Sentence Level Sentiment Analysis from News Articles and Blogs using Machine Learning Techniques

Shirsat, Vishal S.; Jagdale, Rajkumar S.; Shende, Kanchan; Deshmukh, Sachin N.; Kawale, Sunil

doi:10.26438/ijcse/v7i5.16

Cited by 7 publications

(4 citation statements)

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“…Twitter has been often used as a platform where people disseminate information, as well as share their opinion and emotions. This rapid sharing of opinions enables researchers to determine the sentiments associated with almost everything (eg, sentiments toward products, movies, politics, digital technology, and natural calamities) [ 14 - 18 ].…”

Section: Methodsmentioning

confidence: 99%

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

Dubey¹

2020

JMIR Public Health Surveill

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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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Section: Methodsmentioning

confidence: 99%

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

Dubey¹

2020

JMIR Public Health Surveill

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Section: Methodsmentioning

confidence: 99%

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

Dubey¹

2020

Preprint

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BACKGROUND With the increasing number of patients of COVID-19 all over the world, a particular section of the world population has been blaming China and World Health Organization for the spread of this disease. Due to this, a number of cases related to racism and hatred have been encountered all over. With Donald Trump using the term Chinese Virus, this cause has gained momentum and Ethnic Asians are now being targeted. The online situation also looks the same where certain segment of people are posting hateful comments and posts. OBJECTIVE The research study has been done to analyze the increasing racism online during COVID-19. This research aims to analyze the emotions and sentiments that have been associated with the terms like CHinese Virus, Wuhan Virus and Chinese Corona Virus. METHODS 16,000 tweets ranging from 11th April 2020 to 16th April 2020 were analyzed to find out the sentiments and emotions associated with the posts. This research has been done on R. Twitter API and SentimentR package were used for the collection of the tweets and then evaluating the sentiments respectively. RESULTS This results suggested that majority of the tweets were of negative sentiments and carried emotions of fear, sadness, anger and disgust. There were a high usage of slurs and profane words. Also, terms like China LieD People Died, Wuhan Health Organization, Kung Flu, China Must Pay and CCP is Terrorist were used a lot in these tweets. CONCLUSIONS This study gives an insight of the rising hatred and cyber racism on Twitter. This study has analyzed the twitter posts and concludes that there are a majority of people who are tweeting with mostly negative sentiments for ethnic Asian, China and WHO.

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“…It considers the whole document a basic information unit [43]. • Sentence level: classies the opinions expressed in each sentence [44]. • Aspect level: classies the opinion concerning the specic aspects of entities, because a user can give dierent opinions for dierent aspects of the same entity [45].…”

Section: A Opinion Classification Levelmentioning

confidence: 99%

Opinion Mining Using Multi-Dimensional Analysis

Biswas

Poornalatha

2023

IEEE Access

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This review addresses emerging literature in the eld of Opinion Mining with a particular emphasis on user-generated textual content. The focus is on the various tasks involved in Opinion Mining, which satises the comprehension of the essential criteria and methodologies that should be considered prior to embraking on the task. The paper provides an in-depth analysis of benchmarked datasets, widely-used feature sets, algorithms, techniques, open-source tools, challenges, real-world applications, and insights into various dimensions of Opinion Mining. The ndings have both theoretical and practical implications, as it highlights the signicance of Opinion Mining of textual content in comprehensive society. This review covers both technical and real-world knowledge and oers a comprehensive understanding of available open-source tools that are used in real-time.

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Sentence Level Sentiment Analysis from News Articles and Blogs using Machine Learning Techniques

Cited by 7 publications

References 0 publications

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

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

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

Opinion Mining Using Multi-Dimensional Analysis

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