Summary In the competing era of online industries, understanding customer feedback and satisfaction is one of the important concern for any business organization. The well‐known social media platforms like Twitter are a place where customers share their feedbacks. Analyzing customer feedback is beneficial, as it provides an advantage way of unveiling customer interests. The proposed system, namely Senti‐eSystem, aims at the development of sentiment‐based eSystem using hybridized Fuzzy and Deep Neural Network for Measuring Customer Satisfaction to assist business organizations for improving the quality of their services and products. The proposed approach initially deploys a Bidirectional Long Short Term Memory with attention mechanism to predict the sentiment polarity that is positive and negative, followed by Fuzzy logic approach to determine the customer satisfaction level, which further strengthens the capabilities of the proposed approach. The system achieves an accuracy of 92.86%, outperforming the previous state‐of‐art lexicon‐based approaches. Moreover, the effectiveness of the proposed system is also validated by applying the statistical test.

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Personality refer to the distinctive set of characteristics of a person that effect their habits, behaviour's, attitude and pattern of thoughts. Text available on Social Networking sites provide an opportunity to recognize individual's personality traits automatically. In this proposed work, Machine Learning Technique, XGBoost classifier is used to predict four personality traits based on Myers-Briggs Type Indicator (MBTI) model, namely Introversion-Extroversion(I-E), iNtuition-Sensing(N-S), Feeling-Thinking(F-T) and Judging-Perceiving(J-P) from input text. Publically available benchmark dataset from Kaggle is used in experiments. The skewness of the dataset is the main issue associated with the prior work, which is minimized by applying Re-sampling technique namely random over-sampling, resulting in better performance. For more exploration of the personality from text, pre-processing techniques including tokenization, word stemming, stop words elimination and feature selection using TF IDF are also exploited. This work provides the basis for developing a personality identification system which could assist organization for recruiting and selecting appropriate personnel and to improve their business by knowing the personality and preferences of their customers. The results obtained by all classifiers across all personality traits is good enough, however, the performance of XGBoost classifier is outstanding by achieving more than 99% precision and accuracy for different traits.

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In this study, a rapid and non-destructive method for the classification of tea varieties based on fluorescence hyperspectral imaging technology was proposed in the wavelength range of 400.6797-1001.612 nm. Multiplication Scatter Correction (MSC) was used to preprocess the spectral data of tea samples. For optimal feature selection, variable iterative space shrinkage approach (VISSA) and competitive adaptive reweighed sampling (CARS) were established and CARS achieved good results on tea spectral data. Four linear and non-linear classification models, Naïve Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Artificial Bee Colony Support Vector Machine (ABC-SVM) were established and then performances of classification models were compared according to classification accuracy. The classification accuracy of the ABC-SVM model coupled with CARS was achieved 100% which was the highest classification accuracy. The results of this study demonstrated that fluorescence hyperspectral image technology combined with the CARS-ABC-SVM model is feasible to classify tea varieties. Novelty Impact Statement: Traditional methods for the classification of tea varieties mainly focus on the appearance of tea and depend on human sensory evaluation, which is expensive and time-consuming. In this study, a method involving fluorescence hyperspectral image technology with the CARS-ABC-SVM algorithm successfully was used for precise and non-destructive classification of tea varieties. 2 of 9 | AHMAD et Al. spectral information of an object by integrating conventional spectroscopic and imaging techniques (Kaliramesh et al., 2013;