Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India

Abstract

The COVID‐19 pandemic has a significant impact on the global economy and health. While the pandemic continues to cause casualties in millions, many countries have gone under lockdown. During this period, people have to stay within walls and become more addicted towards social networks. They express their emotions and sympathy via these online platforms. Thus, popular social media (Twitter and Facebook) have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID‐19‐related issues. We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases. The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus. India‐specific COVID‐19 tweets have been annotated, for analysing the sentiment of common public. To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35% for Lockdown and 83.33% for Unlock data set. The suggested method outperforms many of the contemporary approaches (long short‐term memory, Bi‐directional long short‐term memory, Gated Recurrent Unit etc.). This study highlights the public sentiment on lockdown and stepwise unlocks, imposed by the Indian Government on various aspects during the Corona outburst.

Clustering Algorithms in Sentiment Analysis Techniques in Social Media – A Rapid Literature Review

“…Algorithm wise, there are different options that can be used for performing the SA, such as:  Rule-based methods [23], [29]. This method uses a set of predefined rules or dictionaries to classify the sentiment of text.…”

“…Algorithms are sets of instructions or rules that are followed in a specific order to accomplish a specific task or solve a specific problem. They are crucial in SA as they are used to automatically process [22], [23], [24] and analyze text data to determine the sentiment or emotional tone. Without algorithms, SA would be a manual and time-consuming process.…”

GIS-Enabled Multi-Criteria Assessment for Hospital Site Suitability: A Case Study of Tehran

“…Serving as a decision support system, GIS complements the decision-making system, MCDM, collectively bolstering spatial analysis capabilities on different sources of data [11,12]. Accordingly, health-based information systems serves as a versatile analytical tool across various domains [13][14][15][16][17], contributing to increased precision while concurrently reducing errors, time, and costs [18][19][20][21]. In the realm of hospital location analysis, common techniques include the Analytical Hierarchy Process (AHP) and GIS-based models, often adopting hybrid approaches [22].…”