On negative results when using sentiment analysis tools for software engineering research

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used

Frankenstein: An Echo of Social Alienation and Social Madness

Keywords: Alienation, Defamiliarization, French Revolution, Racism, Radicalism Mary Shelley"s Frankenstein is the very power glass through which we can have the glimpse how society alienates people because of their certain characteristics which usually do not fulfill the desired and decisive taste of the society. It uncovers the uncanny defamiliarization of the familiar role of the society. The monster, a creation of Victor Frankenstein"s madness is used to testify this. The monster"s hideous appearance is the reason of the society"s disliking it and so it is regarded with disgust and hatred. Although the monster has amiable intentions, the people around him, moulding their mind in accordance to the society"s value and rules, immediately take it for granted that the monster is actually evil. The monster is rejected by people who do not know him, by people he loves, and even by his own creator, Victor Frankenstein. The importance that the ordered society likes only the ordered people and totally places upon person"s appearance is evidenced by the way that Frankenstein"s monster is judged based on his monstrous façade. The monster"s hideous appearance causes anyone who sees him to flee because, as the society clears out, the very appearance of the monster contradicts his inner goodness. It does not support the form of beauty and order. It is perceived as somewhat satanic. The monster"s first encounter with a human happens when he enters into a hut belonging to an old man and seeing the monster"s appearance the man becomes frightened though the monster does no harm to the man. It is a testimony that the society is never ready to accept the monster, an unknown creature as a part of the society and it is enough to assume that the unknown creature is considered as a monster with evil thoughts and intentions. The monster has a similar experience in a village the following day. In this case the reactions are different. One of the villagers faints, some scream, and the majority of them attack the monster. They thinks that either they should hurt or drive away the monster before it would take the chance to hurt them. The people are unable to think that there may be softness in some corner in the mind of the monster. And we perceive it when we see that the monster saves the girl in spite of his being spurned by humans before [1]

Revolutionizing Banking Decision-Making: A Deep Learning Approach to Predicting Customer Behavior

This article explores a machine learning approach focused on predicting bank customer behavior, emphasizing deep learning methods. Various architectures, including CNNs like VGG16, ResNet50, and InceptionV3, are compared with traditional algorithms such as Random Forest and SVM. Results show deep learning models, particularly ResNet50, outperform traditional ones, with an accuracy of 86.66%. A structured methodology ensures ethical data use. Investing in infrastructure and expertise is crucial for successful deep learning integration, offering a competitive edge in banking decision-making