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3 Annals of Computer Science and Information Systems

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An Empirical Study on Application of Word Embedding Techniques for Prediction of Software Defect Severity Level

100%

Kumar L. , Kumar M. , Bhanu Murthy L. , Misra S. , Kocher V. , Padmanabhuni S.

Annals of Computer Science and Information Systems

2021

tom Vol. 25

477--484

This work aims to develop defect severity level prediction models that have the ability to assign severity level of defects based on bugs report. In this work, seven different word embedding techniques are applied to defect description to represent the word, not just as a number but as a vector in n-dimensional space. Further, three feature selection techniques have been applied to find the right set of relevant vectors. The effectiveness of these word embedding techniques and different sets of vectors are evaluated using different classification techniques with SMOTE to overcome the class imbalance problem.

Sentiment Analysis on Twitter by Using TextBlob for Natural Language Processing

100%

Hazarika D. , Konwar G. , Deb S. , Bora D. J.

Annals of Computer Science and Information Systems

2020

tom Vol. 24

63--67

The Internet has become an innovative platform regarding online learning, exchanging content, sharing views. In this paper, we will use Twitter as our social networking platform. Sentiment analysis on Twitter is based on opinion mining on posts to obtain the user's point of view. The leading goal deals with how opinion mining techniques can be accessed to analyze some of the tweets in many reports involving various types of tweet languages on Twitter and classify its polarity. Practical implication shows that the proposed machine learning classifiers are efficient and highly accurate.

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

80%

Akshar T. , Kumar L. , Yogita , Murthy L. B.

Annals of Computer Science and Information Systems

2023

tom 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.