Sentiment analysis of tweets through Altmetrics: A machine learning approach

Hassan, Saeed‐Ul; Saleem, Aneela; Soroya, Saira Hanif; Safder, Iqra; Iqbal, Sehrish; Jamil, Saqib; Bukhari, Faisal; Aljohani, Naif Radi; Nawaz, Raheel

doi:10.1177/0165551520930917

Cited by 28 publications

(13 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The text polarity scores and subjectivity scores for the three vaccines, Pfizer, Moderna, and AstraZeneca are plotted in bar diagrams for visualization. At the same time, the scatter plot is also demonstrated for a better understanding of the frequency of the scores [21], [22]. Figures 5 and 6 demonstrate the bar diagram of polarity and subjectivity respectively of the three mentioned vaccines.…”

Section: Resultsmentioning

confidence: 99%

Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

Shamrat

Chakraborty

Imran³

et al. 2021

IJEECS

View full text Add to dashboard Cite

The pandemic has taken the world by storm. Almost the entire world went into lockdown to save the people from the deadly COVID-19. Scientists around the around have come up with several vaccines for the virus. Amongthem, Pfizer, Moderna, and AstraZeneca have become quite famous. General people however have been expressing their feelings about the safety and effectiveness of the vaccines on social media like Twitter. In this study, such tweets are being extracted from Twitter using a Twitter API authentication token. The raw tweets are stored and processed using NLP. The processed data is then classified using a supervised KNN classification algorithm. The algorithm classifies the data into three classes, positive, negative, and neutral. These classes refer to the sentiment of the general people whose Tweets are extracted for analysis. From the analysis it is seen that Pfizer shows 47.29%positive, 37.5% negative and 15.21% neutral, Moderna shows 46.16%positive, 40.71% negative, and 13.13% neutral, AstraZeneca shows 40.08%positive, 40.06% negative and 13.86% neutral sentiment.

show abstract

Section: Resultsmentioning

confidence: 99%

Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

Shamrat

Chakraborty

Imran³

et al. 2021

IJEECS

View full text Add to dashboard Cite

show abstract

“…The SVM classifier is a highly effective and wellknown algorithm that can give successful results in text classification processes [39]. The SVM algorithm does not need a large amount of data to produce successful classification results.…”

Section: Traditional Machine Learning Modelsmentioning

confidence: 99%

Homophobic and Hate Speech Detection Using Multilingual-BERT Model on Turkish Social Media

Karayiğit

Akdağlı

Acı

2022

ITC

View full text Add to dashboard Cite

Homophobic expressions are a form of insulting the sexual orientation or personality of people. Severe psychological traumas may occur in people who are exposed to this type of communication. It is important to develop automatic classification systems based on language models to examine social media content and distinguish homophobic discourse. This study aims to present a pre-trained Multilingual Bidirectional Encoder Representations from Transformers (M-BERT) model that can successfully detect whether Turkish comments on social media contain homophobic or related hate comments (i.e., sexist, severe humiliation, and defecation expressions). Comments in the Homophobic-Abusive Turkish Comments (HATC) dataset were collected from Instagram to train the detection models. The HATC dataset was manually labeled at the sentence level and combined with the Abusive Turkish Comments (ATC) dataset that has developed in our previous study. The HATC dataset has been balanced using the resampling method and two forms of the dataset (i.e., resHATC and original HATC) were used in the experiments. Afterward, the M-BERT model was compared with DL-based models (i.e., Long-Short Term Memory, Bidirectional Long-Short Term Memory (BiLSTM), Gated Recurrent Unit), Traditional Machine Learning (TML) classifiers (i.e., Support Vector Machine, Naive Bayes, Random Forest) and Ensemble Classifiers (i.e., Adaptive Boosting, eXtreme Gradient Boosting, Gradient Boosting) for the best model selection. The performance of the detection models was evaluated using F1-score, precision, and recall performance metrics. Results showed the best performance (homophobic F1-score: 82.64%, hateful F1-score: 91.75%, neutral F1-score: 96.08%, average F1-score: 90.15%) was achieved with the M-BERT model on the HATC dataset. The M-BERT detection model can increase the effectiveness of filters in detecting Turkish homophobic and related hate speech in social networks. It can be used to detect homophobic and related hate speech for different languages since the M-BERT model has multilingual pre-trained data.

show abstract

“…The current altmetrics studies in the literature cover combining natural language processing techniques and altmetrics. NISO Alternative Assessment Metrics Project (National Information Standards Organization, 2016) and using machine learning techniques to understand the contents of social media posts are promising for the future of altmetrics (Hassan et al, 2020).…”

Section: Using Social Media As a Research Evaluation Tool: Altmetricsmentioning

confidence: 99%

Current Research Evaluation Topics in Social Sciences

Taskin

2021

Preprint

View full text Add to dashboard Cite

The social sciences, as a collection of scholarly fields, have quite different characteristics compared to STEM (science, technology, engineering, and medicine) fields. Still, researchers in the social sciences have been evaluated similarly to researchers in STEM for many years. However, many studies have been published regarding the need to evaluate social sciences differently. This chapter examines research evaluation studies published in social sciences and presents the advantages and disadvantages of current research evaluation topics including altmetrics, content-based citation analysis, peer-review, and scientific visualization

show abstract

Sentiment analysis of tweets through Altmetrics: A machine learning approach

Cited by 28 publications

References 45 publications

Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

Homophobic and Hate Speech Detection Using Multilingual-BERT Model on Turkish Social Media

Current Research Evaluation Topics in Social Sciences

Contact Info

Product

Resources

About