Fine-grained sentiment analysis for measuring customer satisfaction using an extended set of fuzzy linguistic hedges

© 2020 The Authors. Published by Atlantis Press SARL. In recent years, the boom in social media sites such as Facebook and Twitter has brought people together for the sharing of opinions, sentiments, emotions, and experiences about products, events, politics, and other topics. In particular, sentiment-based applications are growing in popularity among individuals and businesses for the making of purchase decisions. Fuzzy-based sentiment analysis aims at classifying customer sentiment at a fine-grained level. This study deals with the development of a fuzzy-based sentiment analysis by extending fuzzy hedges and rule-sets for a more efficient classification of customer sentiment and satisfaction. Prior studies have used a limited number of linguistic hedges and polarity classes in their rule-sets, resulting in the degraded efficiency of their fuzzy-based sentiment analysis systems. The proposed analysis of the current study classifies customer reviews using fuzzy linguistic hedges and an extended rule-set with seven sentiment analysis classes, namely extremely positive, very positive, positive, neutral, negative, very negative, and extremely negative. Then, a fuzzy logic system is applied to measure customer satisfaction at a fine-grained level. The experimental results demonstrate that the proposed analysis has an improved performance over the baseline works

Efficient Diagnosis of Liver Disease using Deep Learning Technique

The diagnoses a patient receives can have significant repercussions for enhancing patient safety, investigation, and policymaking. Medical practitioners employ a variety of pathologic techniques to arrive at conclusions about their patients\u27 states in clinical information. The field of medical diagnosis has seen renewed efforts from clinicians in recent years. When Artificial Intelligence (AI) and Deep Learning (DL) are used in tandem with clinical data, they can greatly enhance the accuracy of disease diagnoses. The use of computers and internet has made it possible to acquire data and visualize previously inaccessible findings, such as addressing the issue of missing values in clinical research. Decision-making can be aided by problem-specific Deep Learning algorithms. In order to automatically identify illness specimens, effective predictive methods are essential. In this regard, this work employs techniques of deep learning to distinguish liver patients from normal persons. In this research, we make a prediction of liver illness using a Deep Learning model called BiLSTM. This model is able to keep track of long-term relationships in both the forward and the backward direction. The efficiency of the model\u27s predictions came out to be 93.00% overall. According to the findings, the implementation of a hybrid model seems to have enhanced the predictive accuracy

Applying Machine Learning Techniques for Performing Comparative Opinion Mining

In recent times, comparative opinion mining applications have attracted both individuals and business organizations to compare the strengths and weakness of products. Prior works on comparative opinion mining have focused on applying a single classifier, limited comparative opinion labels, and limited dataset of product reviews, resulting in degraded performance for classifying comparative reviews. In this work, we perform multi-class comparative opinion mining by applying multiple machine learning classifiers using an increased number of comparative opinion labels (9 classes) on 4 datasets of comparative product reviews. The experimental results show that Random Forest classifier has outperformed the comparing algorithms in terms of improved accuracy, precision, recall and f-measure