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Software Sentiment Analysis using Deep-learning Approach with Word-Embedding Techniques

Chandra Mula Venkata Krishna, Kumar Lov, Murthy Lalita Bhanu, Krishna Aneesh

Annals of Computer Science and Information Systems

2022

Vol. 30

873--882

Sentiment analysis for the software engineering community helps to find important information for various tasks, including the suggestion to improve code quality, defect-related comments for source code, possibilities for improvement etc. The manual finding of sentiment-based comments may be an inaccurate prediction and a time-consuming process. The automation of the sentiment analysis process by leveraging Machine Learning models can benefit software professionals by giving them other developers insights and feelings about software products, libraries, development, and maintenance tasks at a glance. This study aims to develop software sentiment prediction models based on comments by (1) identifying the best embedding techniques to represent the word of the comments, not just as a number but as a vector in n-dimensional space (2) finding the best sets of vectors using different features selection techniques (3) finding best methods to handle class imbalance nature of the data, and (4) finding best architecture of deep-learning for the training of models. The developed models are validated using 5-fold cross-validation with four different performance parameters: accuracy, AUC, recall, and precision on three different datasets. The experimental finding shows that the models developed using the word embedding with feature selection using Deep Learning classifiers on balanced data can significantly predict the underlying sentiments of textual comments.