Hybrid CNN-EHO Model for Advanced Emotion Analysis in Economic News Using NLP Techniques

The present research attempts to apply advanced natural language processing (NLP) techniques to the analysis of emotions within economic news discourse. Understanding the temporal and emotional aspects of economic news is the key to deriving actionable insights in dynamic domains like finance. The study proposes a hybrid approach combining Depeche Mood Emotion Analysis (DMEA) with a Convolutional Neural Network, the latter being enhanced with Elephant Herd Optimization (CNN-EHO) to improve emotion classification in economic news. DMEA ranks emotion and non-emotion features according to lexical scores assigned to terms to highlight their most preponderant emotion classes. Using this technique, the research creates a generic framework by which sentiment may be rated in seven emotional categories, namely joy, sadness, anger and fear. The CNN model detects more elaborate temporal or emotional dependencies hidden inside a text using the convolutional layers, while EHO with Brownian motion applied fosters feature selection and convergence issues. The developed CNN-EHO architecture raises the standards for sentiment classification and covers a key limitation that classic algorithms had. On financial datasets, the integrated techniques report superior performance based on precision, recall and computational efficiency when benchmarked against other contemporary methods of sentiment analysis. Further, the model demonstrates robustness in processing complex features like temporal adverbials and syntactic structures that are vital to analyzing financial discourse. The hybrid approach increases sentiment classification accuracy and sets a benchmark score for real-time application in finance and related domains. Future extensions could see the use of multilingual datasets together with other emotion detection models to further the generalizability and applicability of the system.