Socio-Analyzer: A Sentiment Analysis Using Social Media Data

Bandi, Ajay; Fellah, Aziz

doi:10.29007/kzk1

Cited by 14 publications

(9 citation statements)

References 5 publications

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“…2) Labeling: In order to annotate the data set, we followed guidelines by Bandi and Fellah [4] and labeled each tweet as positive, negative, or neutral. The TextBlob tool 3 was used for the purpose of labeling the emotional sentiment into positive, negative, and neutral.…”

Section: A Covidsenti Data Collection and Labelingmentioning

confidence: 99%

“…The TextBlob tool 3 was used for the purpose of labeling the emotional sentiment into positive, negative, and neutral. According to Bandi and Fellah [4], TextBlob can indicate a sentence's attitude by calculating the score as a polarity [-1 to 1]. When the polarity of a tweet is less than −0.4, its sentiment is regarded as negative.…”

Section: A Covidsenti Data Collection and Labelingmentioning

confidence: 99%

“…I get my news on Coronavirus from o/s sources. 4 " COVIDSENTI-B consists of tweets related to the COVID-19 crises, social distancing, lockdown, and stay at home. Thus, it mainly covers the temporal change in people's behavior based on the number of cases, panic-inducing information, and so on.…”

Section: A Covidsenti Data Collection and Labelingmentioning

confidence: 99%

See 2 more Smart Citations

COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

Naseem

Razzak

Khushi

et al. 2021

IEEE Trans. Comput. Soc. Syst.

276

128

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Social media (and the world at large) have been awash with news of the COVID-19 pandemic. With the passage of time, news and awareness about COVID-19 spread like the pandemic itself, with an explosion of messages, updates, videos, and posts. Mass hysteria manifest as another concern in addition to the health risk that COVID-19 presented. Predictably, public panic soon followed, mostly due to misconceptions, a lack of information, or sometimes outright misinformation about COVID-19 and its impacts. It is thus timely and important to conduct an ex post facto assessment of the early information flows during the pandemic on social media, as well as a case study of evolving public opinion on social media which is of general interest. This study aims to inform policy that can be applied to social media platforms; for example, determining what degree of moderation is necessary to curtail misinformation on social media. This study also analyzes views concerning COVID-19 by focusing on people who interact and share social media on Twitter. As a platform for our experiments, we present a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020. The tweets have been labeled into positive, negative, and neutral sentiment classes. We analyzed the collected tweets for sentiment classification using different sets of features and classifiers. Negative opinion played an important role in conditioning public sentiment, for instance, we observed that people favored lockdown earlier in the pandemic; however, as expected, sentiment shifted by mid-March. Our study supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic.

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Section: A Covidsenti Data Collection and Labelingmentioning

confidence: 99%

Section: A Covidsenti Data Collection and Labelingmentioning

confidence: 99%

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COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

Naseem

Razzak

Khushi

et al. 2021

IEEE Trans. Comput. Soc. Syst.

276

128

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“…We developed a python loop on all rows in our datasets, and the polarity and subjectivity were returned through the textblob() call. A polarity score is a floating number between 0 and 1, while its subjectivity lies between 0 and 1 [25]. In this study, we are interested in the polarity score, converted to a label, as shown in Equation 2.…”

Section: ) Textblobmentioning

confidence: 99%

A Machine Learning-Sentiment Analysis on Monkeypox Outbreak: An Extensive Dataset to Show the Polarity of Public Opinion From Twitter Tweets

et al. 2023

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Research on sentiment analysis has proven to be very useful in public health, particularly in analyzing infectious diseases. As the world recovers from the onslaught of the COVID-19 pandemic, concerns are rising that another pandemic, known as monkeypox, might hit the world again. Monkeypox is an infectious disease reported in over 73 countries across the globe. This sudden outbreak has become a major concern for many individuals and health authorities. Different social media channels have presented discussions, views, opinions, and emotions about the monkeypox outbreak. Social media sentiments often result in panic, misinformation, and stigmatization of some minority groups. Therefore, accurate information, guidelines, and health protocols related to this virus are critical. We aim to analyze public sentiments on the recent monkeypox outbreak, with the purpose of helping decision-makers gain a better understanding of the public perceptions of the disease. We hope that government and health authorities will find the work useful in crafting health policies and mitigating strategies to control the spread of the disease, and guide against its misrepresentations. Our study was conducted in two stages. In the first stage, we collected over 500,000 multilingual tweets related to the monkeypox post on Twitter and then performed sentiment analysis on them using VADER and TextBlob, to annotate the extracted tweets into positive, negative, and neutral sentiments. The second stage of our study involved the design, development, and evaluation of 56 classification models. Stemming and lemmatization techniques were used for vocabulary normalization. Vectorization was based on CountVectorizer and TF-IDF methodologies. K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, Logistic Regression, Multilayer Perceptron (MLP), Naïve Bayes, and XGBoost were deployed as learning algorithms. Performance evaluation was based on accuracy, F1 Score, Precision, and Recall. Our experimental results showed that the model developed using TextBlob annotation + Lemmatization + CountVectorizer + SVM yielded the highest accuracy of about 0.9348.

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“…Naseem et al (1) divided the COVIDSenti dataset into three parts for evaluation and generalization purposes: COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, which have three sentiments, positive, negative, and neutral for classification purposes. They labeled tweets as positive, negative, and neutral by following the Fellah and Bandi guidelines (35). The COVIDSenti dataset consists of two months of tweets fetch from Twitter using Tweepy, Python Twitter API library.…”

Section: Dataset Selectionmentioning

confidence: 99%

COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

Jalil

Abbasi

Javed

et al. 2022

Front. Public Health

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The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

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Socio-Analyzer: A Sentiment Analysis Using Social Media Data

Cited by 14 publications

References 5 publications

COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

A Machine Learning-Sentiment Analysis on Monkeypox Outbreak: An Extensive Dataset to Show the Polarity of Public Opinion From Twitter Tweets

COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

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