Sentiment analysis on covid-19 vaccine using Twitter data: A NLP approach

Abstract: The growing availability of digital text data has sparked a need for effective sentiment analysis methods, which enable the automatic extraction of sentiment from text for various purposes. This study investigates the application of sentiment analysis using the Amazon Reviews Polarity Dataset, a curated compilation of texts categorised into positive and negative attitudes based on review ratings. Despite the dataset containing a substantial amount of labelled data, its narrow focus and classification technique have difficulties in effectively capturing nuanced expressions of sentiment. Using the Amazon Reviews Polarity Dataset as a foundation, this work does a thorough investigation of the performance of several machine learning models for sentiment analysis tasks. We learn a lot about how well different models can detect sentiment patterns by comparing them using a wide variety of performance metrics like recall, accuracy, precision, and F1 score. These metrics include K-Nearest Neighbours (KNN), Random Forest (RF), Logistic Regression (LG), and Ensemble Classifier (ECLF). Logistic Regression stands out as the most effective model, with the Ensemble Classifier coming in a close second, highlighting their promise for practical sentiment analysis applications. These findings emphasise the usefulness of the dataset and the success of the models, showing the crucial significance of careful model selection and evaluation methods in guaranteeing dependable and accurate outcomes in the field of natural language processing applications

Objective: To make an extensive analysis of sentiment within the discourse surrounding COVID-19 vaccines on Twitter, employing Natural Language Processing (NLP) methodologies. Method: The research methodology encompasses data collection via the Twitter API, followed by sentiment analysis facilitated by the TextBlob library. Pre-processing stages are integrated to cleanse and standardize the Twitter data. Subsequently, sentiment analysis categorizes tweets into positive, negative, and neutral sentiments based on polarity scores. Findings: The findings, grounded in a dataset spanning from March 1, 2022, to April 30, 2022, comprising 61,934 tweets, unveil that 45.0% of tweets conveyed positive sentiment, 17.3% exhibited negativity, and 37.7% maintained neutrality. Moreover, an exploration of tweet subjectivity revealed that 70.1% of the content expressed subjectivity, while 29.9% conveyed objectivity. The research is augmented with visual representations, including word clouds and subjectivity-polarity graphs, that offer a more intuitive understanding of sentiment trends. Novelty: This study contributes to the expanding landscape of sentiment analysis and its application within the context of public health crises, empowering stakeholders with valuable knowledge to enhance vaccine acceptance and effectiveness. The tool used “Tweet Downloader” in data collection makes this study different from other reviewed studies. Keywords: COVID-19 vaccine, Twitter sentiment analysis, Public perception, Natural Language Processing (NLP), Social media data

Sentiment analysis plays a pivotal role in understanding the sentiments and opinions expressed in textual data, offering valuable insights into various domains, including tourism. In this study, we present a comprehensive review of sentiment analysis techniques applied to tourism reviews using machine learning algorithms. The abundance of user-generated content on tourism platforms has made sentiment analysis an indispensable tool for businesses and researchers alike. By leveraging machine learning algorithms, researchers can extract sentiments from vast amounts of textual data efficiently and accurately. This review outlines the key methodologies and approaches utilized in sentiment analysis of tourism reviews. It discusses preprocessing techniques such as text tokenization, stop-word removal, and stemming, which are crucial for preparing textual data for analysis. Furthermore, it examines various machine learning algorithms employed for sentiment classification, including Naive Bayes, Support Vector Machines, and Recurrent Neural Networks. Additionally, the review delves into feature extraction methods such as bag-of-words, TF-IDF, and word embeddings, highlighting their impact on sentiment analysis accuracy. Moreover, it explores the challenges and limitations associated with sentiment analysis in the tourism domain, such as sarcasm detection and language nuances.

The sentiment analysis of online product review has been a pressing concern in perception and support of sound decision-making using information provided by e-commerce portals. Through a random forest (RF) classifier and a sophisticated preprocessor pipeline, this paper proposes a useful sentiment classification model of Amazon product reviews. The data collection is made in the beginning of the research process with Amazon Reviews (2018). It is pre-processed in several stages, such as special character removal, stop-word removal, tokenization and part-of-speech (POS) tagging to normalize and standardize the textual data. TF-IDF is an extraction tool which converts text into meaningful numerical processes. RF model is then trained to predict customer sentiment and its ensemble structure is employed to overfitting and maximize predictive stability. The measures of performance in the model include accuracy, precision, recall, F1-score and loss. RF classifier is an excellent accuracy (ACC) rate of 98.83, a high precision (PRE) rate of 97.70, a high recall (REC) rate of 98.64, and a balanced F1-score (F1) rate of 98.17 compared to other traditional and deep learning classifiers such as the Logistic Regression, SVM, NB-SVM, and CNN-RNN. Overall, the RF-based sentiment analysis model is a highly trustworthy, scalable and efficient way of obtaining insights using a huge volume of e-commerce reviews data.

Public health surveillance has gained more importance recently due the global COVID-19 pandemic. It is important to track public opinions and positions on social media automatically, so that this information can be used to improve public health. Sentiment analysis and stance detection are two social media analysis methods that can be applied to health-related social media posts for this purpose. In this chapter, the authors perform sentiment analysis and stance detection in Turkish tweets about COVID-19 vaccination. A sentiment- and stance-annotated Turkish tweet dataset about COVID-19 vaccination is created. Different machine learning approaches (SVM and Random Forest) are applied on this dataset, and the results are compared. Widespread COVID-19 vaccination is claimed to be useful in order to cope with this pandemic. Therefore, results of automatic sentiment and stance analysis on Twitter posts on COVID-19 vaccination can help public health professionals during their decision-making processes.

Sentiment analysis (SA) is the procedure of recognizing the emotions related to the data that exist in social networking. The existence of sarcasm in textual data is a major challenge in the efficiency of the SA. Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection, punctuations, and sentiment shift that are vital indicators of sarcasm. With the advent of deep-learning, recent works, leveraging neural networks in learning lexical and contextual features, removing the need for handcrafted feature. In this aspect, this study designs a deep learning with natural language processing enabled SA (DLNLP-SA) technique for sarcasm classification. The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data. Besides, the DLNLP-SA technique holds various sub-processes namely preprocessing, feature vector conversion, and classification. Initially, the pre-processing is performed in diverse ways such as single character removal, multi-spaces removal, URL removal, stopword removal, and tokenization. Secondly, the transformation of feature vectors takes place using the N-gram feature vector technique. Finally, mayfly optimization (MFO) with multi-head self-attention based gated recurrent unit (MHSA-GRU) model is employed for the detection and classification of sarcasm. To verify the enhanced outcomes of the DLNLP-SA model, a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.

This study examines the perceptions and results of COVID-19 immunization using sentiment analysis of Twitter data from India. The tweets were collected from January 2021 to March 2023 using relevant hashtags and keywords. The dataset was pre-processed and cleaned before conducting sentiment analysis using Natural Language Processing techniques. Our results show that the overall sentiment toward COVID-19 vaccination in India has been positive, with a majority of tweets expressing support for vaccination and encouraging others to get vaccinated. However, we also identified some negative sentiments related to vaccine hesitancy, side effects, and mistrust in the government and pharmaceutical companies. We further analyzed the sentiment based on demographic factors such as gender, age, and location. The analysis revealed that the sentiment varied across different demographics, with some groups expressing more positive or negative sentiments than others. This study provides insights into the perception and outcomes of COVID-19 vaccination in India and highlights the need for targeted communication strategies to address vaccine hesitancy and increase vaccine uptake in specific demographics.

In the realm of contemporary soft computing practices, analysis of public perceptions and opinion mining (OM) have received considerable attention due to the easy availability of colossal data in the form of unstructured text generated by social media, e-commerce portals, blogs, and other similar web resources. The year 2020 witnessed the gravest epidemic in the history of mankind, and in the present year, we stand amidst a global, massive and exhaustive vaccination movement. Since the inception of the COVID-19 vaccines and their applications, people across the globe, from the ordinary public to celebrities and VIPs have been expressing their fears, doubts, experiences, expectations, dilemmas and perceptions about the current COVID-19 vaccination program. Being very popular among a large class of modern human society, the Twitter platform has been chosen in this research to study public perceptions about this global vaccination drive. More than 112 thousand Tweets from users of different countries around the globe are extracted based on hashtags related to the affairs of the COVID-19 vaccine. A three-tier framework is being proposed in which raw Tweets are extracted and cleaned first, visualized and converted into numerical vectors through word embedding and N-gram models next, and finally analyzed through a few machine learning classifiers with the standard performance metrics, accuracy, precision, recall, and F1-measure. The Logistic Regression (LR) and Adaptive Boosting (AdaBoost) classifiers attended the highest accuracies of 87% and 89% with the Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) word embedding models respectively. Overall, the BoW model achieved slightly better average classification accuracy (78.33%) than that of the TF-IDF model (77.89%). Moreover, the experimental results show that most of the people have a neutral attitude towards the current COVID-19 vaccination drive and people favoring the COVID-19 vaccination program are greater in number than those who doubt it and its consequences.KeywordsVaccineCOVID-19 vaccination programSentiments analysis Twitter Machine learningN-gram

The rapid growth of financial technology (fintech) services in Indonesia has significantly transformed digital transaction behavior, with digital wallets such as DANA becoming widely adopted. Despite high usage rates, public discourse on social media frequently highlights both positive experiences and recurring concerns related to system reliability and data security. Twitter, as a real-time public communication platform, provides a rich source of user-generated content that reflects public perception toward fintech services. This study investigates public sentiment toward DANA fintech services in Indonesia through sentiment analysis of Twitter data using machine learning approaches. Purpose:This study aims to analyze public perception of DANA fintech services based on sentiment expressed on Twitter and to compare the performance of several machine learning classification algorithms in identifying positive, neutral, and negative sentiments in Indonesian-language social media texts. Methods/Study design/approach: The study employs a quantitative descriptive approach using text mining and sentiment analysis techniques. Twitter data were collected via the Twitter API over a specified period and processed through preprocessing stages including text cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest algorithms. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Result/Findings:The results indicate that public sentiment toward DANA is predominantly positive, driven by perceptions of transaction convenience and speed, followed by neutral sentiment, while negative sentiment is mainly associated with system disruptions and data security issues. Among the evaluated algorithms, SVM achieved the highest classification accuracy (91.4%), outperforming Random Forest and KNN. These findings demonstrate that SVM is more effective for sentiment classification of short Indonesian-language texts on social media platforms. Novelty/Originality/Value:This study contributes to the growing body of sentiment analysis research in Indonesia by providing empirical evidence on public perception of fintech services using machine learning techniques. By systematically comparing multiple classification algorithms in the context of Indonesian Twitter data, the study offers methodological insights and practical implications for fintech service providers in improving service quality, communication strategies, and user trust.

BackgroundThe COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance.ObjectiveThe aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter.MethodsWe collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes.ResultsAn increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis.ConclusionsThe analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

Sentiment analysis, a crucial aspect of Natural Language Processing (NLP), plays a pivotal role in understanding public opinion, customer feedback, and user sentiments in various domains. In this study, we present a comprehensive approach to sentiment analysis that incorporates both textual and emoji data, leveraging diverse datasets from sources such as social media, customer reviews, and surveys. Our methodology consists of several key steps, including data collection, pre-processing, feature extraction, feature fusion, and feature selection. For data pre-processing, we apply techniques such as tokenization, lowercasing, stop word removal, and stemming to ensure uniform and meaningful text representation. We also extract emojis from text using regular expressions and convert them into textual representations, facilitating unified processing. Text data is transformed into feature vectors using Term frequency_ Inverse Term frequency (TF-IDF) weighting-based Bag of Words (TF-IDF_BoW) with word frequencies, pre-trained Word2Vec word embeddings, and n-grams to capture local word patterns. Emoji data, on the other hand, is processed using pre-trained emoji embeddings (Emoji2Vec) and emoji frequency counting. The Feature Fusion stage involves the combination of text and emoji features into a single feature vector, with a weighted concatenation approach. In the Feature Selection phase, introduced a Self-Improved Siberian Tiger Optimization (SI-STO), a novel feature selection technique, to identify the most relevant features for sentiment prediction. The sentiment classification model SentEmoNet combines LSTM with an Attention Mechanism and a CNN for capturing text patterns and extracting local features from text and emoji embeddings. We use standard sentiment analysis metrics to assess performance in the model evaluation phase. The proposed model has achieved an accuracy of 97.87%, which is better than the existing ones.

This study aims to analyze the sentiment of the Hindu community toward religious issues on social media, with a specific focus on Kapuas Regency, Central Kalimantan. The development of social media as a digital public sphere has positioned platforms such as Instagram and Twitter as primary spaces for the spontaneous and open expression of opinions, perceptions, and religious attitudes. This study is important because religious issues in digital spaces often influence social harmony and shape religious discourse within society. The research employs a quantitative approach using text mining and sentiment analysis based on Natural Language Processing (NLP). Primary data were collected through web scraping techniques, utilizing Apify for Instagram and asynchronous Python scripts for Twitter, with relevant keywords, hashtags, and geographic indicators. The analysis process includes text preprocessing (cleaning, tokenization, stopword removal, and stemming), followed by sentiment classification using a lexicon-based approach with the InSet dictionary into three categories: positive, negative, and neutral. The analysis results were evaluated using a confusion matrix, along with precision, recall, and F1-score metrics to assess model reliability. The findings indicate that positive sentiment predominates on both Instagram and Twitter, followed by neutral sentiment, while negative sentiment appears in only a small proportion. Positive sentiment is generally associated with expressions of prayer, gratitude, tolerance, and calls for togetherness, whereas negative sentiment tends to emerge in discussions related to ritual differences or responses to socio-religious controversies. The sentiment analysis model achieved an accuracy of 100% on Instagram data (self-evaluation) and 74.4% on Twitter data (manual evaluation), with relatively high precision and recall values, indicating that the results are statistically reliable.

Social media has become an essential part of everyday communication, including within the library context. Twitter, in particular, provides an Application Programming Interface (API) that enables real-time and comprehensive text-mining analyses of users’ perceptions and experiences. This study examines efforts to enhance library service quality by identifying and interpreting user sentiments on Twitter in a more detailed manner, enabling libraries to formulate targeted and effective service improvement strategies. The research consists of several stages: a literature review on sentiment analysis, data collection from Twitter using API-based data crawling, and a series of pre-processing steps, including data cleaning, case folding, tokenization, numerization, stopword removal, and stemming. Data were analyzed using the Aspect-Based Sentiment Analysis (ABSA) method. The findings indicate that the Collection and Facilities aspects receive the highest levels of positive sentiment, while Accessibility and Service attract considerable negative sentiment, signaling priority areas for improvement. Based on these results, libraries may consider extending operational hours, improving digital access, updating collections, and renovating facilities. Additionally, strengthening staff competencies and interpersonal skills is crucial for improving service quality and increasing user satisfaction.

Social media has emerged as a critical communication tool in contemporary society, bringing the world closer by making news and opinions more accessible through virtual platforms. Among these, Twitter stands out with over 350 million users worldwide. However, the highly unstructured nature of Twitter data poses significant challenges for analysis. Recently, the rich content of social media networks has spurred extensive research, particularly in light of the COVID-19 pandemic that has impacted global populations. Concurrent with the search for a vaccine, numerous studies have focused on analyzing public sentiment during the crisis, using data from various social media platforms. This study concentrates on people's opinions regarding COVID-19 vaccines, employing several machine learning models to analyze data from Twitter. It was discovered that the most accurate algorithm was the SGD Classifier, which utilized all three n-gram ranges, achieving an accuracy of 0.75.

Stock news is one of the information sources that can used to monitor stock prices. The information from stock news usually contains positive and negative sentiments that can affect stock prices. Therefore, sentiment analysis is needed to process the sentiment of stock news. The stock news dataset is taken from Kaggle. From these data, there is an imbalanced class between positive and negative sentiment. This research proposed a method to solve the imbalance dataset with random oversampling which worked by randomly replicating several minority classes. This research presents several scenarios of pre-processing text with different stages, intending to get high accuracy. The classification method used in this paper is a robustly optimized Bidirectional Transformer Encoder Representation (RoBERTa). Besides that, this paper also compared with baseline of Machine Learning (ML) such as Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Support Vector Machine, Random Forest Classifier, Logistic Regression and used two different text representation such as TF-IDF and Word2Vec. The best result in this research is obtained using RoBERTa method with the fourth scenario of pre-processing text, in which the stage of pre-processing in this scenario only removing hashtag, without removing punctuation, removing the number, converting number, stop word removal, and lemmatization. The performance result is 0.85 precision, 0,84 recall, 0,84 F1-score, and 86% for accuracy result.