Bengali VADER: A Sentiment Analysis Approach Using Modified VADER

Amin, Al; Hossain, Imran; Akther, Aysha; Alam, Kazi Masudul

doi:10.1109/ecace.2019.8679144

Cited by 48 publications

(18 citation statements)

References 10 publications

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“…It is a valence-based model that incorporates a human-centric approach. It is developed by combining empirical validation by human raters and qualitative methods to improve the sentiment analysis [39]. Authors in [40] integrated VADER in their study and suggested that its performance is significantly similar to that of human raters.…”

Section: ) Vadermentioning

confidence: 99%

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

et al. 2021

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Sentiment analysis is the extraction and categorization of sentiments that have been expressed in text data using text analysis techniques. Manifested by earlier studies, sentiment analysis of drug reviews has a large potential for providing valuable insights to assist health-care professionals and companies for evaluating the safety of drugs after it has been marketed. Such insights help safeguard patients and increase their trust in medical companies. The existing systems either follow a lexicon-based approach or a learningbased approach for sentiment analysis in the medical domain. Learning-based techniques require annotated data while lexicon-based techniques tend to be domain-specific which restricts their wide use. This research embarks on a hybrid technique that utilizes both learning-based and lexicon-based approaches to achieve better results. General-purpose sentiment lexicons, such as AFFIN, TextBlob, and VADER, are used for annotating the reviews. Furthermore, several feature engineering techniques, such as term frequency (TF), term frequency-inverse document frequency (TF-IDF), and union of TF and TF-IDF (TF U TF-IDF) have been incorporated for the extraction of useful features. Finally, the learning models including logistic regression (LR), AdaBoost classifier (AB), random forest (RF), extra tree classifier (ETC), and multilayer perceptron (MLP) are used to classify sentiments of the reviews. The performance of the proposed hybrid approach is evaluated using accuracy, precision, recall, and F1-score. Experimental results indicate that the combination of learning-based and lexicon-based approaches provide improved results than their individual use. Moreover, TextBlob has shown promising results giving an accuracy of 96% with MLP when used with TF-IDF and with LR when used with TF U TF-IDF.

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Section: ) Vadermentioning

confidence: 99%

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

et al. 2021

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“…Valance Aware Dictionary and sEntiment Reasoner (VADER) is one of many tools found within the popular Natural Language Toolkit (NLTK), with an excess of 9000 lexicon features and the ability to analyse sentiments extracted from social media sources. It produces a gold-standard sentiment lexicon by combining quantitative and qualitative methods [ 43 ]. Sentiment lexicons contain lists with initial lexical capabilities (words) categorised to a semantic orientation (i.e., positive or negative) [ 38 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis

Roe

Lowe

Williams

et al. 2021

IJERPH

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Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter’s Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.

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“…The lexicons are scored and validated by humans. They utilize qualitative methods for improving the performance of the sentiment analyzer [ 33 ]. Kirli et al [ 34 ] suggests that the scores of the VADER sentiment analyzer hold similar results as that of human raters.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset

et al. 2022

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COVID-19 pandemic has caused a global health crisis, resulting in endless efforts to reduce infections, fatalities, and therapies to mitigate its after-effects. Currently, large and fast-paced vaccination campaigns are in the process to reduce COVID-19 infection and fatality risks. Despite recommendations from governments and medical experts, people show conceptions and perceptions regarding vaccination risks and share their views on social media platforms. Such opinions can be analyzed to determine social trends and devise policies to increase vaccination acceptance. In this regard, this study proposes a methodology for analyzing the global perceptions and perspectives towards COVID-19 vaccination using a worldwide Twitter dataset. The study relies on two techniques to analyze the sentiments: natural language processing and machine learning. To evaluate the performance of the different lexicon-based methods, different machine and deep learning models are studied. In addition, for sentiment classification, the proposed ensemble model named long short-term memory-gated recurrent neural network (LSTM-GRNN) is a combination of LSTM, gated recurrent unit, and recurrent neural networks. Results suggest that the TextBlob shows better results as compared to VADER and AFINN. The proposed LSTM-GRNN shows superior performance with a 95% accuracy and outperforms both machine and deep learning models. Performance analysis with state-of-the-art models proves the significance of the LSTM-GRNN for sentiment analysis.

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Bengali VADER: A Sentiment Analysis Approach Using Modified VADER

Cited by 48 publications

References 10 publications

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset

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