## 1 Introduction  
Recently, due to the COVID-19 pandemic, customers' intention has shifted towards social media and online e-commerce platforms. They express and post their opinions about products and services, shaping brand perception [1; 2]. The large-scale manufacturer relies on customer reviews, ratings, and feedback related to their product design and quality. However, the effectiveness of customer reviews is largely impeded by fake reviews [3]. Moreover, the manual analysis of these reviews is time-consuming, thus further reducing effectiveness. To address this, automated opinion extraction mechanisms use machine learning algorithms to classify customer reviews as positive, negative, or neutral. Social media platforms, including Facebook, Twitter, Instagram, YouTube, WhatsApp, and TikTok, have billions of users sharing their content, feedback, and reviews related to products, etc. Product reviews on these platforms exist in a textual, visual, and vocal form, which requires robust feature extraction techniques to improve classification performance [4]. Smart cities equipped with an intelligent navigation and positioning system can provide an intelligent recommendation system based on consumer reviews on social media platforms. The recommendation mechanisms become more sophisticated when users' verbal, visual, and textual content is classified through machine learning and deep learning models. In the online e-commerce market, product suggestions can be made intelligently by utilizing social media reviews posted by online customers for DNN model training and optimization. The consumer textual sentimental opinions related to products require natural language processing (NLP) techniques to identify key phrases and terms that are associated with particular features [5], whereas the visual text in the shape of an image needs optical character recognition (OCR) followed by NLP [6]. The vocal feedback and reviews require voice recognition based on 1D CNN models and handcrafted models [7]. Recently developed AI frameworks have been broadly classified into two categories known as machine learning and non-machine-learning frameworks [8]. Machine learning methods require a robust and distinctive set of features extracted from the training database, whereas non-machine-learning frameworks do not require feature extraction mechanisms. The non-machine-learning techniques categorize the input files by their signature data. In the static and dynamic analysis methods, feature values are extracted in the idle and the program execution stage. Non-machine-learning methods solely depend on the developer's expertise and do not consider prior examples of malware information. Moreover, non-machine-learning methods do not require special-purpose software and hardware resources. The machine learning models are most often employed in combination with isolated classifiers that can learn the pre-existing training data [9]. Generally, ML methods, including reinforcement learning, have become a common practice in cybersecurity and healthcare systems [10]. The machine-learning-based identification is carried out in two stages, which are the feature extraction and classification stages. Two broad categories of feature extraction are employed, consisting of handcrafted statistical methods and deep neural networks. Both extract feature values based on combinations of data values in their neighborhoods. Depending on customer reviews, the existing feature extraction models can broadly be classified into three classes: visual-image-based representation, voice-based features, and textual features. The visual features require image descriptors to extract language characters as a pre-processing stage of the NLP methods [11]. The existing visual object segmentation and representation both are handcrafted as well as DNN-based [12].