Sentiment Analysis Of Twitter Data Research Articles

Exploring Stress Detection in Tweets: A Comparative Business Analysis of Classification Algorithms

Abstract: In today's digitally-driven landscape, social media platforms such as Twitter, Facebook, and WordPress have emerged as critical conduits for public discourse, resulting in an overwhelming influx of unprocessed data, particularly on Twitter. Coping with the sheer volume of this data presents a significant challenge. To address this, sentiment analysis, a technique that categorizes sentiments into positive, negative, or neutral, offers a promising solution. This study delves into three main approaches for sentiment analysis: Machine learning-based methods, Sentiment lexicon-based approaches, and Hybrid methods. Its primary research objectives revolve around the identification of suitable algorithms and metrics for evaluating the performance of Machine Learning Classifiers. Additionally, the study aims to compare these metrics with respect to the dataset size, gauging their impact on the most appropriate sentiment analysis algorithm.The research methodology applied is experimental, entailing a rigorous assessment of algorithms using carefully selected metrics. The results of this investigation spotlight Naïve Bayes, Random Forest, XGBoost, and CNN-LSTM as the leading machine learning algorithms under consideration. These algorithms are assessed based on key performance metrics such as precision, accuracy, F1 score, and recall. Notably, the CNN-LSTM model emerges as the optimal choice for sentiment analysis of Twitter data within the specified dataset size, achieving a remarkable accuracy rate of 88%.In summary, this research successfully pinpoints the most suitable algorithm for sentiment analysis of Twitter data, especially in the context of dataset size. The CNN- LSTM model showcases its effectiveness, serving as a robust tool for sentiment analysis and delivering an impressive accuracy rate. This study significantly enhances our comprehension of public sentiment on Twitter, providing valuable insights into the ever-evolving realm of digital discourse and the analysis of vast unprocessed data

Effects of dehumanization and disgust-eliciting language on attitudes toward immigration: a sentiment analysis of Twitter data

Attitudes towards immigration may be influenced by dehumanization and disgust: The more people dehumanize immigrants and the more disgusted they feel, the more negative their attitudes toward immigrants tend to be. Despite the fact that the majority of U.S. adults are on social media, however, little is known about how exposure to social media that links dehumanization, disgust and immigration influences users’ attitudes on this issue over time. We used Twitter data, machine learning and sentiment analysis to explore this question. Results showed that dehumanizing and/or disgust-eliciting language appeared in 66% of sampled immigration-relevant tweets. Surprisingly, exposure to both types of language in such tweets related to increases in positive sentiment about immigration over time. There was evidence of Granger-causality only for dehumanizing language, however, and only when controlling for communicators’ political affiliation. These findings suggest social media exposure may influence public attitudes toward immigration in unexpected ways.

Who is responsible for fixing the food system? A framing analysis of media reactions to the UK's National Food Strategy

PurposeHigh levels of child obesity alongside rising stunting and the absence of a coherent food policy have deemed UK’s food system to be broken. The National Food Strategy (NFS) was debated intensely in media, with discussions on how and who should fix the food system.Design/methodology/approachUsing a mixed methods approach, the authors conduct framing analysis on traditional media and sentiment analysis of twitter reactions to the NFS to identify frames used to shape food system policy interventions.FindingsThe study finds evidence that the media coverage of the NFS often utilised the tropes of “culture wars” shaping the debate of who is responsible to fix the food system – the government, the public or the industry. NFS recommendations were portrayed as issues of free choice to shift the debate away from government action correcting for market failure. In contrast, the industry was showcased as equipped to intervene on its own accord. Dietary recommendations made by the NFS were depicted as hurting the poor, painting a picture of helplessness and loss of control, while their voices were omitted and not represented in traditional media.Social implicationsBritish media’s alignment with free market economic thinking has implications for food systems reform, as it deters the government from acting and relies on the invisible hand of the market to fix the system. Media firms should move beyond tropes of culture wars to discuss interventions that reform the structural causes of the UK’s broken food systems.Originality/valueAs traditional media coverage struggles to capture the diversity of public perception; the authors supplement framing analysis with sentiment analysis of Twitter data. To the best of our knowledge, no such media (and social media) analysis of the NFS has been conducted. The paper is also original as it extends our understanding of how media alignment with free market economic thinking has implications for food systems reform, as it deters the government from acting and relies on the invisible hand of the market to fix the system.

Sentiment analysis of twitter data to detect and predict political leniency using natural language processing

Sentiment analysis of twitter data to detect and predict political leniency using natural language processing

Response to the critique of “Public's perception on nursing education during the COVID-19 pandemic: Sentiment analysis of Twitter data”

Response to the critique of “Public's perception on nursing education during the COVID-19 pandemic: Sentiment analysis of Twitter data”

Mining Netizen's Opinion on Sustainable Agriculture: Sentiment Analysis of Twitter Data

This research aims to measure public sentiment related to sustainable agriculture on the Twitter social media platform. The research method involves the extraction and classification of tweet data using a Python Library called VADER (Valence Aware Dictionary and Sentiment Reasoner). The research utilized tweet data posted in the past one year. The results showed fluctuations and decreases in the number of tweets discussing sustainable agriculture. The location with the most tweet activity around sustainable agriculture was Brussels, Belgium, with 642 tweets during the observation period. Word cloud analysis on keywords showed that in positive sentiments, words such as "food security" and "climate change" dominated the visualization. On the other hand, in negative sentiments, words such as "farmer" and "private farmland" appeared more frequently. Overall, the majority of tweets expressed a positive attitude towards sustainable agriculture, with 68.5% positive sentiment. A total of 22.3% of tweets showed neutral sentiments, with no strong positive or negative tendencies. Only 9.1% of tweets contained negative sentiment, indicating that a small proportion of tweets expressed less favorable views towards sustainable agriculture.

Public’s perception on nursing education during the COVID-19 pandemic: SENTIMENT analysis of Twitter data

With the emergence of nurses as heroes on the front lines during the COVID-19 pandemic, posts on Twitter about nursing and nursing education began to increase. This study aims to make a sentiment analysis on Twitter posts regarding society's perception of nursing education during the COVID-19 pandemic and to shed light on concerns, sentiments, and experiences related to nursing education during the pandemic. The text mining method was used to analyze the sentiment analysis of Twitter data. Between July 1st, 2021, and July 1st, 2022, during the COVID-19 pandemic, a total of 30,194. Twitter messages in English were analyzed using the “nursing education” hashtag and keyword. All data cleaning and analysis were carried out with R software, and the tweet data set was analyzed using the frequency of keywords and sentiment analysis. Sentiment analysis of each tweet was conducted using various sentiment analysis dictionaries. The results showed that nursing, education, health, school, and nurses were the most used keywords. In the sentiment analysis conducted during the pandemic, 84 % of the tweets comprised positive, 12 % negative, and 4 % neutral sentiments. The conclusions highlight the importance of knowing and appreciating the contributions of nurses and nursing students during the pandemic and supporting more nurse professionals during crises such as the COVID-19 pandemic by addressing the problems during nursing education.

Data Driven Analysis of Borobudur Ticket Sentiment Using Naïve Bayes.

 The recent growth of social media is hugely influential and plays a significant role in various aspects of people's lives in the digital era. Twitter is a social media network that is widely used in Indonesia. Twitter users can engage in multiple activities, such as communicating with individuals and groups, writing daily activities, promoting businesses, arguing, and expressing ideas about a topic of discussion. At the beginning of June 2022, raising the entrance charge for Borobudur Temple became one of the concerns that caused a lot of conversation in the real world and on other social media platforms, including Twitter. The plan to increase the price of entrance tickets to Borobudur Temple has drawn various pro and con reactions in the community. This study analyzes public sentiment toward the planned increase in ticket prices for Borobudur Temple. Sentiment analysis of Twitter data can be implemented using a classification algorithm. The classification algorithms widely used in sentiment analysis research are Nave Bayes (NB) and Decision Tree (DT). The reason for choosing Nave Bayes and Decision Tree is because this algorithm is the most popular algorithm used to process text data classification; the process is simple, efficient, and performs well. This study's dataset source was taken from social media sites like Twitter. In comparison to the Decision Tree, which generates a test percentage of 100%, the accuracy of the Naive Bayes approach, based on the evaluation of the test results, produces the highest accuracy number. At the same time, the Decision Tree method's accuracy test yields a test accuracy value of 35.97%.

A Review on Sentiment Analysis of Twitter Data

A Review on Sentiment Analysis of Twitter Data

Exploring public sentiment and vaccination uptake of COVID-19 vaccines in England: a spatiotemporal and sociodemographic analysis of Twitter data.

Vaccination is widely regarded as the paramount approach for safeguarding individuals against the repercussions of COVID-19. Nonetheless, concerns surrounding the efficacy and potential adverse effects of these vaccines have become prevalent among the public. To date, there has been a paucity of research investigating public perceptions and the adoption of COVID-19 vaccines. Therefore, the present study endeavours to address this lacuna by undertaking a spatiotemporal analysis of sentiments towards vaccination and its uptake in England at the local authority level, while concurrently examining the sociodemographic attributes at the national level. A sentiment analysis of Twitter data was undertaken to delineate the distribution of positive sentiments and their demographic correlates. Positive sentiments were categorized into clusters to streamline comparison across different age and gender demographics. The relationship between positive sentiment and vaccination uptake was evaluated using Spearman's correlation coefficient. Additionally, a bivariate analysis was carried out to further probe public sentiment towards COVID-19 vaccines and their local adoption rates. The results indicated that the majority of positive tweets were posted by males, although females expressed higher levels of positive sentiment. The age group over 40 dominated the positive tweets and exhibited the highest sentiment polarity. Additionally, vaccination uptake was positively correlated with the number of positive tweets and the age group at the local authority level. Overall, public opinions on COVID-19 vaccines are predominantly positive. The number of individuals receiving vaccinations at the local authority level is positively correlated with the prevalence of positive attitudes towards vaccines, particularly among the population aged over 40. These findings suggest that targeted efforts to increase vaccination uptake among younger populations, particularly males, are necessary to achieve widespread vaccination coverage.

On the use of aspect-based sentiment analysis of Twitter data to explore the experiences of African Americans during COVID-19

According to data from the U.S. Center for Disease Control and Prevention, as of June 2020, a significant number of African Americans had been infected with the coronavirus disease, experiencing disproportionately higher death rates compared to other demographic groups. These disparities highlight the urgent need to examine the experiences, behaviors, and opinions of the African American population in relation to the COVID-19 pandemic. By understanding their unique challenges in navigating matters of health and well-being, we can work towards promoting health equity, eliminating disparities, and addressing persistent barriers to care. Since Twitter data has shown significant promise as a representation of human behavior and for opinion mining, this study leverages Twitter data published in 2020 to characterize the pandemic-related experiences of the United States’ African American population using aspect-based sentiment analysis. Sentiment analysis is a common task in natural language processing that identifies the emotional tone (i.e., positive, negative, or neutral) of a text sample. Aspect-based sentiment analysis increases the granularity of sentiment analysis by also extracting the aspect for which sentiment is expressed. We developed a machine learning pipeline consisting of image and language-based classification models to filter out tweets not related to COVID-19 and those unlikely published by African American Twitter subscribers, leading to an analysis of nearly 4 million tweets. Overall, our results show that the majority of tweets had a negative tone, and that the days with larger numbers of published tweets often coincided with major U.S. events related to the pandemic as suggested by major news headlines (e.g., vaccine rollout). We also show how word usage evolved throughout the year (e.g., outbreak to pandemic and coronavirus to covid). This work also points to important issues like food insecurity and vaccine hesitation, along with exposing semantic relationships between words, such as covid and exhausted. As such, this work furthers understanding of how the nationwide progression of the pandemic may have impacted the narratives of African American Twitter users.

Sentiment analysis of Twitter data regarding the agnipath scheme of the defense forces

Due to the popularity of social media today, people frequently share such criticism on Facebook, Twitter, Instagram, and other platforms. Therefore needs to know how your input from users of social media is generated in order to ascertain the public reaction to the policy that has been enacted. However, because of the comments, it is challenging to tell how many people have responded positive or negative. The objective of sentiment analysis of tweets is to provide insight into people’s attitudes and perceptions regarding an event. This study illustrates the role of Twitter in the announcement of a new army vacancy through the “agnipath scheme” dubbed “agniveer”. The result of this study can be used by the defense forces and government for decision making or policies related to the agnipath scheme. The study studied 4,000 English-language Twitter posts from July 3, 2022 to July 9, 2022. Manual text analysis revealed seven basic groups of tweet sentiments. The tweets’ positive, negative, and neutral emotions were shown using orange data mining software, a powerful machine learning, data mining, and data visualization toolset. Result shows that agnipath scheme is mostly accepted by the people.

Machine Learning Prospects: Insights for Social Media Data Mining and Analytics

Social network has increased surprising consideration in the most recent decade. Social network deals with enormous volume of composite as well as unstructured data and they are very hard to handle. Due to expanding dimensions and demand, one of the encouraging and interesting research field becomes social network. Data Mining affirms to get knowledge by discovery patterns among data. We have discussed social media mining and Social Media analytics. We have insights on the social media effect of our lives, some facts and reports from various sources. We have Integrated this growing research field of social networks with Machine Learning with one simple example of sentiment analysis of Twitter data using Machine Learning. We have also proposed the algorithms to improve the social media analytics results using Machine Learning. In this paper, we will exhibit how machine learning will utilizing for social networking systems like Twitter. In this procedure, a framework is proposed that will collect the tweets messages from the and we will inspect the item’s input to show the positive, negative, or nonpartisan tweets, for this this purpose we have proposed new machine learning algorithms Naive Bayes, maximum entropy to find these outputs. Our proposed Model will help new researchers, companies, Industries, business community, practitioners, new integrated application designers, and the global community to solve the new research problem and may reducing design failure rate of 80% by large through social media mining and networks.

COVID-19 vaccination perception and outcome: society sentiment analysis on twitter data in India.

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.

Resale HDB Price Prediction Considering Covid-19 through Sentiment Analysis

Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon-based tool, VADER, is used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid-19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. In a micro sense, this paper demonstrates the use of sentiment analysis of Twitter data in urban economics. In a macro sense, the paper demonstrates the extent to which social media is able to capture the behavioral economic cues of a population.

Migrants vs. stayers in the pandemic – A sentiment analysis of Twitter content

In this paper, we propose a sentiment analysis of Twitter data focused on the attitudes and sentiments of Polish migrants and stayers during the pandemic. We collected 9 million tweets and retweets between January and August 2021, and analysed them using MultiEmo, the multilingual, multilevel, multi-domain sentiment analysis corpus. We discovered that the sentiment of tweets differs between migrants and stayers over time, and it relates to the country of migration. The general sentiment is similar for migrants and stayers, but a more detailed analysis reveals that hashtags related to staying safe and staying at home, as well as vaccinations are more polarised for migrants than for stayers, and they reflect the general development trend of the pandemic in Europe. In addition to comparing migrants with stayers, we also compared migrants staying in different countries. amongst the countries of migration, for which we collected at least 3000 tweets, the most positive sentiment of Polish migrants’ tweets was observed in Belgium, with the most negative sentiment coming from Estonia. We also observed that the sentiment of tweets written in Polish by stayers in Poland is less negative when compared to Polish migrants in most of the countries with the highest number of tweets.

Analisa Sentimen Pengguna Transportasi Jakarta Terhadap Transjakarta Menggunakan Metode Naives Bayes dan K-Nearest Neighbor

Social media used in communicating that is very popular in Indonesia. One of the most popular is Twitter. Twitter is a social media site where people can share information publicly. This information can be processed to make sentiment analysis. This research attempts to create a system that can detect positive or negative sentiments in public information. The method used for this sentiment classification is the comparison method of Naive Bayes Classifier and K-Nearest Neighbor Classifier using TF-IDF weighting. The input to this system is in the form of tweet data for Transjakarta, while the output of this system is in the form of visualization of positive and negative sentiment data using Streamlit which is a library from python. Based on testing the accuracy of the Naive Bayes approach for sentiment analysis of Twitter data related to the use of Transjakarta transportation is 61.1%, and the accuracy of the K-Nearest Neighbor method is 75.7%. For the two methods used in determining the level of accuracy, it can be concluded that the K-nearest-neighbor method produces better accuracy.

Examining logistics developments in post-pandemic Japan through sentiment analysis of Twitter data

The objective of this study is to utilize natural language processing technologies to examine data gathered from Twitter related to logistics in Japan during the COVID-19 pandemic. The Bidirectional Encoder Representations from Transformers (BERT) machine learning model is utilized to assess the sentiment of the content. The findings suggest a positive outlook on logistics during time frame analyzed. This research has four key implications: (1) the sentiment towards the term "logistics" is generally positive as per our analysis; (2) there is a trend of increasing interest in logistics in western Japan in 2022; (3) social media can be utilized as a tool to address the challenges faced by the logistics industry; and (4) our research highlights the potential of using social media data to provide a more timely and comprehensive analysis of logistics and transportation trends.

Dynamics of emotion trends in Canadian Twitter users during COVID-19 confinement in relation to caseloads: Artificial intelligence-based emotion detection approach.

Multiple waves of COVID-19 have significantly impacted the emotional well-being of all, but many were subject to additional risks associated with forced regulations. The objective of this research was to assess the immediate emotional impact, expressed by Canadian Twitter users, and to estimate the linear relationship, with the vicissitudes of COVID caseloads, using ARIMA time-series regression. We developed two Artificial Intelligence-based algorithms to extract tweets using 18 semantic terms related to social confinement and locked down and then geocoded them to tag Canadian provinces. Tweets (n = 64,732) were classified as positive, negative, and neutral sentiments using a word-based Emotion Lexicon. Our results indicated: that Tweeters were expressing a higher daily percentage of negative sentiments representing, negative anticipation (30.1%), fear (28.1%), and anger (25.3%), than positive sentiments comprising positive anticipation (43.7%), trust (41.4%), and joy (14.9%), and neutral sentiments with mostly no emotions, when hash-tagged social confinement and locked down. In most provinces, negative sentiments took on average two to three days after caseloads increase to emerge, whereas positive sentiments took a slightly longer period of six to seven days to submerge. As daily caseloads increase, negative sentiment percentage increases in Manitoba (by 68% for 100 caseloads increase) and Atlantic Canada (by 89% with 100 caseloads increase) in wave 1(with 30% variations explained), while other provinces showed resilience. The opposite was noted in the positive sentiments. The daily percentage of emotional expression variations explained by daily caseloads in wave one were 30% for negative, 42% for neutral, and 2.1% for positive indicating that the emotional impact is multifactorial. These provincial-level impact differences with varying latency periods should be considered when planning geographically targeted, time-sensitive, confinement-related psychological health promotion efforts. Artificial Intelligence-based Geo-coded sentiment analysis of Twitter data opens possibilities for targeted rapid emotion sentiment detection opportunities.

A Review on Twitter Sentiment Analysis Using ML

Abstract: Social Media sites like twitter have billions of people share their opinions day by day as tweets. As tweet is characteristic short and basic way of human emotions. So, in this paper we focused on sentiment analysis of Twitter data. Most of Twitter's existing sentiment analysis solutions basically consider only the textual information of Twitter messages and strives to work well in the face of short and ambiguous Twitter messages. Recent studies show that patterns of spreading feelings on Twitter have close relationships with the polarities of Twitter messages. In this paper focus on how to combine the textual information of Twitter messages and sentiment dissemination models to get a better performance of sentiment analysis in Twitter data. To this end, proposed system first analyses the diffusion of feelings by studying a phenomenon called inversion of feelings and find some interesting properties of the reversal of feelings. Therefore, we consider the interrelations between the textual information of Twitter messages and the patterns of diffusion of feelings, and propose random forest machine learning to predict the polarities of the feelings expressed in Twitter messages. As far as we know, this work is the first to use sentiment dissemination models to improve Twitter's sentiment analysis. Numerous experiments in the real-world dataset show that, compared to state-of-the-art text-based analysis algorithms.