Sentiment Analysis Of Tweets Research Articles

Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods

Nowadays, Twitter is one of the most popular social networking services. People post messages called “tweets”, which may contain photos, videos, links and text. With the vast amount of interaction on Twitter, due to its popularity, analyzing Twitter data is of increasing importance. Tweets related to finance can be important indicators for decision makers if analyzed and interpreted in relation to stock market. Financial tweets containing keywords from the BIST100 index were collected and the tweets were tagged as “POSITIVE”, “NEGATIVE” and “NEUTRAL”. Binary and multi-class datasets were created. Word embedding and pre-trained word embedding were used for tweet representation. As classifiers, Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and GRU-CNN models were used in this study. The best results for binary and multi-class datasets were observed with pre-trained word embedding with the CNN model (83.02%, 72.73%). When word embedding was employed, the Neural Network model had the best results on the multi-class dataset (63.85%) and GRU-CNN had the best results on the binary dataset (80.56%).

Analyzing patients satisfaction level for medical services using twitter data.

Public concern regarding health systems has experienced a rapid surge during the last two years due to the COVID-19 outbreak. Accordingly, medical professionals and health-related institutions reach out to patients and seek feedback to analyze, monitor, and uplift medical services. Such views and perceptions are often shared on social media platforms like Facebook, Instagram, Twitter, etc. Twitter is the most popular and commonly used by the researcher as an online platform for instant access to real-time news, opinions, and discussion. Its trending hashtags (#) and viral content make it an ideal hub for monitoring public opinion on a variety of topics. The tweets are extracted using three hashtags #healthcare, #healthcare services, and #medical facilities. Also, location and tweet sentiment analysis are considered in this study. Several recent studies deployed Twitter datasets using ML and DL models, but the results show lower accuracy. In addition, the studies did not perform extensive comparative analysis and lack validation. This study addresses two research questions: first, what are the sentiments of people toward medical services worldwide? and second, how effective are the machine learning and deep learning approaches for the classification of sentiment on healthcare tweets? Experiments are performed using several well-known machine learning models including support vector machine, logistic regression, Gaussian naive Bayes, extra tree classifier, k nearest neighbor, random forest, decision tree, and AdaBoost. In addition, this study proposes a transfer learning-based LSTM-ETC model that effectively predicts the customer's satisfaction level from the healthcare dataset. Results indicate that despite the best performance by the ETC model with an 0.88 accuracy score, the proposed model outperforms with a 0.95 accuracy score. Predominantly, the people are happy about the provided medical services as the ratio of the positive sentiments is substantially higher than the negative sentiments. The sentiments, either positive or negative, play a crucial role in making important decisions through customer feedback and enhancing quality.

Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models

Sentiment analysis plays a crucial role in understanding public opinion and social media trends. It involves analyzing the emotional tone and polarity of a given text. When applied to Arabic text, this task becomes particularly challenging due to the language’s complex morphology, right-to-left script, and intricate nuances in expressing emotions. Social media has emerged as a powerful platform for individuals to express their sentiments, especially regarding religious and cultural events. Consequently, studying sentiment analysis in the context of Hajj has become a captivating subject. This research paper presents a comprehensive sentiment analysis of tweets discussing the annual Hajj pilgrimage over a six-year period. By employing a combination of machine learning and deep learning models, this study successfully conducted sentiment analysis on a sizable dataset consisting of Arabic tweets. The process involves pre-processing, feature extraction, and sentiment classification. The objective was to uncover the prevailing sentiments associated with Hajj over different years, before, during, and after each Hajj event. Importantly, the results presented in this study highlight that BERT, an advanced transformer-based model, outperformed other models in accurately classifying sentiment. This underscores its effectiveness in capturing the complexities inherent in Arabic text.

Investigating Stock Prediction Using LSTM Networks and Sentiment Analysis of Tweets Under High Uncertainty: A Case Study of North American and European Banks

Investigating Stock Prediction Using LSTM Networks and Sentiment Analysis of Tweets Under High Uncertainty: A Case Study of North American and European Banks

SENTIMENT ANALYSIS OF CODE-MIXED LANGUAGES

In the multilingual context of today, code-mixed language is frequently utilized. It happens when a sentence contains both foreign language vocabulary and grammar. Finding the sentence's polarity value is the aim of sentiment analysis of code-mixed language. It is mostly concerned with sentiment analysis of tweets that contain extra Hindi and English words and symbols. The collection is composed of 20,000 tweets, which produces word-level representations of the tweets for use as input in several models, including CNN, LSTM, and Bi-LSTM. When compared to other models, the Bi-LSTM model performs better. The precision of CNN, LSTM and Bi-LSTM is 65.00%, 58.59% and 54.24% respectively.

Sentiment Analysis of Pandemic Tweets with COVID-19 as a Prototype

Sentiment Analysis of Pandemic Tweets with COVID-19 as a Prototype

XLM-RoBERTa Based Sentiment Analysis of Tweets on Metaverse and 6G

This study employs the XLM-RoBERTa transformer model to perform sentiment analysis on Twitter data, focusing on discussions around the Metaverse and 6G technologies. Through a comprehensive dataset, we analyzed tweets to discern public sentiment, classifying them into negative, neutral, positive, and a calculated final sentiment score. Our results demonstrate a predominance of neutral and positive sentiments, with negative sentiments less frequent but impactful when present. The analysis, visualized through bar charts, box plots, and histograms, suggests a cautiously optimistic public view towards these cutting-edge technologies. The research highlights the effectiveness of XLM-RoBERTa in processing complex language data and contributes to understanding the public perception that may influence the adoption and development of the Metaverse and 6G.

Enhanced Aquila Optimizer Combined Ensemble Bi-LSTM-GRU With Fuzzy Emotion Extractor for Tweet Sentiment Analysis and Classification

Enhanced Aquila Optimizer Combined Ensemble Bi-LSTM-GRU With Fuzzy Emotion Extractor for Tweet Sentiment Analysis and Classification

A Perspective Study on Tweet Sentiment Analysis using Data Mining, Machine Learning and Deep Learning Paradigms

In recent years there has been an increase in interest in collecting and studying text from social networks, review websites, blogs, forums and other forms of user-generated information. The text offers a vast array of ideas from people of diverse profiles, including education, age and their perspectives, region of residence, on how they see goods and services, policy opinions, etc. The analysis of judgments, responses, and emotions drawn from texts is known as sentiment analysis. The sentiment categorization procedure establishes whether a text is subjective or objective, or whether it provokes both positive and negative responses. The most popular method of classification is based on polarity or orientation for accomplishing tweet sentiment analysis. In this paper, a detailed survey on various algorithms used for performing opinion mining, sentiment analysis, tweet sentiment analysis is discussed in detail. The study shows that text preprocessing, data mining, machine learning algorithm and deep learning paradigms plays a vital role in categorization of people’s feeling on a specific topic or a product. In this study, the existing challenges in optimizing the process of tweet sentiment analysis is also discussed and the suggestions for improving is also discussed.

Social mood during the Covid-19 vaccination process in Spain. A sentiment analysis of tweets and social network leaders

In accordance with the cognitive orientation contemplated in the resolution of complex problems posed in public decision-making using decision support systems and social networks, this work studies the possibility of identifying the state of mind of society through the state of mind of network leaders. Using sentiment and emotion analysis as research techniques and Twitter as a representative social network, the study corpus considers tweets and retweets in Spanish about COVID-19 in the period from February 27, 2020 to December 31, 2021. As cognitive orientation claims, the proposed techniques will allow us to extract the arguments that support the different positions and decisions from the analysis of the tweets issued exclusively by social leaders. In the case study considered, the COVID-19 vaccination process in Spain, the reduction in the number of tweets′ authors (more than 8,000) to the network leaders (just 8) was greater than 99 %; and the subsequent reduction in the number of associated tweets was greater than 88 % from the 18,193 tweets in society to the 2,145 tweets of the eight social leaders. The impressive degree of information compression achieved may be useful to establish new directions of social mood analysis applied to healthcare and business management.

Sentiment Analysis of Arabic Tweets on Online Learning During the COVID-19 Pandemic: A Machine Learning and LSTM Approach

Sentiment Analysis of Arabic Tweets on Online Learning During the COVID-19 Pandemic: A Machine Learning and LSTM Approach

Simulating real-time tweet sentiment analysis by different machine learning methods based on spark

Sentiment analysis is essential since it benefits many fields, such as politics and economics. Because much data is generated every moment, a real-time processing system can efficiently analyze sentiment. This paper uses Spark to simulate real-time tweet sentiment analysis, and compares the performances of three machine learning methods, Logistic Regression, Naive Bayes, and Decision Tree. The idea of the real-time tweet sentiment analysis system is using Spark Streaming to send a batch of tweets every fixed period to a machine learning pipeline to predict the emotions of tweets. In the pipeline, tweets will be tokenized first, then the stop words in tweets will be removed. After that, the author uses TF-IDF to extract features, transferring data from unstructured to structured. The last stage is using the machine learning method to predict the sentiments of tweets. By comparing, Logistic Regression has the best performance, and the second one is Naive Bayes, Decision Tree performs not as well as the other two methods.

How do tweeters feel about scientific misinformation: an infoveillance sentiment analysis of tweets on retraction notices and retracted papers

How do tweeters feel about scientific misinformation: an infoveillance sentiment analysis of tweets on retraction notices and retracted papers

Sentiment Analysis of Nepali COVID-19 Tweets using BERT-LSTM

Sentiment Analysis of Nepali COVID-19 Tweets using BERT-LSTM

Sentiment Analysis Regarding Kanjuruan Stadium Polemics Based on Public Opinion Through Twitter Social Media with SVM Classifier Method

Football fans are individuals who promote, motivate, and inspire football. Players of football clubs have both positive and negative fanaticism in both the real world and social media, especially on Twitter. Twitter is one of the communication media. Attracting people worldwide, Twitter saw a record increase in global users, with 313 million monthly active users in 2016 alone; the majority accessed Twitter through mobile devices, accounting for 82 percent of users. Due to the multitude of users tweeting, the latest news and comments become significant worldwide. What happens becomes the main topic, and comments received from many users trigger trending topics on Twitter. This research aims to develop a classification model to predict whether tweets from stadium events are positive or negative from fan perspectives. The classification model is based on a Twitter dataset, and sentiment analysis of tweets was conducted using the Support Vector Machine (SVM) algorithm. The next step involved preprocessing, including case-folding, cleansing, translation to English, and sentiment labeling using VADER. Subsequently, in the preprocessing step 2, tokenization, stopwords, and stemming were applied. For modeling, classic algorithms such as Naïve Bayes and Support Vector Machine were used. The highest accuracy, 87.77%, was achieved using the Support Vector Machine (SVM) algorithm.

A sentiment and content analysis of tweets on monkeypox stigma among the LGBTQ+ community: A cue to risk communication plan.

The stigma associated with monkeypox (mpox) may prevent people from following recommended guidelines. Using a "model of stigma communication," this study maps and determines the mpox stigma on Twitter among LGBTQ+ (Lesbian, gay, bisexual, transgender, queer and more) community. The tweets that contained the terms '#monkeypox', '#MPVS', '#stigma', and '#LGBTQ+' and were published between May 01, 2022 and Sept 07, 2022 were extracted. For sentiment analysis, the VADER, Text Blob, and Flair analysers were implemented. This study evaluated the dynamics of stigma communication based on the "model of stigma communication". A total of 70,832 tweets were extracted, from which 66,387 tweets were passed to the sentiment analyser and 3100 tweets were randomly selected for manual coding. Consolidated Criteria for Reporting Qualitative Research (COREQ) criteria was adopted to report this study. This research provided insight on the cause, communication, and patterns of mpox stigma in the LGBTQ+ community. In the community, stigmatisation was influenced by the group's labelling as the source of monkeypox. Some users believed that mpox resembled previously observed diseases such as HIV/AIDS, and COVID-19. Despite officials and media outlets disseminating information about preventing mpox and stigmatisation, a number of individuals failed to comply. The LGBTQ+ community faced peril in the form of violence due to escalating stigma. Misinformation and misinterpretation spread further stigmatisation. This study indicates that authorities must address misinformation, stigmatization of the LGBTQ+ community, and the absence of a comprehensive risk-communication plan to improve the system. The effects of stigmatization on the vulnerable population must be handled in conjunction with a well-developed risk communication plan, without jeopardizing their wellbeing.

Using COVID-19 Vaccine Attitudes Found in Tweets to Predict Vaccine Perceptions in Traditional Surveys: Infodemiology Study.

Traditionally, surveys are conducted to answer questions related to public health but can be costly to execute. However, the information that researchers aim to extract from surveys could potentially be retrieved from social media, which possesses data that are highly accessible and lower in cost to collect. This study aims to evaluate whether attitudes toward COVID-19 vaccines collected from the Household Pulse Survey (HPS) could be predicted using attitudes extracted from Twitter (subsequently rebranded X). Ultimately, this study aimed to determine whether Twitter can provide us with similar information to that observed in traditional surveys or whether saving money comes at the cost of losing rich data. COVID-19 vaccine attitudes were extracted from the HPS conducted between January 6 and May 25, 2021. Twitter's streaming application programming interface was used to collect COVID-19 vaccine tweets during the same period. A sentiment and emotion analysis of tweets was conducted to examine attitudes toward the COVID-19 vaccine on Twitter. Generalized linear models and generalized linear mixed models were used to evaluate the ability of COVID-19 vaccine attitudes on Twitter to predict vaccine attitudes in the HPS. The results revealed that vaccine perceptions expressed on Twitter performed well in predicting vaccine perceptions in the survey. These findings suggest that the information researchers aim to extract from surveys could potentially also be retrieved from a more accessible data source, such as Twitter. Leveraging Twitter data alongside traditional surveys can provide a more comprehensive and nuanced understanding of COVID-19 vaccine perceptions, facilitating evidence-based decision-making and tailored public health strategies.

Public Perception of ChatGPT and Transfer Learning for Tweets Sentiment Analysis Using Wolfram Mathematica

Understanding public opinion on ChatGPT is crucial for recognizing its strengths and areas of concern. By utilizing natural language processing (NLP), this study delves into tweets regarding ChatGPT to determine temporal patterns, content features, and topic modeling and perform a sentiment analysis. Analyzing a dataset of 500,000 tweets, our research shifts from conventional data science tools like Python and R to exploit Wolfram Mathematica’s robust capabilities. Additionally, with the aim of solving the problem of ignoring semantic information in the LDA model feature extraction, a synergistic methodology entwining LDA, GloVe embeddings, and K-Nearest Neighbors (KNN) clustering is proposed to categorize topics within ChatGPT-related tweets. This comprehensive strategy ensures semantic, syntactic, and topical congruence within classified groups by utilizing the strengths of probabilistic modeling, semantic embeddings, and similarity-based clustering. While built-in sentiment classifiers often fall short in accuracy, we introduce four transfer learning techniques from the Wolfram Neural Net Repository to address this gap. Two of these techniques involve transferring static word embeddings, “GloVe” and “ConceptNet”, which are further processed using an LSTM layer. The remaining techniques center on fine-tuning pre-trained models using scantily annotated data; one refines embeddings from language models (ELMo), while the other fine-tunes bidirectional encoder representations from transformers (BERT). Our experiments on the dataset underscore the effectiveness of the four methods for the sentiment analysis of tweets. This investigation augments our comprehension of user sentiment towards ChatGPT and emphasizes the continued significance of exploration in this domain. Furthermore, this work serves as a pivotal reference for scholars who are accustomed to using Wolfram Mathematica in other research domains, aiding their efforts in text analytics on social media platforms.

Sentiment analysis of Indonesian tweets on COVID-19 and COVID-19 vaccinations

Background Sentiments and opinions regarding COVID-19 and the COVID-19 vaccination on Indonesian-language Twitter are scarcely reported in one comprehensive study, and thus were aimed at our study. We also analyzed fake news and facts, and Twitter engagement to understand people’s perceptions and beliefs that determine public health literacy. Methods We collected 3,489,367 tweets data from January 2020 to August 2021. We analyzed factual and fake news using the string comparison method. The difflib library was used to measure similarity. The user’s engagement was analyzed by averaging the engagement metrics of tweets, retweets, favorites, replies, and posts shared with sentiments and opinions regarding COVID-19 and COVID-19 vaccination. Result Positive sentiments on COVID-19 and COVID-19 vaccination dominated, however, the negative sentiments increased during the beginning of the implementation of restrictions on community activities (PPKM). The tweets were dominated by the importance of health protocols (washing hands, keeping distance, and wearing masks). Several types of vaccines were on top of the word count in the vaccine subtopic. Acceptance of the vaccination increased during the studied period, and the fake news was overweighed by the facts. The tweets were dynamic and showed that the engaged topics were changed from the nature of COVID-19 to the vaccination and virus mutation which peaked in the early and middle terms of 2021. The public sentiment and engagement were shifted from hesitancy to anxiety towards the safety and effectiveness of the vaccines, whilst changed again into wariness on an uprising of the delta variant. Conclusion Understanding public sentiment and opinion can help policymakers to plan the best strategy to cope with the pandemic. Positive sentiments and fact-based opinions on COVID-19, and COVID-19 vaccination had been shown predominantly. However, sufficient health literacy levels could yet be predicted and sought for further study.

Deep Learning-Based Sentiment and Stance Analysis of Tweets About Vaccination

Sentiment analysis and stance detection are interrelated problems of affective computing, and their outputs commonly complement each other. The focus of this article is to determine sentiments and stances of Twitter users about vaccination. A tweet dataset on COVID-19 vaccination is compiled and jointly annotated with sentiment and stance. This deep learning approach employs BERT, which is a model based on pre-trained transformers. The generative deep learning model, ChatGPT, is also used for stance and sentiment analysis on the dataset. ChatGPT achieves the best performance for stance detection, while BERT is the best performer for sentiment analysis. This study is the first one to observe stance and sentiment detection performance of ChatGPT on health-related tweets. This article also includes a full-fledged system proposal based on automatic sentiment and stance analysis. COVID-19 pandemic is an impactful global public health phenomenon, and hence, joint extraction of sentiments and stances from health-related tweets can profoundly contribute to health-related decision-making processes.