Aspect-Based Sentiment Analysis of online Reviews for Business Intelligence

The COVID-19 pandemic has affected the patients to a large extent who are in immediate need of medical care. People rely on online reviews shared on review websites to gather information about hospitals like the availability of beds, availability of ventilators, etc. However, these reviews are large in number and unstructured, which makes it difficult to interpret them. Hence, the authors have proposed a methodology that will apply an aspect-based sentiment analysis on the reviews to gain meaningful insights of the hospital based on different aspects like doctor, staff, facilities, etc. For the purpose of empirical validation, a total of 26,071 reviews pertaining to 325 hospitals were scrapped. The authors concluded that the study can be useful for patients as it helps them to select the hospital that best suits them. The study also helps hospital administration to improve the current services according to the needs of the patients. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Exploring Text Mining Techniques for Business Intelligence

COVID-19 pandemic has created a huge impact on people around the world. Even though most of the people have encountered negative experiences, there are some people who take this as an advantage and exploit this opportunity. Difficult conditions have been adapted by companies to survive the Covid-19 situation. This paper is mainly focused to identify the business lines developed around Covid-19 and their impact on the business environment, applying text mining methodology. The analysis uses open-source software Python Anaconda which mainly focuses on the areas of business and marketing. Four different areas of intervention are identified in the results. It is found that the common factors that have been impacted are the new forms of action which majorly improve the people's quality of life. © 2022 IEEE.

iCompass at CheckThat! 2022: Combining Deep Language Models for Fake News Detection

Users of social media tend to explore different platforms to obtain news and find information about different events and activities. Furthermore they read, share, publish news with no prior knowledge of the certainty of being real or fake. This necessitates the development of an automated system for fake news detection. In this paper we report a system and its output as part of CLEF2022 - CheckThat! Lab Fighting the COVID-19 Infodemic and Fake News Detection. Task 3 was carried out using two BERT base uncased and data preprocessing with stop-words removal, lemmatization. We achieve an F1 score of 0.339 on news classification on English dataset. © 2022 Copyright for this paper by its authors.

A Two-Phase Classifier Model for Predicting the Drug Satisfaction of the Patients Based on Their Sentiments

Since the corona virus has emerged, genuine clinical resources, such as a paucity of experts and healthcare workers, a lack of adequate equipment and medications, and so on, have reached their peak of inaccessibility. Several people have died as a consequence of the medical profession’s concern. Individuals began self-medicating due to a lack of supply, which exacerbated an already precarious health situation. A rise in new ideas for automation is being spurred by machine learning’s recent success in a varied variety of applications. In this paper, we have proposed a two-phase Decision Tree Classifier based on Artificial Neural networks (DTNN). The work is based on the satisfaction of the drugs among patients with the help of their comments as positive or negative polarity. The dataset of drugs used in this paper is Cymablta and Depopovera. The proposed results are compared with the existing methodology of Support Vector Machine Neural Network (SVMNN). The results are shown in graphical and tabular form which shows the efficiency of the proposed methodology. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Sentiment Analysis for Arabic Tweets on Covid-19 Using Computational Techniques

The Coronavirus pandemic has affected the regular course of life. Usage of social media like Twitter is rapidly increasing in Arab's world regarding this phenomenon that has taken over the world by storm. This platform allows Arabian to easily write comments and share their feelings, thoughts and suggestions that can be positive or negative comments. This paper examines the Arabic sentiment analysis of Coronavirus-related tweets, as well as how Arab sentiment has changed over time in various countries. The goal of this study is to extract Arabic tweets from different periods of the pandemic and apply various preprocessing operations to them. Furthermore, the different state-of-art deep learning and machine learning classifiers are applied on the dataset and the accuracy of the classifiers are evaluated using several visualization tools. This paper focused on predictive modelling of tweets to find how the people's opinion keeps on dwindling with the change of time during the course of time before, during and post pandemic. It also analyzes the different facts reveled before and after lockdown post pandemic, performing sentiment analysis to justify the claims that social media is not the reliable source to take preventive measures by the government agencies, imposing any decision. Deep learning algorithm has achieved more accuracy than machine learning in both periods, in the peck pandemic period, the RFC accuracy was around 83% where the DNN had 84%. © 2022 IEEE.