Sentiment analysis has grown into an essential tool for businesses, government agencies, and a variety of other industries to help them make adaptive decisions. This is in line with the growing number of internet users, or “netizens,” and their online activities, which include reviews and comments. Sentiment analysis is a natural language processing (NLP) technology that has been assisting industries in defining, redefining, or innovating their goods and services by using user or customer reactions and comments. Despite these importance, the method of online sentiment analysis or opinion mining is complicated due to the variety of textual presentation, data heterogeneity, and cross-platform content nature. Opinion mining techniques now in use are designed for stand-alone tasks, such as sentiment analysis tailored to a particular sector, product, individual, or even netizen behaviour. It indicates that a model created for one issue cannot be used to another. This work proposes a robust hybrid deep-driven cross-industry sentiment analysis (HD-CISA) model for netizen digital behaviour analysis and associated opinion mining, taking that into consideration as motivation. First, the HD-CISA processes text inputs gathered from four distinct industries: the healthcare, e-Commerce, social media, and hotel sectors. This is done using data adaptive pre-processing. Concatenated and projected as input to the Bi-LSTM deep network for contextual feature extraction, the pre-processed data were used for Word2Vec word-embedding. In order to establish long-term reliance across the input phrases, the Bi-GRU network subsequently learned the extracted characteristics. In order to create a composite feature vector that was learned and categorised at the Softmax layer for sentiment prediction, the local contextual features of the Bi-LSTM and the global features of the Bi-GRU were finally merged. The outcomes of the simulation produced results that were greater than any known method till now accuracy of 97.91%, precision of 97.73%, recall of 97.48%, and F-measure of 97.60%.