Industries use various platforms to receive feedback from users of their products. In this paper, there is an overview of the potentials of using natural language processing system (NLP) in classifying the quality of user experience. The user experience is captured using google form. To test the efficacy of the platform, sentiments of users were analysed using hotels.ng as the source of data. The natural processing of electronic word of mouth (e-WOM) can be applied to any feedback platforms to classify and predict customers' sentiments and provide a veritable opportunity for companies to capture the quality of users' experiences and improve service delivery. The feature or sentiments extraction was done using opinion mining and data cleaning tools on heterogeneous data sources to judge the decision-making process of users. Using charts and correlations, with an average performance level of the willingness to recommend and degree of review helpfulness, the platform showed that the Quality of User Experience (QoUE) of the customers are 7.31 and 7.03 respectively. Finally, an improved logistic regression classifier was developed to test, train and classify the user experiences. Comparing the improved logistic regression classifier with standard logistic regression classifier shows that the training accuracy of the proposed improved logistic regression gave 97.67% as against the standard logistic regression which had accuracy of 86.01%
The study aims to provide technical information on the development and application of hazard analysis and critical control points (HACCP) in one of Dhaka's popular baking (cake) industries. A generic HACCP plan in accordance with legal requirements was created after a detailed analysis of data collected from the company. Every step of the production was examined for biological, chemical, and physical hazards. The prerequisite program was designed to address some hazards prior to production, thereby simplifying the HACCP plan. The critical control points were determined by answering the questions in the decision trees. Finally, the HACCP control chart was created to include critical limits, monitoring, and corrective action as components of several HACCP principles. One critical control point (CCP) and two operational pre-requisite programs (oPRPs) were identified throughout the manufacturing process.
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