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1 Annals of Computer Science and Information Systems

1 2023

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Comparative Analysis of Word Embedding and Machine Learning Techniques for Classification of Software Developer Communications on Gitter

Akshar Tumu, Kumar Lov, Yogita, Murthy Lalita Bhanu

Annals of Computer Science and Information Systems

2023

Vol. 35

335--346

In recent times, software developers widely use instant messaging and collaboration platforms, as these platforms aid them in exploring new technologies, raising different development-related issues, and seeking solutions from their peers virtually. Gitter is one such platform that has a heavy userbase. It generates a tremendous volume of data, analysis of which is helpful to gain insights about trends in open-source software development and the developers' inclination toward various technologies. The classification techniques can be deployed for this purpose. The selection of an apt word embedding for a given dataset of text messages plays a vital role in determining the performance of classification techniques. In the present work, the comparative analysis of nine-word embeddings in combination with seventeen classification techniques with onevsone and onevsrest has been performed on the GitterCom dataset for categorizing text messages into one of the pre-determined classes based on their purpose. Further, two feature selection methods have been applied. The SMOTE technique has been used for handling data imbalance. It resulted in a total of 612 classification pipelines for analysis. The experimental results show that word2vect, GLOVE with 300 vector size, and GLOVE with 100 vector size are three top-performing word embeddings having performance values taken across different classification techniques. The models trained using ANOVA features performed similarly to those models trained using all features. Finally, using the SMOTE technique helps models to get a better prediction ability.