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

Sentiment Analysis in the Software Engineering community aims to make the development and maintenance of software a better experience by helping provide code and library suggestions, defect-related comments for source code, etc. The manual finding of sentiment-based comments may be an inaccurate prediction and a time-consuming process. Automating the sentiment analysis process by leveraging Machine Learning models can benefit software professionals by giving them insights into other developers 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 the best methods to handle the class imbalance nature of the data, and (4) finding the 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 embeddings with feature selection using Deep Learning classifiers on balanced data can significantly predict the underlying sentiments of textual comments.