11th International Conference on ICTIS

Authors - Ronald S. Cordova, Rowena O. Sibayan, Hazel C. Tagalog, Rolou Lyn R. Maata
Abstract - Awareness regarding consumer sentiments will benefit a business entity and/or a company in making their marketing strategies more effective and engaging in the current digital marketing context. In traditional marketing scenarios, since there is a lack of actual emotional aspect in expressing views in real-time contexts, it has always been challenging for a business to perform a significant adjustment in their marketing campaigns and achieve a greater success rate. The proposed idea focuses on AI and ML-based approaches for sentiment analysis in digital marketing.  The framework is made up of seven core steps: data collection, preprocessing and data cleaning, sentiment analysis models, feature extraction and model training, sentiment classification and analysis, insights and decision-making, and application in digital marketing. From social media to e-commerce reviews to online discussions, consumer sentiment data comes from many digital sources. The text for analysis is standardized, and noise is cleaned in data preparation. Then, apart from other artificial intelligence-based sentiment classification models, sentiments are classified as positive, negative, or neutral using lexicon-based, machine learning, and deep learning approaches. The learned knowledge enables businesses to react dynamically to consumer sentiment, target advertisements, and adjust marketing strategies.  Businesses will be able to conduct more profitable promotions, communicate with customers better, and monitor real-time sentiment through this AI-driven sentiment analysis platform. The paper emphasizes the benefit of incorporating artificial intelligence in decision-making within digital marketing, even in addressing issues like ambiguous sentiment expression management and multi-language data. This paper provides a strategic way towards maximum customer interaction and brand loyalty and also emphasizes the need for sentiment analysis that is sustained by available data in modern digital marketing.