Sentiment analysis plays an important role in understanding employee feedback and improving workplace culture. By leveraging NLP techniques to analyze this feedback accurately, organizations can pinpoint specific areas that need improvement, address employee concerns, and foster a positive work environment. These NLP-driven deep learning models offer valuable tools for E-Commerce HR and sales departments, enabling monitoring employee and users’ sentiment trends over time and assisting in implementing targeted interventions. Focusing on the e-commerce industry, this work utilizes NLP-driven deep learning methodologies to analyze employee and user feedback, aiming to identify sentiments. The proposed NLP-driven, deep learning-based framework is designed to classify user feedback into positive, negative, or neutral sentiments. The key steps in this framework include data collection, NLP-enhanced feature extraction using BERT-BiGRU, and final classification using a Graph Neural Network-based finite-state automata. The effectiveness of this NLP-centric approach was tested on diverse datasets of customer feedback from the e-commerce industry. The results demonstrate the framework’s efficacy, achieving an impressive 93.35% accuracy rate, surpassing existing benchmark methods. The research significantly benefits e-commerce by refining product portfolios and enhancing workplace culture.

Achieving high production with limited resources is a major challenge faced by poultry farmers in countries with developing economies, such as Pakistan. Optimization of the technical efficiency (TE) of poultry business operations is a promising strategy. A representative sample of 210 poultry farms in the province of Punjab in Pakistan was analyzed for TE. The studied sample comprised 105 layer chicken farms (battery cage system, egg production) and 105 broiler chicken farms (environmental control shed system, meat production). A Cobb–Douglas stochastic frontier production analysis approach with the inefficiency effect model was used to simultaneously estimate TE levels and identify factors that influence efficiency. The results indicated that flock size, labor, feed, and water consumption are positively related to egg production, whereas vaccination was found to be insignificant. For broiler businesses, flock size, feed, and water consumption were positively related to the output, whereas labor and vaccination were found to be insignificant. The results of the TE inefficiency effect model revealed that farmer age, education, experience, access to credit, and access to extension services all had a significant and positive influence on the technical efficiency of both layer and broiler farmers. The estimated mean TE level of layer and broiler poultry farmers was 89% and 92%, respectively, evaluated against the benchmark of the identified frontier of efficient production with prevailing systems. The study concludes that it is possible to increase egg production by 11% and meat production by 8% by making more efficient use of the available resources and technology. To improve poultry farmers’ efficiency, policy interventions should focus more on the pronounced effects of variables such as education, farmer experience, credit access, and extension services.

An emerging approach that addresses data heterogeneity challenges is the mediator-based architecture allowing transparent access to the data stored in many sources. Due to the growing diversity of data sources, the data integration process becomes a performance and administrative bottleneck. When dealing with decentralized heterogeneous data sources, the mediator-based technique is typically used to integrate the data. It describes a collection of applications that follow a number of data sources. The importance of analyzing and synthesizing the collected data has lately increased for academics researching autonomous and heterogeneous software systems. This study uses a mediator-based information integration model to improve pattern-based reasoning and overcome grammatical problems with integrating diverse information from web sources (IATs). The main goal of this study is to improve the mediator design for usage in the mediator-based information integration paradigm in order to address the problems caused by syntactic heterogeneity. Using our recommended methodology and enhancement strategy, the proposed technique would choose the pertinent domain from a variety of vendor-related data sources and antiquated file systems and deliver the necessary information set from heterogeneous data sources. Our suggested extended design functions well in the online bookstore as well, where there are several data sources and antiquated file systems. Future applications of this research include providing a thorough syntactic method that must be used to integrate data from various sources within the same organization into any Executive Support System (ESS).