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.

In the contemporary landscape of data-driven decision-making, businesses are increasingly harnessing customer segmentation as a strategic tool for tailoring their marketing endeavors. his research employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to investigate customer segmentation for targeted marketing. It encompasses phases such as data understanding, preprocessing, modeling, evaluation, deployment, and monitoring. Our study applies K-Means and Hierarchical Clustering algorithms to create customer segments. While Hierarchical Clustering provides a visually insightful segmentation structure, K-Means excels in terms of the Silhouette score, a crucial clustering metric. Overall, K-Means Clustering emerges as the superior choice due to its interpretability and comprehensive utility. This research contributes to data-driven marketing by offering insights for businesses seeking to enhance marketing strategies, elevate customer engagement, and boost revenue.