GN embedding classifiers is due to the large generic training corpus of the GN word embedding model which identifies the sentiments expressed accurately when compared to SE-specific word embedding.

Original languageEnglish
Title of host publicationProceedings of International Conference on Information Technology and Applications. ICITA 2021
EditorsAbrar Ullah, Steve Gill, Álvaro Rocha, Sajid Anwar
Number of pages10
ISBN (Electronic)9789811676185
ISBN (Print)9789811676178
Publication statusPublished - 21 Apr 2022
Event15th International Conference on Information Technology and Applications 2021 - Dubai, United Arab Emirates
Duration: 13 Nov 202114 Nov 2021

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


Conference15th International Conference on Information Technology and Applications 2021
Abbreviated titleICITA 2021
Country/TerritoryUnited Arab Emirates


  • BiLSTM
  • CNN
  • Deep learning
  • LSTM
  • Sentiment analysis
  • Software engineering
  • Word embedding
  • Word2Vec

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


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