Sarcasm is a mode of expression whereby individuals communicate their positive or negative sentiments through words contrary to their intent. This communication style is prevalent in news headlines and social media platforms, making it increasingly challenging for individuals to detect sarcasm accurately. To mitigate this challenge, developing an intelligent system that can detect sarcasm in headlines and news is imperative. This research paper proposes a deep learning architecture-based model for sarcasm identification in news headlines. The proposed model has three main objectives: (1) to comprehend the original meaning of the text or headlines, (2) to learn the nature of sarcasm, and (3) to detect sarcasm in the text or headlines. Previous studies on sarcasm detection have utilized datasets of tweets and employed hashtags to differentiate between ordinary and sarcastic tweets depending on the limited dataset. However, these datasets were prone to noise regarding language and tags. In contrast, using multiple datasets in this study provides a comprehensive understanding of sarcasm detection in online communication. By incorporating different types of sarcasm from the Sarcasm Corpus V2 from Baskin Engineering and sarcastic news headlines from The Onion and HuffPost, the study aims to develop a model that can generalize well across different contexts. The proposed model uses LSTM to capture temporal dependencies, while the proposed model utilizes a GlobalMaxPool1D layer for better feature extraction. The model was evaluated on training and test data with an accuracy score of 0.999 and 0.925, respectively.

Specification of customer's need as software requirements in natural language create ambiguities in requirements and may also lead to failure of the software project. Generally, customers are unable to define their needs due to lack of domain understanding, technological constraints and knowledge gap between stakeholders and requirements analysts. One of the most effective approaches to minimize these gaps and ambiguities is the usage of ontologies for requirements specification and validation. However, the current approaches are mostly limited only to the translation of ambiguous software requirements. In this paper, we have discussed, analyzed and compared the current usage of these ontologies and found that these approaches are time-consuming and create complexities in the overall development process. We have presented a requirements specification ontology (ReqSpecOnto), bypassing the need for creating an ambiguous Software Requirement Specification (SRS). The upper software requirements ontology is defined in Ontology Web Language (OWL) that can be applied in different software scenarios. A case study of budget and planning system for a state physics lab is selected to specify its requirements as derived ontology from the upper ontology created. Results are validated through HermiT and Pellet reasoners to verify defined relationships and constraints. Finally, SPARQL queries are used to obtain the necessary requirements.