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

Background: With increasing numbers of patients with COVID-19 globally, China and the World Health Organization have been blamed by some for the spread of this disease. Consequently, instances of racism and hateful acts have been reported around the world. When US President Donald Trump used the term “Chinese Virus,” this issue gained momentum, and ethnic Asians are now being targeted. The online situation looks similar, with increases in hateful comments and posts.
Objective: The aim of this paper is to analyze the increasing instances of cyber racism during the COVID-19 pandemic, by assessing emotions and sentiments associated with tweets on Twitter.
Methods: In total, 16,000 tweets from April 11-16, 2020, were analyzed to determine their associated sentiments and emotions. Statistical analysis was carried out using R. Twitter API and the sentimentr package were used to collect tweets and then evaluate their sentiments, respectively. This research analyzed the emotions and sentiments associated with terms like “Chinese Virus,” “Wuhan Virus,” and “Chinese Corona Virus.”
Results: The results suggest that the majority of the analyzed tweets were of negative sentiment and carried emotions of fear, sadness, anger, and disgust. There was a high usage of slurs and profane words. In addition, terms like “China Lied People Died,” “Wuhan Health Organization,” “Kung Flu,” “China Must Pay,” and “CCP is Terrorist” were frequently used in these tweets.
Conclusions: This study provides insight into the rise in cyber racism seen on Twitter. Based on the findings, it can be concluded that a substantial number of users are tweeting with mostly negative sentiments toward ethnic Asians, China, and the World Health Organization.

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The Resurgence of Cyber Racism During the COVID-19 Pandemic and its Aftereffects: Analysis of Sentiments and Emotions in Tweets.

The COVID-19 pandemic, is also known as the coronavirus pandemic, is an ongoing serious global problem all over the world The outbreak first came to light in December 2019 in Wuhan, China This was declared pandemic by the World Health Organization on 11th March 2020 COVID-19 virus infected on people and killed hundreds of thousands of people in the United States, Brazil, Russia, India and several other countries Since this pandemic continues to affect millions of lives, and a number of countries have resorted to either partial or full lockdown People took social media platforms to share their emotions, and opinions during this lockdown to find a way to relax and calm down In this research work, sentiment analysis on the tweets of people from top ten infected countries has been conducted The experiments have been conducted on the collected data related to the tweets of people from top ten infected countries with the addition of one more country chosen from Gulf region, i e Sultanate of Oman A dataset of more than 50,000 tweets with hashtags like #covid-19, #COVID19, #CORONAVIRUS, #CORONA, # StayHomeStaySafe, #Stay Home, #Covid_19, #CovidPandemic, #covid19, #Corona Virus, #Lockdown, #Qurantine, #qurantine, #Coronavirus Outbreak, #COVID etc posted between June 21, 2020 till July 20, 2020 was considered in this research Based on the tweets posted in English a sentiment analysis was performed This research was conducted to understand how people from different infected countries cope with the situation The tweets were collected, pre-processed and then text mining algorithms used and finally sentiment analysis have been done and presented with the results The purpose of this research paper to know about the sentiments of people from COVID-19 infected countries

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Public Sentiment Analysis on Twitter Data during COVID-19 Outbreak

Sentiment analysis is a process that discovers the user opinions and views against any service or a product. YouTube is one of the most popular videos sharing platforms obtaining millions of views. These receive several comments, containing valuable information that helps in improving the rating levels of the uploaded content. These comments are utilized by using natural language processing techniques and machine learning techniques. There are many attempts had been proposed scholarly with two (positive or negative), three (two with neutral) or multiple (happy, sad, fear, surprise and anger) classes. However, it is challenging to choose the best accurate model. Therefore, there had been attempts to use sentiment analysis on YouTube comments in identifying the polarity as well. This research paper investigates the sentiment analysis methods and techniques that can be used on the YouTube content. Additionally, it explains and categorizes these approaches which are useful in researches in data mining and sentiment analysis.

Sentiment Analysis for Youtube Videos with User Comments: Review

YouTube is one of the most popular video-sharing platforms. Recently, the use of YouTube as an educational tool has increased due to the covid-19 pandemic. The quality of the educational videos is crucial in the learning process. Users' comments on educational videos can help to determine the quality of the videos. Such comments can be utilized using a natural language processing technique called Sentiment Analysis. This research proposes a model to perform sentiment analysis on YouTube Arabic educational videos using classical machine learning classifiers and deep learning models. There are six experiments were conducted on two types of datasets: imbalance and balanced datasets. In addition, three balancing techniques were utilized which are: oversampling, under-sampling, and SMOTE. The results show that the best accuracy obtained from all the experiments was 96% by the SVC, RF, and DL model using the oversampling and SOMTE techniques. The worst accuracy achieved by the ML classifiers was 74% by the KNN classifier using the under-sampling technique. Meanwhile, the worst accuracy achieved by the DL model was 89% for the manually balanced dataset using the bigram, trigram, and five-gram. Additionally, this research introduced a novel dataset based on Arabic YouTube educational videos. This model can be beneficial to many researches in data mining research area or related research areas.

Sentiment Analysis for YouTube Educational Videos Using Machine and Deep Learning Approaches

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Sentiment Analysis of Movie Review using Machine Learning Techniques

Despite the fact that there are various electronic alternatives to utilize and submit data, some people choose to use handwritten forms in systems like courier services. Manually entering data from handwritten forms into a system is time-consuming and error-prone. To prevent this scenario, the suggested method extracts text from handwritten forms and saves the collected data in a CSV file. This technique eliminates unnecessary work delays and makes it less prone to errors. This project applies segmentation techniques to images of handwritten forms and then uses modified tesseract functions on this segmented data to extract text from the supplied image. Formatted input aids in the achievement of better outcomes through segmentation. Pytesseract, an OCR tool, is implemented in text extraction. The main part of this proposed system is text extraction from the uploaded image, first segmenting this image and then converting it to digital text. This project will recognize the text from the handwritten form provided as input in Tesseract and try to save it to the system as a CSV file. This makes storing the data in the system automatically. This proposed system requires users to submit data in a specific format via the form provided.

V Uma Ramya

Papers

Sentiment Analysis of Movie Review using Machine Learning Techniques

MTESSERACT: An Application for Form Recognition in Courier Services