Novel heuristic-based hybrid ResNeXt with recurrent neural network to handle multi class classification of sentiment analysis

Present-day, interdisciplinary research is increasing in social network-related applications, and it is a daily routine activity in every human life. So, sentiment analysis (SA) based on opinion mining is the most sophisticated concept in the well-known social network environment. Different machine learning methods were implemented to extract different text label features in SA, and all of those methods can detect whether a given text is positive or negative based on the text features. Analysis of sentiment has been suffering from inaccuracies while using machine learning and sentiment-based lexical methods dependent on domain-specific problems. Multi-class SA is an expensive task where memory, label samples, and other parameters are insufficient. So, we propose and implement a novel hybrid model which is a combination of ResNeXt and recurrent neural framework (NH-ResNeXt-RNF) to explore multi-class sentiment from textual features. This framework investigates the polarity of words connected to a specific domain across the entire dataset and eliminates noisy data in an unsupervised manner using pre-processing. Optimization is required to perform efficient multi-class classification to reduce the effort associated with annotation for multi-class SA via unsupervised learning. The proposed model performance is evaluated on two data sets namely: Amazon and Twitter. We increase the accuracy of the sentiment of polarity on each sentence present in the data set. Experimental results of the proposed approach give better and more efficient multi-class (positive, negative, very positive, neutral and highly negative) domain-specific sentiment than traditional approaches related to supervised, semi-supervised, and unsupervised domains. The proposed hybrid model accuracy is 96.5% and 95.37% for Amazon and Twitter datasets respectively.