Deep Hybrid CNN-LSTM Framework For Advanced Social Media Sentiment Analysis In Data-Driven Marketing Analytic

Abstract

The aim of the study, the world of data-driven marketing has evolved to the point at which the correct measurement of the societal mood at all levels of social media is imperative to shaping a responsive and personalized approach. This paper presents a deep hybrid architecture that leverages Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to overcome the limitations that the sentiment analysis has in the social media due to noise and unstructure data. Though CNNs perform better on local patterns and semantic features in the text data, LSTMs are more effective at reflecting sequential dependencies and contextual meaning, which makes a combination of the two a potentially effective solution to the problem of complex linguistic forms that user-generated content consists of. The model was trained and validated on various datasets provided by websites like Twitter, Facebook, Instagram and YouTube after undergoing tough preprocessing procedures like tokenization, stop word deletion, lemmatization and sequence padding. Comparative experiments revealed that CNN-LSTM architecture is more competitive in comparison to standard machine learning models (Naive Bayes, SVM) as well as CNN and LSTM without CNN-LSTM-like architecture and delivered the accuracy of 91%, precision of 90%, and F1 score of 90.5%. Additional assessment of the confusion matrix, ROC curves, and the case-level analysis of predictions indicated how robust the model was in sentiment classification and how sensitive it could be. The results validate that a hybrid CNN-LSTM model will be a scalable and efficient solution to real-time sentiment monitoring and therefore there is a strong implication in the field of marketing analytics, campaign optimization analysis, and dealing with the customer experience. Possible future improvements can involve attention layers, transformer, and multi-lingual adaptability in order to achieve even better performance in classification and domain generalizability.