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

Kumar Lov, Kumar Mukesh, Bhanu Murthy Lalita, Misra Sanjay, Kocher Vipul, Padmanabhuni Srinivas

Annals of Computer Science and Information Systems

2021

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

Hazarika Ditiman, Konwar Gopal, Deb Shuvam, Bora Dibya Jyoti

Annals of Computer Science and Information Systems

2020

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.