A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, -nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.

Software Requirements Engineering has paved its roots in both industry and academia, as today’s complex systems are programmed to provide efficient user-centric functionalities. This also refers to the emergence of challenges in Requirements Elicitation techniques, approaches and tools while performing them. Particularly, in the area of Requirements Engineering for software development, a number of techniques and approaches have been observed in literature but for mobile application development, which is different from the traditional software development, has not been discussed much in past studies. Short development cycle, device limitations and less development time for mobile application development are some of the issues to which there is no ‘silver bullet’ available. Therefore, the Requirement Analysts are in dire need of defined guidelines for Requirement Elicitation in mobile application development. With this study, we aim to provide detailed overview of Requirements Elicitation techniques and its challenges. We have conducted a systematic literature review by surveying 4507 initial and 36 primary studies. A comprehensive set of 22 elicitation techniques were measured based on quality assessment criteria including time and cost factors, resource effectiveness and domain understanding. Furthermore, challenges in Requirements Elicitation were also grouped into 8 different categories based on their applicability. Our study effectively contributes in highlighting Software Requirements Elicitation Techniques and its challenges in mobile application development.

Large language models (LLMs) have been applied to various domains, including software engineering (SE), with comparatively better success. Recently, researchers have started exploring the capabilities of LLMs in requirements engineering (RE) by proposing various approaches for automating RE activities. Moreover, a comprehensive understanding of LLM application in various RE-related activities is still emerging and growing. For this purpose, we conducted a systematic literature review (SLR) by exploring LLMs applications in RE (LLM4RE), aiming to understand how LLMs can help improve various RE activities, such as requirements elicitation, analysis, modelling, validation, specification, prioritization, and tracing. Using inclusion and exclusion criteria to answer various research questions, we identify and critically analyze 35 research papers from 2023 to October 2024. In particular, we are interested in answering LLMs application in various RE activities, identifying various LLM strategies to train LLMs for particular RE activities, determining what different evaluation matrices are used to evaluate LLMs performance, and determining which RE activities have been effectively answered to date. Through the proposed research questions, we identified that several RE activities have been comparatively improved and possibly automated. Also, various evaluation metrics have been identified that effectively evaluate the outputs generated by the LLMs. The study can prove to be effective for software vendors and researchers in automating various RE-related activities for improved software quality and user satisfaction.

Requirements elicitation is one of the most significant activities of requirements engineering (RE) process. Poorly specified requirements can lead to a failed project. Various elicitation techniques are used to elicit requirements from the users and other stakeholders, each having its own pros and cons. Lack of user engagement, less user involvement, textual nature of the requirements, time taking process are some of the major problems that make it difficult to perform elicitation via traditional techniques. Moreover, these problems further create other challenges such as ambiguity, inconsistency, and incompleteness in requirements. Currently, researchers have focused on reducing ambiguity in requirements with the help of different techniques such as natural language processing techniques, requirement templates, and formal methods; however, these techniques work on reducing ambiguity during specification or from specified requirements. One of the “young’ and exciting way of engaging users in requirements elicitation of a system is “Gamification’, which helps in user engagement into the system. We intend to discover how gamification helps in reducing ambiguity by engaging stakeholders in an interactive manner. In this review study, we have reviewed traditional techniques used to detect and reduce requirements ambiguity. On the contrary, we have also presented the significance of gamification in requirements elicitation and the popular but effective game elements used in similar systems. Furthermore, this study highlights the significance of using gamification in requirements elicitation, which is beneficial to software development team as well as the users involved in the system.

Social media offers online data source where internet users' share their opinions, views and discuss any news or events that happen around the world. Twitter is a popular social media which effectively takes part in any mega event and is used before, during and after live events. Some tweets become trend based on their popularity. The trending tweets are classified by twitter into categories like: Sports (S), Entertainment (E), Politics (P), and Technology (T) which are used in our work. We have used context based meanings to further improve the trending tweet classification results. In this article, we have verified the twitter's automated classification results with manual classification by experts. Furthermore, we compared the classification results with SVM, Linear Regression, Logistic Regression and Naive Bayes implemented in Weka tool. We found that twitter's classifier works with almost 89% accuracy in our dataset. This work would contribute in improving misclassification of data of different categories on social media.