Sentiment Analysis Of Tweets Research Articles

Prediction of Twitter Sentiment Analysis by Using Naive Bayes Algorithm

The purpose of our study is to do a sentiment analysis of tweets to determine whether they are positive, negative, or neutral. Many businesses use sentiment analysis to determine how well their products and companies are performing in the marketplace. In a burgeoning discipline, sentiment analysis is now being used to predict the outcome of political elections and polls. Twitter is an internet platform that allows users to send tweets with a maximum of 140 characters. We intend to get a representation of public opinion by analyzing the sentiments conveyed in the tweets due to the enormous amount of data we are using. This research investigates sentiment analysis, which involves extracting many tweets and analyzing them using a machine-learning technique like the naive Bayes classifier. Results are classified as several positive, negative or neutral and it is also expressed in the pie chart and bar chart. With the speedup development of websites, informal organizations, websites, online entries, surveys, feelings, proposals, evaluations, and criticism are created by scholars. This author created opinion content that can be about books, individuals, lodgings, items, research, occasions, and so on. These sentiments become extremely useful for organizations, state-run administrations, and people. While this content is intended to be helpful, a greater part of this essayist's produced content requires utilizing the message mining procedures and feel analysis. In any case, there are a few difficulties confronting the opinion examination and assessment process. These difficulties become snags in dissecting the exact significance of feelings and distinguishing the appropriate opinion extremity. Opinion analysis is the act of applying regular language handling and text examination strategies to recognize and remove abstract data from text. This paper presents a review of the feeling examination challenges pertinent to their methodologies and strategies

Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques.

The outbreak of the COVID-19 pandemic has also triggered a tsunami of news, instructions, and precautionary measures related to the disease on social media platforms. Despite the considerable support on social media, a large number of fake propaganda and conspiracies are also circulated. People also reacted to COVID-19 vaccination on social media and expressed their opinions, perceptions, and conceptions. The present research work aims to explore the opinion dynamics of the general public about COVID-19 vaccination to help the administration authorities to devise policies to increase vaccination acceptance. For this purpose, a framework is proposed to perform sentiment analysis of COVID-19 vaccination-related tweets. The influence of term frequency-inverse document frequency, bag of words (BoW), Word2Vec, and combination of TF-IDF and BoW are explored with classifiers including random forest, gradient boosting machine, extra tree classifier (ETC), logistic regression, Naïve Bayes, stochastic gradient descent, multilayer perceptron, convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and recurrent neural network (RNN). Results reveal that ETC outperforms using BoW with a 92% of accuracy and is the most suitable approach for sentiment analysis of COVID-19-related tweets. Opinion dynamics show that sentiments in favor of vaccination have increased over time.

Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period

In a time where social media is fundamental for any political campaign and to share a message with an electoral audience, this study searches for a conclusion of the actual persuasion capacity of social media in the electors when they need to decide whom to vote for as their next government. For this, it compares the sentiment that Social Media users demonstrated during an electoral period with the actual results of those elections. For this analysis, it was used, as a case study, tweets mentioning the two major English parties, Conservative and Labor, their respective candidates for the position of prime minister, and terms that identified their political campaign during the electoral period of the General Elections of the United Kingdom that occurred on December 12, 2019. Data were collected using R. The treatment and analysis were done with R and RapidMiner. Results show that tweets’ sentiment is not a reliable election results predictor. Additionally, results also show that it is impossible to state that social media impacts voting decisions. At least not from the polarity of the sentiment of opinions on social media.

Sentiment analysis and emotion detection of post-COVID educational Tweets: Jordan case.

Education evolved dramatically under Covid-19, and owing to the conditions, distant learning became mandatory. However, this has opened new realities to the educational business under the label of "Hybrid-Learning," where educational institutions are still using online learning in addition to face-to-face learning, which has changed people's lives and split their opinions and emotions. As a result, this study investigated the Jordanian community's perspectives and feelings on the transition from pure face-to-face education to blended education by examining related tweets in the post-COVID era. Specifically, using NLP Emotion detection and Sentiment Analysis approaches, as well as deep learning models. As a result of analyzing the collected tweets, 18.75% of studied Jordanian's community sample are dissatisfied (Anger and Hate), 21.25% are negative (Sad), 13% are Happy, and 24.50 percent are Neutral about it.

Sentiment analysis of Malayalam tweets using bidirectional encoder representations from transformers: a study

Sentiment analysis on views and opinions expressed in Indian regional languages has become the current focus of research. But, compared to a globally accepted language like English, research on sentiment analysis in Indian regional languages like Malayalam are very low. One of the major hindrances is the lack of publicly available Malayalam datasets. This work focuses on building a Malayalam dataset for facilitating sentiment analysis on Malayalam texts and studying the efficiency of a pre-trained deep learning model in analyzing the sentiments latent in Malayalam texts. In this work, a Malayalam dataset has been created by extracting 2,000 tweets from Twitter. The bidirectional encoder representations from transformers (BERT) is a pretrained model that has been used for various natural language processing tasks. This work employs a transformer-based BERT model for Malayalam sentiment analysis. The efficacy of BERT in analyzing the sentiments latent in Malayalam texts has been studied by comparing the performance of BERT with various machine learning models as well as deep learning models. By analyzing the results, it is found that a substantial increase in accuracy of 5% for BERT when compared with that of Bi-GRU, which is the next bestperforming model.

Prediction of movie success based on machine learning and twitter sentiment analysis using internet movie database data

<span lang="EN-US">Nowadays, predicting the success of a new movie is a crucial task. In this work, the hybrid approach considers the movie features as well as sentiment expressed in the movie review to predict the success rate of a movie. Multiple movie features such as title, director, star cast, and writer. Are considered for prediction. The related raw data is collected from the internet movie database (IMDb) website and after pre-processing, the collected data is used to generate the supervised machine learning model. Different supervised learning models are compared and the one with the best results is used further. The mean squared error, root mean squared error and r2 score of the models generated are comparable with existing models. Further, sentiment analysis of the movie-related tweets is performed. The accuracy of best sentiment analysis model is 88.47%. Finally, the two models are combined to give the success prediction rating of new movies and the results of the hybrid model are encouraging. The proposed model may be used to find the top-rated movies of a particular calendar year.</span>

Sentiment Analysis of Tweets on Soda Taxes.

As a primary source of added sugars, sugar-sweetened beverage (SSB) consumption may contribute to the obesity epidemic. A soda tax is an excise tax charged on selling SSBs to reduce consumption. Currently, 8 cities/counties in the United States have imposed soda taxes. This study assessed people's sentiments toward soda taxes in the United States based on social media posts on Twitter. We designed a search algorithm to systematically identify and collect soda tax-related tweets posted on Twitter. We built deep neural network models to classify tweets by sentiments. Computer modeling. Approximately 370 000 soda tax-related tweets posted on Twitter from January 1, 2015, to April 16, 2022. Sentiment associated with a tweet. Public attention paid to soda taxes, indicated by the number of tweets posted annually, peaked in 2016, but has declined considerably ever since. The decreasing prevalence of tweets quoting soda tax-related news without revealing sentiments coincided with the rapid increase in tweets expressing a neutral sentiment toward soda taxes. The prevalence of tweets expressing a negative sentiment rose steadily from 2015 to 2019 and then slightly leveled off, whereas that of tweets expressing a positive sentiment remained unchanged. Excluding news-quoting tweets, tweets with neutral, negative, and positive sentiments occupied roughly 56%, 29%, and 15%, respectively, during 2015-2022. The authors' total number of tweets posted, followers, and retweets predicted tweet sentiment. The finalized neural network model achieved an accuracy of 88% and an F1 score of 0.87 in predicting tweet sentiments in the test set. Despite its potential to shape public opinion and catalyze social changes, social media remains an underutilized source of information to inform government decision making. Social media sentiment analysis may inform the design, implementation, and modification of soda tax policies to gain social support while minimizing confusion and misinterpretation.

Threshold of Depression Measure in the Framework of Sentiment Analysis of Tweets: Managing Risk during a Crisis Period Like the COVID-19 Pandemic

The COVID-19 pandemic has had a devastating impact on the world. The surge in the number of daily new cases and deaths around the world and in South Africa, in particular, has increased fear, psychological breakdown, and uncertainty among the population during the COVID-19 pandemic period, leading many to resort to prayer, meditation, and the consumption of religious media as coping measures. This study analyzes social media data to examine the perceptions and attitudes of the South African community toward religion as well as their well-being appreciation during the COVID-19 period. We extract four sets of tweets related to COVID-19, religion, life purpose, and life experience, respectively, by users within the geographical area of South Africa and compute their sentiment scores. Then, a Granger causality test is conducted to assess the causal relationship between the four time series. While the findings reveal that religious sentiment scores Granger-causes life experience, COVID-19 similarly Granger-causes life experience, illustrating some shifts experienced within the community during the crisis. This study further introduces for the first time a Threshold of Depression measure in the sentiment analysis framework to assist in managing the risk induced by extremely negative sentiment scores. Risk management during a period of crisis can be a hectic task, especially the level of distress or depression the community is experiencing in order to offer adequate mental support. This can be assessed through the Conditional Threshold of Depression which quantifies the threshold of depression of a community conditional on a given variable being at its Threshold of Depression. The findings indicate that the well-being indicators (life purpose and life experience) provide the highest values of this threshold and could be used to monitor the emotions of the population during periods of crisis to support the community in crisis management.

A Russia-Ukraine Conflict Tweets Sentiment Analysis Using Bidirectional LSTM Network

A Russia-Ukraine Conflict Tweets Sentiment Analysis Using Bidirectional LSTM Network

The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model.

Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus.

Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches

Social media is a platform where people communicate, share content, and build relationships. Due to the current pandemic, many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their feelings. In this paper, we analyse the sentiments of Indian citizens about the COVID-19 pandemic and vaccination drive using text messages posted on the Twitter platform. The sentiments were classified using deep learning and lexicon-based techniques. A lexicon-based approach was used to classify the polarity of the tweets using the tools VADER and NRCLex. A recurrent neural network was trained using Bi-LSTM and GRU techniques, achieving 92.70% and 91.24% accuracy on the COVID-19 dataset. Accuracy values of 92.48% and 93.03% were obtained for the vaccination tweets classification with Bi-LSTM and GRU, respectively. The developed models can assist healthcare workers and policymakers to make the right decisions in the upcoming pandemic outbreaks.

Sentiment analysis of COVID-19 tweets from selected hashtags in Nigeria using VADER and Text Blob analyser

BackgroundInformation is essential for growth; without it, little can be accomplished. Data gathering has seen significant changes throughout the previous few centuries because of the certain transitory medium. The look and style of information transference are affected by the employment of new and emerging technologies, some of which are efficient, others are reliable, and many more are quick and effective, but a few were disappointing for various reasons.AimsThis study aims at using TextBlob and VADER analyser with historical tweets, to analyse emotional responses to the coronavirus pandemic (COVID-19). It shows us how much of a sociological, environmental, and economic impact it has in Nigeria, among other things. This study would be a tremendous step forward for students, researchers, and scholars who want to advance in fields like data science, machine learning, and deep learning.MethodologyThe hashtag ‘COVID-19' was used to collect 1,048,575 tweets from Twitter. The tweets were pre-processed with a Twitter tokenizer, while TextBlob and Valence Aware Dictionary for Sentiment Reasoning (VADER) were used for text mining and sentiment analysis, respectively. Topic modelling was done with Latent Dirichlet Allocation and visualized with Multidimensional scaling.ResultsThe result of the VADER sentiment returned 39.8%, 31.3%, and 28.9%, positive, neutral, and negative sentiment, respectively, while the result of the TextBlob sentiment returned 46.0%, 36.7%, and 17.3%, neutral, positive, and negative sentiment, respectively.ConclusionWith all of this, information from social media may be used to help organizations, governments, and nations around the world make smart and effective decisions about how to restrict and limit the negative effects of COVID-19. Also, know the opinion and challenges of people, then deal with the problem of misinformation. It is concluded that with popular belief a significant number of the populace regards COVID-19 as a virus that has come to stay, some believe it will eventually be conquered.

A BERT Framework to Sentiment Analysis of Tweets.

Sentiment analysis has been widely used in microblogging sites such as Twitter in recent decades, where millions of users express their opinions and thoughts because of its short and simple manner of expression. Several studies reveal the state of sentiment which does not express sentiment based on the user context because of different lengths and ambiguous emotional information. Hence, this study proposes text classification with the use of bidirectional encoder representations from transformers (BERT) for natural language processing with other variants. The experimental findings demonstrate that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs well in terms of accuracy rate, precision rate, recall rate, and F1-score compared to when it was used with Word2vec and when it was used with no variant.

Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets

Sentiment Analysis (SA) is one of the Machine Learning (ML) techniques that has been investigated by several researchers in recent years, especially due to the evolution of novel data collection methods focused on social media. In literature, it has been reported that SA data is created for English language in excess of any other language. It is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic language. An earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the text. Neural word embedding has been employed in literature, since it is less labor-intensive than automatic feature engineering. By ignoring the context of sentiment, most of the word-embedding models follow syntactic data of words. The current study presents a new Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets (DFODL-SAAT) model. The aim of the presented DFODL-SAAT model is to distinguish the sentiments from opinions that are tweeted in Arabic language. At first, data cleaning and pre-processing steps are performed to convert the input tweets into a useful format. In addition, TF-IDF model is exploited as a feature extractor to generate the feature vectors. Besides, Attention-based Bidirectional Long Short Term Memory (ABLSTM) technique is applied for identification and classification of sentiments. At last, the hyperparameters of ABLSTM model are optimized using DFO algorithm. The performance of the proposed DFODL-SAAT model was validated using the benchmark dataset and the outcomes were investigated under different aspects. The experimental outcomes highlight the superiority of DFODL-SAAT model over recent approaches.

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Analyzing Public Sentiment and Acceptance of the Bimodal Voter Accreditation System in Nigeria using Sentiment Analysis and RoBERTa Model

The research article investigates the satisfaction of voters with the deployment of the Bimodal Voter Accreditation System (BVAS) in the 2023 Nigerian presidential election by conducting a sentiment analysis of Twitter posts. The article aims to analyze public opinion and sentiment towards the BVAS system and provide valuable insights that can inform policy decisions and actions related to the implementation and improvement of the system. In this study, Twitter posts related to BVAS and the election were collected using the Twitter streaming API. A total of 997,400 tweets were obtained. The data preprocessing phase involved cleaning the tweets by removing noise, filtering out irrelevant comments, and segmenting the text into words or phrases. The sentiment analysis itself was conducted using the Robustly Optimized BERT Pretraining Approach (ROBERTa), which is a language model introduced by Facebook AI. ROBERTa is a powerful model for sequence modeling and is optimized for sentiment analysis. It predicts the sentiment of each tweet as either positive or negative. This study reveals that attitudes toward the employment of BVAS in the Nigerian presidential election of 2023 are neutral, the acceptance rate is low compared to the rejection rate. Although, there is a large percentage of people who are unfavorable to the introduction of this technology. The study utilized Twitter data exclusively for sentiment analysis, which may limit its accuracy in representing the sentiments of the entire population. Twitter users may not be a fully representative sample and could introduce biases. Additionally, the sentiment analysis focused on English tweets only, potentially overlooking sentiments expressed in other languages spoken in Nigeria, like Hausa, Yoruba, or Igbo. To achieve a more comprehensive analysis, future research could expand to include sentiment analysis of tweets in different languages.

Towards a polynomial approximation of support vector machine accuracy applied to Arabic tweet sentiment analysis

Machine learning algorithms have become very frequently used in natural language processing, notably sentiment analysis, which helps determine the general feeling carried within a text. Among these algorithms, Support Vector Machines have proven powerful classifiers especially in such a task, when their performance is assessed through accuracy score and f1-score. However, they remain slow in terms of training, thus making exhaustive grid-search experimentations very time-consuming. In this paper, we present an observed pattern in SVM's accuracy, and f1-score approximated with a Lagrange polynomial.

Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

Sentiment Analysis (SA), a Machine Learning (ML) technique, is often applied in the literature. The SA technique is specifically applied to the data collected from social media sites. The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process. In this background, the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets (QPSODL-SAAT). The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic. Initially, the data pre-processing is performed to convert the raw tweets into a useful format. Then, the word2vec model is applied to generate the feature vectors. The Bidirectional Gated Recurrent Unit (BiGRU) classifier is utilized to identify and classify the sentiments. Finally, the QPSO algorithm is exploited for the optimal fine-tuning of the hyperparameters involved in the BiGRU model. The proposed QPSODL-SAAT model was experimentally validated using the standard datasets. An extensive comparative analysis was conducted, and the proposed model achieved a maximum accuracy of 98.35%. The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches, such as the Surface Features (SF), Generic Embeddings (GE), Arabic Sentiment Embeddings constructed using the Hybrid (ASEH) model and the Bidirectional Encoder Representations from Transformers (BERT) model.

Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre- and Peri-COVID-19 Pandemic Retrospective Study.

The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19. We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users' insomnia experiences, using logistic regression. We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval. The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep.

Sentiment Analysis of Tweets During the COVID-19 Pandemic Using Multinomial Logistic Regression

Recently, the research on sentimental analysis has been growing rapidly. The tweets of social media are extracted to analyze the user sentiments. Many of the studies prefer to apply machine learning algorithms for performing sentiment analysis. In the current pandemic, there is an utmost importance to analyze the sentiments or behavior of a person to make the decisions as the whole world is facing lockdowns in multiple phases. The lockdown is psychologically affecting the human behavior. This study performs a sentimental analysis of Twitter tweets during lockdown using multinomial logistic regression algorithm. The proposed system framework follows the pre-processing, polarity and scoring, and feature extracting before applying the machine learning model. For validating the performance of proposed framework, other three majorly used machine learning based models-- namely decision tree, naïve Bayes, and K-nearest neighbors-- are implemented. Experimental results prove that the proposed framework provides improved accuracy over other models.