Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

https://www.mdpi.com/1424-8220/20/6/1796/pdf

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges.

Fake reviews, also known as deceptive opinions, are used to mislead people and have gained more importance recently. This is due to the rapid increase in online marketing transactions, such as selling and purchasing. E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased. New customers usually go through the posted reviews or comments on the website before making a purchase decision. However, the current challenge is how new individuals can distinguish truthful reviews from fake ones, which later deceives customers, inflicts losses, and tarnishes the reputation of companies. The present paper attempts to develop an intelligent system that can detect fake reviews on e-commerce platforms using n-grams of the review text and sentiment scores given by the reviewer. The proposed methodology adopted in this study used a standard fake hotel review dataset for experimenting and data preprocessing methods and a term frequency-Inverse document frequency (TF-IDF) approach for extracting features and their representation. For detection and classification, n-grams of review texts were inputted into the constructed models to be classified as fake or truthful. However, the experiments were carried out using four different supervised machine-learning techniques and were trained and tested on a dataset collected from the Trip Advisor website. The classification results of these experiments showed that naïve Bayes (NB), support vector machine (SVM), adaptive boosting (AB), and random forest (RF) received 88%, 93%, 94%, and 95%, respectively, based on testing accuracy and tje F1-score. The obtained results were compared with existing works that used the same dataset, and the proposed methods outperformed the comparable methods in terms of accuracy. 

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Data Analytics for the Identification of Fake Reviews Using Supervised Learning

On 11th March 2020, World Health Organization announced COVID19 outbreak as a pandemic. Starting from China, this virus has infected and killed thousands of people from Italy, Spain, USA, Iran and other European countries as well. While this pandemic has continued to affect the lives of millions, a number of countries have resorted to complete lockdown. During this lockdown, people have taken social networks to express their feelings and find a way to calm themselves down. In this research work, country wise sentiment analysis of the tweets has been done. This research work has taken into account the tweets from twelve countries. These tweets have been gathered from 11th March 2020 to 31st March 2020, and are related to COVID19 in some or the other way. This analysis has been done to analyse how the citizens of different countries are dealing with the situation. The tweets have been collected, pre-processed, and then used for text mining and sentiment analysis. The results of the study concludes that while majority of the people throughout the world are taking a positive and hopeful approach, there are instances of fear, sadness and disgust exhibited worldwide. However, four countries, France, Switzerland, Netherland and United States of America have shown signs of distrust and anger on a bigger scale as compared to remaining eight countries.

Twitter Sentiment Analysis during COVID-19 Outbreak

A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews

Consumer sentiment analysis is a recent fad for social media related applications such as healthcare, crime, finance, travel, and academics. Disentangling consumer perception to gain insight into the desired objective and reviews is significant. With the advancement of technology, a massive amount of social web-data increasing in terms of volume, subjectivity, and heterogeneity, becomes challenging to process it manually. Machine learning techniques have been utilized to handle this difficulty in real-life applications. This paper presents the study to find out the usefulness, scope, and applicability of this alliance of Machine Learning techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism. We have shown a systematic literature review to compare, analyze, explore, and understand the attempts and direction in a proper way to find research gaps to illustrating the future scope of this pairing. This work is contributing to the extant literature in two ways; firstly, the primary objective is to read and analyze the use of machine learning techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism. Secondly, in this work, we presented a systematic approach to identify, collect observational evidence, results from the analysis, and assimilate observations of all related high-quality research to address particular research queries referring to the described research area.

Sentiment Analysis and Opinion Mining is a most popular field to analyze and find out insights from text data from various sources like Facebook, Twitter, and Amazon, etc. It plays a vital role in enabling the businesses to work actively on improving the business strategy and gain an in-depth insight of the buyer’s feedback about their product. It involves computational study of behavior of an individual in terms of his buying interest and then mining his opinions about a company’s business entity. This entity can be visualized as an event, individual, blog post or product experience. In this paper, Dataset has taken from Amazon which contains reviews of Camera, Laptops, Mobile phones, tablets, TVs, video surveillance. After preprocessing we applied machine learning algorithms to classify reviews that are positive or negative. This paper concludes that, Machine Learning Techniques gives best results to classify the Products Reviews. Naive Bayes got accuracy 98.17% and Support Vector machine got accuracy 93.54% for Camera Reviews.

Sentiment Analysis on Product Reviews Using Machine Learning Techniques

https://www.techscience.com/cmc/v70n2/44647/pdf

Online product reviews play a major role in the success or failure of an E-commerce business. Before procuring products or services, the shoppers usually go through the online reviews posted by previous customers to get recommendations of the details of products and make purchasing decisions. Nevertheless, it is possible to enhance or hamper specific E-business products by posting fake reviews, which can be written by persons called fraudsters. These reviews can cause financial loss to E-commerce businesses and misguide consumers to take the wrong decision to search for alternative products. Thus, developing a fake review detection system is ultimately required for E-commerce business. The proposed methodology has used four standard fake review datasets of multidomains include hotels, restaurants, Yelp, and Amazon. Further, preprocessing methods such as stopword removal, punctuation removal, and tokenization have performed as well as padding sequence method for making the input sequence has fixed length during training, validation, and testing the model. As this methodology uses different sizes of datasets, various input word-embedding matrices of n-gram features of the review’s text are developed and created with help of word-embedding layer that is one component of the proposed model. Convolutional and max-pooling layers of the CNN technique are implemented for dimensionality reduction and feature extraction, respectively. Based on gate mechanisms, the LSTM layer is combined with the CNN technique for learning and handling the contextual information of n-gram features of the review’s text. Finally, a sigmoid activation function as the last layer of the proposed model receives the input sequences from the previous layer and performs binary classification task of review text into fake or truthful. In this paper, the proposed CNN-LSTM model was evaluated in two types of experiments, in-domain and cross-domain experiments. For an in-domain experiment, the model is applied on each dataset individually, while in the case of a cross-domain experiment, all datasets are gathered and put into a single data frame and evaluated entirely. The testing results of the model in-domain experiment datasets were 77%, 85%, 86%, and 87% in the terms of accuracy for restaurant, hotel, Yelp, and Amazon datasets, respectively. Concerning the cross-domain experiment, the proposed model has attained 89% accuracy. Furthermore, comparative analysis of the results of in-domain experiments with existing approaches has been done based on accuracy metric and, it is observed that the proposed model outperformed the compared methods.

/pdf/development-of-integrated-neural-network-model-for-49j5welhl5.pdf

Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets.

Now a days, huge amount data has generated on the internet and it is important to extract useful information from that huge data. Different data mining techniques are used to extract and implement to solve divers types of problems. In the era of News and blogs, there is need to extract news and need to analyze to determine opinion of that news reviews. Sentiment analysis finds an opinion i.e. positive or negative about particular subject. Negation is a very common morphological creation that affects polarity and therefore, needs to be taken into reflection in sentiment analysis. Automatic detection of negation from News article is a need for different types of text processing applications including Sentiment Analysis and Opinion Mining. Our system uses online news databases from one resources namely BBC news. While handling news articles, we executed three subtasks namely categorizing the objective, separation of good and bad news content from the articles and performed preprocessing of data is cleaned to get only what is required for analysis, Steps like tokenization, stop word removal etc. The currently work focuses on different computational methods modeling negation in sentiment analysis. Especially, on aspects level of representation used for sentiment analysis, negation word recognition and scope of negation and identification.

Document Level Sentiment Analysis from News Articles

Stock market variation data is collected in one form of breaking news from various finance web sites. The free financial data about companies is available on Internet Portals .The stock market sentiment changes with at a fraction due to major financial reforms, weather, natural disasters, and news events. Online finance news generates large amount of data. The market reforms are predicted with various machine learning algorithms. The term frequency-inverse document frequency (TF-IDF) features extracted from online news data for various companies of Bombay Stock Exchange are used along with other stock market features for prediction. The next day's stock price is predicted using ensemble deep learning framework. The data set is optimized by various deep learning techniques to get more accurate results. The proposed model produces approximately 85% of accurate prediction with deep learning framework. The market trends in terms of high and low stock values are matching exactly. The results can be further improved with use of high frequency trading algorithms.

Sachin N. Deshmukh

Papers

Sentiment Analysis on Product Reviews Using Machine Learning Techniques

Data Analytics for the Identification of Fake Reviews Using Supervised Learning

Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets.

Document Level Sentiment Analysis from News Articles

Ensemble deep learning framework for stock market data prediction (EDLF-DP)