Comparative study of Twitter Sentiment On COVID - 19 Tweets

Nair, Anu J; Veena, G; Vinayak, Aadithya

doi:10.1109/iccmc51019.2021.9418320

Cited by 57 publications

(15 citation statements)

References 12 publications

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“…Figure 2 shows how the demoji module converts the emoji in the tweet into an equivalent text for further processing. [24]. It is designed to help computers understand the meaning of ambiguous language by using surrounding text to establish context.…”

Section: F Web Interfacementioning

confidence: 99%

See 1 more Smart Citation

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Ananthapadmanabha¹,

C²,

Sabariraju³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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One in every four individuals has a diagnosable mental disease in a year. Around 20% of children and adolescents have a mental health condition and often ignore it. It is found that 93% of youth use social media to communicate and engage, as it reflects their emotions, moods, and thoughts. As a result, machine learning algorithms may anticipate people's moods and emotions based on their postings and comments. On the other hand, psychometric tests use questions to determine how individuals think, feel, behave, and react. It is necessary to investigate a hybrid approach for identifying people's mental illness by combining social media inputs and psychometric tests, especially during a pandemic. The hybrid approach can combine the results from both the models to reflect on the user's digital & non-digital reactions to certain sensitive situations to determine their mental state. Hence, the present paper aims to develop a web framework that can forecast the emergence of mental illness in the future based on data from social media comments and real-time data from psychometric tests using machine learning algorithms. The proposed work includes the cluster ensemble method for social media posts and a convolution neural network model for psychometric tests. This model predicts mental illness with an accuracy of 87.05 percent. The individual can use this result to take the required precautions by visiting a psychologist.

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Section: F Web Interfacementioning

confidence: 99%

“…Fig. 2 Emoji to text conversion Bidirectional encoder representations from Transformers (BERT) are open-source machine learning frameworks for natural language processing[24]. It is designed to help computers understand the meaning of ambiguous language by using surrounding text to establish context.…”

mentioning

confidence: 99%

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Ananthapadmanabha¹,

C²,

Sabariraju³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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show abstract

“…Valence Aware Dictionary for Sentiment Reasoning (VADER), a text sentiment analysis model was used by ( A.J. Nair et al 2021) for mining public opinion from COVID-19 tweets to help out public health organizations, as well as government officials [12]. In [4], [13]- [17], the authors had used Multinomial NB, Decision Tree (DT), Logistic Regression, Support Vector Machine (SVM), Random Forest Classifier with different features like weighted TF-IDF and n-gram to investigate sentiment analysis from the tweets related to COVID-19 pandemic to mine public anxiety.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic

HOSSAIN¹,

Assaduzzaman²,

Mynoddin³

et al. 2022

Preprint

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Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsApp, etc are playing a very vital role in developing virtual communities. A pandemic situation like COVID-19 accelerated people's involvement in social sites to express their concerns or views regarding crucial issues. Mining public sentiment from these social sites especially from Twitter will help various organizations to understand the people's thoughts about the COVID-19 pandemic and to take necessary steps as well. To analyze the public sentiment from COVID-19 tweets is the main objective of our study. We proposed a deep learning architecture based on Bidirectional Gated Recurrent Unit (BiGRU) to accomplish our objective. We developed two different corpora from unlabelled and labeled COVID-19 tweets and use the unlabelled corpus to build an improved labeled corpus. Our proposed architecture draws a better accuracy of 87% on the improved labeled corpus for mining public sentiment from COVID-19 tweets.

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“…The tweets were processed under preliminary processing, classification, and analysis. A. J. Nair et al [22], proposed a work that uses sentiment analysis using techniques like Logistic Regression, BERT sentiment analysis. The suggested analysis approach can be adapted based on the domain but are sensitive to sentiment expressions.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Depression on social media using Distant Supervision

Vayadande¹,

Bodhankar²,

Mahajan³

et al. 2022

ITM Web Conf.

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Amidst Covid-19, young adults have experienced major symptoms of anxiety and/or depression disorder (56%). Mental health issues have been spiking all over the world rapidly. People have taken up to social media as a platform to vent about their mental breakdowns. Twitter has seen enormous rise in depressive and anxious tweets in these times, but the downside being that majority of the population has neglected the importance of mental health issues and there are not enough resources to liberate people about it. Also, people hesitate to talk about their mental issues and seek help. So, a machine learning model using distant supervision to detect depression on Twitter is curated. Use of Sentiment140 dataset with 1.6 million records of different tweets. Our training data makes use of Twitter tweets included with emojis, which are classified as noisy labels on a dataset. Further, this paper mentions about how to use models like Support Vector Machine (SVM), Logistic Regression, Naive Bayes, Random Forest, XGBoost to distinguishing tweets between depressive or nondepressive. The purpose behind using multiple models is to achieve highest accuracy when trained with emoticon dataset. The paper’s main contribution is the idea of using tweets with emoticons for distant supervised learning.

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Comparative study of Twitter Sentiment On COVID - 19 Tweets

Cited by 57 publications

References 12 publications

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic

Classification of Depression on social media using Distant Supervision

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