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Since the spread of the coronavirus flu in 2019 (hereafter referred to as COVID-19), millions of people worldwide have been affected by the pandemic, which has significantly impacted our habits in various ways. In order to eradicate the disease, a great help came from unprecedentedly fast vaccines development along with strict preventive measures adoption like lockdown. Thus, world wide provisioning of vaccines was crucial in order to achieve the maximum immunization of population. However, the fast development of vaccines, driven by the urge of limiting the pandemic caused skeptical reactions by a vast amount of population. More specifically, the people's hesitancy in getting vaccinated was an additional obstacle in fighting COVID-19. To ameliorate this scenario, it is important to understand people's sentiments about vaccines in order to take proper actions to better inform the population. As a matter of fact, people continuously update their feelings and sentiments on social media, thus a proper analysis of those opinions is an important challenge for providing proper information to avoid misinformation. More in detail, sentiment analysis (Wankhade et al. in Artif Intell Rev 55(7):5731–5780, 2022. https://doi.org/10.1007/s10462-022-10144-1) is a powerful technique in natural language processing that enables the identification and classification of people feelings (mainly) in text data. It involves the use of machine learning algorithms and other computational techniques to analyze large volumes of text and determine whether they express positive, negative or neutral sentiment. Sentiment analysis is widely used in industries such as marketing, customer service, and healthcare, among others, to gain actionable insights from customer feedback, social media posts, and other forms of unstructured textual data. In this paper, Sentiment Analysis will be used to elaborate on people reaction to COVID-19 vaccines in order to provide useful insights to improve the correct understanding of their correct usage and possible advantages. In this paper, a framework that leverages artificial intelligence (AI) methods is proposed for classifying tweets based on their polarity values. We analyzed Twitter data related to COVID-19 vaccines after the most appropriate pre-processing on them. More specifically, we identified the word-cloud of negative, positive, and neutral words using an artificial intelligence tool to determine the sentiment of tweets. After this pre-processing step, we performed classification using the BERT + NBSVM model to classify people's sentiments about vaccines. The reason for choosing to combine bidirectional encoder representations from transformers (BERT) and Naive Bayes and support vector machine (NBSVM) can be understood by considering the limitation of BERT-based approaches, which only leverage encoder layers, resulting in lower performance on short texts like the ones used in our analysis. Such a limitation can be ameliorated by using Naive Bayes and Support Vector Machine approaches that are able to achieve higher performance in short text sentiment analysis. Thus, we took advantage of both BERT features and NBSVM features to define a flexible framework for our sentiment analysis goal related to vaccine sentiment identification. Moreover, we enrich our results with spatial analysis of the data by using geo-coding, visualization, and spatial correlation analysis to suggest the most suitable vaccination centers to users based on the sentiment analysis outcomes. In principle, we do not need to implement a distributed architecture to run our experiments as the available public data are not massive. However, we discuss a high-performance architecture that will be used if the collected data scales up dramatically. We compared our approach with the state-of-art methods by comparing most widely used metrics like Accuracy, Precision, Recall and F-measure. The proposed BERT + NBSVM outperformed alternative models by achieving 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. These promising results will be properly discussed in next sections. The use of artificial intelligence methods and social media analysis can lead to a better understanding of people's reactions and opinions about any trending topic. However, in the case of health-related topics like COVID-19 vaccines, proper sentiment identification could be crucial for implementing public health policies. More in detail, the availability of useful findings on user opinions about vaccines can help policymakers design proper strategies and implement ad-hoc vaccination protocols according to people's feelings, in order to provide better public service. To this end, we leveraged geospatial information to support effective recommendations for vaccination centers. [ABSTRACT FROM AUTHOR]

Online social network is one of the most prominent media that holds information about society's epidemic problem. Due to privacy reasons, most of the users will not disclose their location. Detecting the location of the tweet users is required to track the geographic location of the spreading diseases. This work aims to detect the spreading location of the COVID-19 disease from the Twitter users and content discussed in the tweet. COVID-19 is a disease caused by the "novel coronavirus." About 80% of confirmed cases recover from the disease. However, one out of every six people who get COVID-19 can become seriously ill, stated by the World health organization. Inferring the user location for identifying the spreading location for the disease is a very challenging task. This paper proposes a new technique based on a hypergraph model to detect the Twitter user's locations based on the spreading disease. This model uses hypergraph with weighting factor technique to infer the spreading disease's spatial location. The accuracy of prediction can be improved when a massive volume of streaming data is analyzed. The Helly property of the hypergraph was applied to discard less potential words from the text analysis, which claims this work of unique nature. A weighting factor was introduced to calculate the score of each location for a particular user. The location of each user is predicted based on the one that possesses the highest weighting factor. The proposed framework has been evaluated and tested for various measures like precision, recall and F-measure. The promising results obtained have substantiated the claim for this work compared to the state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

User-generated content on social media platforms has reached big data levels. Sentiment analysis of this data provides opportunities to gain valuable insights into any domain. However, analyzing real-world data may confront the challenge of class imbalance, which can adversely affect the generalization ability of models due to majority class overfitting. Therefore, having an efficient model that manages any scenario of imbalanced data is practically needed. In this light, this work proposes different models based on studying the impact of data quality and transfer learning through pre-trained embeddings on boosting minority class detection. The proposed models are tested on imbalanced datasets related to social media and education. The experimental results highlight the effectiveness of Wor2vec, Glove, and Fasttext embeddings with preprocessed data. In contrast, BERT embeddings present better results with no-preprocessed data. Furthermore, in comparison with other methods, the best-performing model resulting from this study shows outperformance with notable improvements. [ABSTRACT FROM AUTHOR]

Consumer sentiment is one of the essential measures of predictive recommendations in travel and tourism. Nowadays, a massive amount of data is available on the online platform related to consumer sentiment, which may help draw insights into how consumers provide feedback and how we can use that feedback to predict recommendations using machine learning techniques. In this study, we have designed a predictive recommendations method that predicts the consumer recommendations in travel and tourism, particularly in the case of the airline. We developed our predictive methods as a multi-label classification system. We implemented K-Nearest Neighbors, Support Vector Machine, Multi-layer Perceptron, Logistic Regression, Random Forest, and Ensemble Learning as basic classification models to train our model. Further, we boosted our predictive model by implementing state-of-the-art partitioning methods to partition the label space in lower spaces and utilized label space partitioning approaches, namely RAkELo and Louvain, as a transformation technique to transform every label set into a multi-class classification problem. The suggested model obtained higher performance in terms of accuracy using various evaluation measures compared to other binary classifications. Furthermore, the valuable traveler may get benefited from this approach in making their predictive decisions before travel. [ABSTRACT FROM AUTHOR]

Sarcasm is a type of speech where people use positive words to convey a negative message. Recently, only a few research have been presented that focus on the entire spectrum of sarcasm in order to identify sarcastic sentiments present in both the image and the text. This work presents a novel firebug swarm optimization-based long short-term memory (FSO-LSTM) architecture to identify the sarcastic sentiments present in tweets. To identify the facial expressions of the users, the proposed FSO-based LSTM architecture is trained using the CK + dataset. The FSO algorithm is used to optimize the weighting factors of the LSTM architecture and also minimize the root-mean-square error (RMSE) and mean absolute error. The proposed method primarily attempts to address two challenging issues in sarcasm detection: the high number of false negatives and the fact that polite tweets often go undetected. The user's mood changes (sarcastic) such as rude, polite, furious, and impassive can be identified using the proposed model. Hence, the proposed classifier is capable of analyzing the behavior change of the user by collecting the past twitter account history. The efficiency of the proposed methodology is evaluated using different performance metrics such as accuracy, RMSE, confusion matrix, and loss. The proposed methodology offers an average classification accuracy of 97.25% classification accuracy when compared to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]