Tweet Data Research Articles

MFB: A Generalized Multimodal Fusion Approach for Bitcoin Price Prediction Using Time-Lagged Sentiment and Indicator Features

Bitcoin’s volatile nature has made its price prediction a sought-after mathematical model in the FinTech industry. Existing studies, however, need to look into the critical aspect of time-lagged sentiment in Bitcoin price forecasting. This omission is significant because time-lagged sentiment captures delayed market reactions that are not immediately apparent in price movements. Moreover, the correlation between time-lagged sentiment and technical indicators and the limitations of individual machine learning and deep learning models necessitates a comprehensive approach for accurate and reliable Bitcoin price predictions. This paper introduces the multimodal fusion Bitcoin (MFB), an innovative generalized multimodal fusion approach that effectively integrates BiLSTM and BiGRU layers for complex feature extraction. The model employs the BorutaShap algorithm for feature selection and utilizes attention mechanisms and spatial dropout for optimization and generalization. MFB’s training and validation use news and tweet data combined with Bitcoin technical indicators to explore the impact of time-lagged sentiment on price movements, leading to more accurate and timely market predictions. The MFB performs superior Bitcoin prediction performance, achieving 97.63% accuracy and an MAE of 0.0065. Experiments highlight MFB’s capability to outperform existing models, offering significant insights for investors in making informed decisions. MFB’s innovative methodology, particularly in next-hour Bitcoin price forecasting, marks an advancement in financial forecasting. By capturing the nuanced dynamics of market sentiment and its delayed effects, MFB is a pioneering multimodal fusion approach in the FinTech domain, revolutionizing Bitcoin price prediction.

Application of the Naïve Bayes Classifier Algorithm to Analyze Sentiment for the Covid-19 Vaccine on Twitter in Jakarta

The epidemic of a new disease caused by the coronavirus (2019-nCoV), commonly referred to asCOVID-19, has been declared a global virus epidemic by the World Health Organization (WHO).President Joko Widodo has officially ratified Presidential Decree No. 99 of 2020 concerning theprovision of vaccines and the implementation of vaccination activities. Twitter is a social mediaplatform that allows users to share information and opinions directly with fellow users. Tweets givencan be in any form, either positively or negatively, so one of the methods used is sentiment analysis.Sentiment analysis helps determine an opinion or comment on an issue, whether the response ispositive or negative. The Naïve Bayes algorithm is used in sentiment analysis because it is suitablefor tweets or text data that is not too long or short text. The initial stage of sentiment analysis is textpre-processing which consists of Cleaning, case folding, tokenizing, and stopword removal. Then thedata is labeled manually. The analysis results are visualized as bar charts, pie charts, and word clouds.Then the word weighting is carried out using the term frequency-inverse document (TF-IDF), andclassification is carried out using the Naïve Bayes classifier. From the test results, the accuracy valueof the confusion matrix is 82% from 2600 tweet data with 80% training data composition and 20%test data.

Optimizing the Learning Rate Hyperparameter for Hybrid BiLSTM-FFNN Model in a Tourism Recommendation System

Indonesia, with its abundant natural resources, is rich in captivating tourist attractions. Tourism, a vital economic sector, can be significantly influenced by digitalization through social media. However, the overwhelming amount of information available can confuse tourists when selecting suitable destinations. This research aims to develop a tourism recommendation system employing content-based filtering (CBF) and hybrid Bidirectional Long Short-Term Memory Feed-Forward Neural Network (BiLSTM-FFNN) model to assist tourists in making informed choices. The dataset comprises 9,504 rating matrices obtained from tweet data and reputable web sources. In various experiments, the hybrid BiLSTM-FFNN model demonstrated superior performance, achieving an accuracy of 93.36% following optimization with the Stochastic Gradient Descent (SGD) algorithm at a learning rate of about 0.193. The accuracy, after applying Synthetic Minority Over-sampling Technique (SMOTE) and fine-tuning the learning rate hyperparameter, showed a 14.3% improvement over the baseline model. This research contributes by developing a recommendation system method that integrates CBF and hybrid deep learning with high accuracy and provides a detailed analysis of optimization techniques and hyperparameter tuning.

Sentiment Analysis on Twitter Social Media Regarding Covid-19 Vaccination with Naive Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT)

Abstract. The Covid-19 vaccine is an important tool to stop the Covid-19 pandemic, however, there are pros and cons from the public regarding this Covid-19 vaccine. Purpose: These responses were conveyed by the public in many ways, one of which is through social media such as Twitter. Responses given by the public regarding the Covid-19 vaccination can be analyzed and categorized into responses with positive, neutral or negative sentiments. Methods: In this study, sentiment analysis was carried out regarding Covid-19 vaccination originating from Twitter using the Naïve Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT) algorithms. The data used in this study is public tweet data regarding the Covid-19 vaccination with a total of 29,447 tweet data in English. Result: Sentiment analysis begins with data preprocessing on the dataset used for data normalization and data cleaning before classification. Then word vectorization was performed with TF-IDF and data classification was performed using the Naïve Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT) algorithms. From the classification results, an accuracy value of 73% was obtained for the Naïve Bayes Classifier (NBC) algorithm and 83% for the Bidirectional Encoder Representations from Transformers (BERT) algorithm. Novelty: A direct comparison between classical models such as NBC and modern deep learning models such as BERT offers new insights into the advantages and disadvantages of both approaches in processing Twitter data. Additionally, this study proposes temporal sentiment analysis, which allows evaluating changes in public sentiment regarding vaccination over time. Another innovation is the implementation of a hybrid approach to data cleansing that combines traditional methods with the natural language processing capabilities of BERT, which more effectively addresses typical Twitter data issues such as slang and spelling errors. Finally, this research also expands sentiment classification to be multi-label, identifying more specific sentiment categories such as trust, fear, or doubt, which provides a deeper understanding of public opinion.

Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Terhadap Program Makan Siang Gratis

The work program promised by the 2024 Presidential Candidate and Vice Presidential Candidate pair, namely, Prabowo Subianto and Gibran Rakabuming Raka, one of which is a free lunch program, this program is an effort to improve community welfare, but this has attracted public attention on social media, one the other is platform X. The public response to the free lunch program is the main focus. In this research, researchers analyzed sentiment related to the free lunch program using the Naive Bayes method using the Python programming language on Google Colab to analyze sentiment towards social media users. X. the results obtained from 920 tweets data, there are 167positive value tweets, 744tweets are negative. The evaluation results with the confusion matrix showed an accuracy of 86.95%, with precision 93%, recall 61%, and F1-Score 65%.From the results of this research it can be concluded that the majority of the public who commented on social media X gave a negative response to the free lunch program. These results can be used as material for government evaluation in determining effective and efficient policies for society.

The Analysis of Tourism Attitudes using Natural Language Processing Techniques: A Case of Malaysian Tourists

The spread of COVID-19 has had a significant impact on all facets of the global tourism sector, particularly in Thailand, one of the world’s leading travel destinations. At the height of the epidemic, many countries imposed a nationwide lockdown, prohibiting all citizens from leaving the country and all foreign tourists from entering. This led to a global shutdown that significantly affected the daily lives of billions of people and seriously impacted the travel and tourism industry. After a two-year hiatus due to the epidemic, the situation eased and the lockdown restrictions were lifted. An interesting question is how visitors’ attitudes and preferences changed when compared to the time before the outbreak. This study attempts to answer this question by focusing on Malaysian visitors’ attitudes and perceptions toward destinations in southern Thailand. The study examines the perceptions of Malaysian Twitter (now X) users from three areas in Malaysia; Kedah, Perlis, and Kuala Lumpur, by employing Natural Language Processing (NLP) techniques such as sentiment analysis and topic modeling. Then, tweet data before and after the lockdowns were gathered, analyzed, and compared. For sentiment analysis, it was found that, when neutral tweets were disregarded, results both before and after the COVID-19 lockdowns revealed that the attitudes conveyed by Malaysian tourists were overall positive especially a territory and a state that are more far away from Thailand, i.e., Kuala Lumpur and Kedah. The results from the topic modeling analysis showed a meaningful distinction between before and after the COVID-19 lockdowns. Practical suggestions are offered for tourism policymakers to identify and address both the strengths and weaknesses of tourism development in Southern Thailand.

Virality classification from Twitter data using pre-trained language model and multi-layer perceptron

Twitter is one of the well-known text-based social media that is often used to disseminate content. According to Katadata, Indonesia ranked fifth in the world in 2023. So many people or organizations want to make tweets go viral. Therefore, this research aims to develop a model that uses tweet data from the Indonesian language Twitter social media to categorize the level of virality. There are several tasks in classifying the level of virality, such as upsampling data, predicting sentiment and emotion, and text embedding. Upsampling data was carried out because the dataset used was an imbalanced dataset. Data upsampling, emotions, and text embedding is carried out using the bidirectional encoder representation from transformers (BERT) model. Meanwhile, sentiment prediction uses the Ro-bustly optimized BERT pretraining approach (RoBERTa). The results of text embedding, sentiment, emotion, will be combined with Twitter metadata then all features will be fed into the multi-layer perceptron (MLP) model to classifying the level of virality which is divided into 3 classes based on the number of retweets, namely low, medium and high. The proposed method produces an F1-score of 49% and an accuracy of 95% and performs better than the baseline model.

Implementasi Model Support Vector Machine Dalam Analisa Sentimen Masyarakat Mengenai Kebijakan Penerapan Aplikasi Mypertamina

The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.

Sentiment Analysis of Starlink on Twitter Using Support Vector Machine Algorithm

Indonesia faces unique challenges in the provision of internet services. Cable and fiber optic infrastructure is often difficult and expensive to implement in many areas. Based on data from the Asosiasi Penyelenggara Jasa Internet Indonesia (APJII), by 2024 internet users will reach 221.5 million. Starlink, Elon Musk's satellite-based internet service through his SpaceX company, offers an innovative solution to fill the void in Indonesia's telecommunications infrastructure. However, Starlink's presence has raised concerns among local service providers, particularly regarding potential market disruption and existing regulations. Starlink could also pose a potential threat to Indonesia's security and sovereignty. Starlink has been a hot topic on various social media platforms, including Twitter. Twitter is a very popular social media platform with millions of active users who often share their opinions in real-time. The number of public responses in assessing the presence of Starlink in Indonesia, became a reference for a sentiment analysis. The Support Vector Machine algorithm is used to classify opinions into positive and negative categories. Based on testing that has been done using the Cross Validation technique with a K-Fold value with a total of 1976 tweets data. The results show that 1112 tweets contain positive sentiment and 864 tweets contain negative sentiment. This shows that 56.3% of people agree with the presence of starlink in Indonesia. While from the use of the Support Vertor Machine algorithm, the Accruracy value is 76.22%, Precision is 77.48%, and Recall is 81.38%.

Analyzing Public Sentiment Towards BSI Service Disruptions Through X: Naïve Bayes Algorithm

Disruptions to banking services can negatively affect customer trust and happiness, thus affecting the bank's reputation in the eyes of the public. Analysis of sentiment expressed on social media is very important because it can provide a direct picture of individual perceptions and responses in real time. This research aims to analyze public sentiment towards disruptions in Bank Syariah Indonesia (BSI) services through social media using the Naive Bayes algorithm. Through this analysis, the research seeks to understand the pattern of public responses and perceptions of BSI disruptions and evaluate the performance of the Naive Bayes algorithm in classifying sentiment on related tweet data. The data used came from specific social media platforms, where sentiment analysis was conducted by categorizing the data into positive, negative, and neutral categories. The research findings show that the sentiment analysis of the community towards BSI service disruptions through X social media platforms shows a diverse pattern of responses and perceptions. This finding recorded 525 data points with negative sentiment, 325 data points with neutral sentiment, and 141 data points with positive sentiment. The research also compared the performance of the Naive Bayes algorithm with the Google Cloud Natural Language API, which showed an accuracy rate of 81.03%. This research provides valuable insights for Bank Syariah Indonesia in understanding public perception of BSI services on social media.

Analysis of Sentiment Based on Opinions from the 2019 Presidential Election

Twitter is a popular social media platform where the public is free to comment and write about anything. It is common for people to post comments containing harsh words and even hate speech. The 2019 presidential election in Indonesia generated a significant amount of comments, with some users praising the candidates, others criticizing them, and some even resorting to insults. To extract meaningful information from these comments and classify the text, sentiment analysis is essential. In this research, sentiment analysis involves the process of categorizing textual documents into two classes: negative and positive sentiment. The opinion data was collected from the Twitter social network in the form of tweets related to the 2019 presidential election. The dataset used in the study consisted of 3,337 tweets, which were divided into 70% training data and 30% test data. The training data comprised tweets whose sentiment was already known, serving as a foundation for the model to learn and make predictions. The primary objective of this research is to determine whether the tweets, written in Indonesian, express positive or negative sentiments. The Naive Bayes Classifier algorithm was employed to classify the tweet data. This algorithm is well-suited for text classification tasks due to its simplicity and efficiency in handling large datasets. The classification results on the test data demonstrated that the Naive Bayes Classifier algorithm achieved an overall accuracy of 71%. Specifically, the accuracy for negativesentiment classification was 71%, while the accuracy for positive sentiment classification was 70%. These results indicate that the Naive Bayes Classifier is effective in distinguishing between positive and negative sentiments in tweets related to the presidential election

Naive Bayes Algorithm with Feature Selection Using Particle Swarm Optimization

The COVID-19 vaccine in Indonesia has led to the emergence of public opinion which is conveyed on social media such as Twitter. One of the analyses that can be done to produce various information from public opinion is sentiment analysis. Sentiment analysis is used to determine whether an opinion tends to be positive or negative. This study aims to classify the public opinion of the COVID-19 vaccine in Indonesia with sentiment analysis and to visualize the location of the sentiment of the COVID-19 vaccine tweet data in Indonesia. To achieve this aim, the Naïve Bayes algorithm with Particle Swarm Optimization (PSO) feature selection was used. This study uses opinions into positive and negative class sentiments towards 2,547 tweets related to the COVID-19 vaccine in Indonesia from January to June 2021. The results show that the distribution of positive and negative class sentiments is 2,328 and 219, respectively. In addition, the positive sentiment for the COVID-19 vaccine was dominated by people on the island of Java based on a random number matrix initialized by the PSO method. The classification of public opinion on Twitter media provides accurate and optimal performance results using a combination of the Naïve Bayes algorithm with PSO feature selection. The results of the combination of these methods have accuracy and F1 score values of 91.28% and 95.38%, respectively. The visualization of geo-spatial mapping showed that positive sentiments related to the COVID-19 vaccine exist in almost all regions in Indonesia but are dominated by the Jabodetabek area.

Twitter As A Mirror: Examining Public Sentiments Towards Frugal Living From An Islamic Ethical Perspective

Nowadays, human needs are increasingly complex. This condition is one of the reasons why people are required to be wise in spending their assets. Frugality is a lifestyle that can be a solution to ensure future economic benefits. This frugal lifestyle is known by urban communities as frugal living. This lifestyle has become a trending topic on social media twitter because it reaps the pros and cons of one's social life. Against this phenomenon, researchers will analyze the sentiment of Indonesian society towards frugal living and study it using the perspective of Islamic law. This research is a qualitative research with a descriptive approach using statistical data of twitter users who write tweets related to frugal living in Indonesia. Twitter users' tweets can be in the form of sentences, photos and videos. In this study, the data used by researchers is only tweets in the form of sentences. This study uses sentiment analysis to determine the sentiment of Twitter users related to frugal living. The tweet data related to frugal living was obtained as many as 4753 with restrictions on Indonesian language from June 1, 2023 to October 23, 2023. Based on the research results, it was found that the most dominant sentiment was neutral with a percentage of 56% or a total of 2655 tweets. The positive sentiment is in second place at 27% or 1296 tweets and the negative sentiment is 17% or 802 tweets. Although the positive sentiment is not dominant, frugal living has a good impact on the life of a Muslim. This lifestyle is in line with religious advice to live simply and moderately in order to prosper and be free from extravagance

A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis

Social media sentiment classification was an essential consideration in natural language processing (NLP) for evaluating normal people’s perspectives on a given topic. With Twitter’s massive rise in popularity in recent years, the capacity to extract information about public sentiment from tweets became a major focus. This paper not only analyzed public sentiment through data from Twitter but introduced a novel ensemble approach in the methods employed for Twitter sentiment classification. Real-time tweets on various topics, including “covid,” “crime,” “spam,” “flipkart,” “migraine,” and “airlines,” were extracted and thoroughly examined to gain insight into public opinions. Leveraging the Twitter API for real-time tweet extraction, natural language processing techniques were applied to clean the tweet data. Subsequently, we applied several machine learning (ML) algorithms Naïve Bayes, decision tree (DT), random forest (RF), logistic regression (LGR), and deep learning (DL) algorithms recurrent neural network (RNN), LSTM, and GRU individually. Later, we proposed a novel ensemble of ML and DL algorithms for sentiment classification, with a novel emphasis on ensemble techniques and enhanced the accuracy with a significance compared to individual ML or DL model applied. The experimental results demonstrated that our novel ensemble approach achieved high accuracy when compared to existing work.

Implementation of Text Processing Techniques on Citizen Opinions Regarding Floods in Surabaya

An analysis of residents opinions on flooding in Surabaya is essential to identify their perceptions and aspirations towards this disaster. This can facilitate the making of appropriate flood mitigation policies. The current problem is that it is difficult to retrieve data from X in the form of both users and tweets automatically. So that in some studies that use tweet data becomes less efficient in the data collection process. This research confirmed the use of messages to determine highly impactful disaster zones and showed how tweets can be used to identify oscillations in disaster intensity over time. The topic of this research is to apply the Data Crawling method to obtain datasets on social media X. Then the next method is text preprocessing using Wordcloud, Matplotlib, (NTLK) Natural Language Toolkit, and Sastrawi libraries. In natural language processing, the data to be extracted includes unstructured or “arbitrary” data. In normal dialect preparing (NLP), the information to be extracted includes unstructured or "self-assertive" information. For future purposes (assumption examination, subject modeling, etc.), such information must be changed over into organized data. The discoveries of the think about can help the organization in comprehending the necessities and inclinations of the people with respect to surges. This article demonstrates how Artificial Intelegence may be applied to text data analysis in order to provide insightful findings. the outcomes of this research can help the government in making more effective policies to overcome flooding in Surabaya

Twitter Sentiment Analysis Using Machine Learning

In an age of social media, online forums, and chats, cyberbullying is a prevalent issue. On Twitter (now X), approximately 500 million tweets are shared per day (Antonakaki et.al., 2021). It is the job of the moderators to ensure these tweets follow standard community guidelines. However, the sheer number of tweets makes it difficult to sort manually and ensure they are following protocol. Sentiment analysis and machine learning algorithms can be used to classify these texts automatically as positive or negative. Normally, these machine learning models are much more efficient and may provide higher accuracy rates in identifying hate speech in Twitter. In this paper, we are exploring the use of five classical machine learning algorithms to classify Twitter hate speech as neutral, racist, or sexist. Model performance was compared after using raw tweet data versus pre-processed tweets through data cleanup. Furthermore, we highlight two methods to deal with imbalanced datasets to improve the prediction rates. Overall, we were able to achieve a 96% accuracy in correctly classifying tweets into the different labels.

Nonrandom Tweet Mortality and Data Access Restrictions: Compromising the Replication of Sensitive Twitter Studies

Abstract Used by politicians, journalists, and citizens, Twitter has been the most important social media platform to investigate political phenomena such as hate speech, polarization, or terrorism for over a decade. A high proportion of Twitter studies of emotionally charged or controversial content limit their ability to replicate findings due to incomplete Twitter-related replication data and the inability to recrawl their datasets entirely. This paper shows that these Twitter studies and their findings are considerably affected by nonrandom tweet mortality and data access restrictions imposed by the platform. While sensitive datasets suffer a notably higher removal rate than nonsensitive datasets, attempting to replicate key findings of Kim’s (2023, Political Science Research and Methods 11, 673–695) influential study on the content of violent tweets leads to significantly different results. The results highlight that access to complete replication data is particularly important in light of dynamically changing social media research conditions. Thus, the study raises concerns and potential solutions about the broader implications of nonrandom tweet mortality for future social media research on Twitter and similar platforms.

Twitter Trending Topics Analysis Using Power BI on Real-Time Basis

In today’s world of growing social media usage twitter has become an enormous source of data from public in form of tweets. These tweets can be collect and useful to extract effective meaningful insights ex, public sentiments, choices and opinions about a particular event, product or person, which can prove helpful for business growth, political parties and celebrities to know public choices, sentiments and their reviews over a particular product , person or government decision. But before performing sentiment analysis on tweets data we need to clean and per-process it as tweets data is highly unstructured and noisy for that many methods are there:-data per-processing, stop-words removal, stemming, lemmatization etc. After data cleaning effective meaningful insights can be extracted and sentiment analysis can be performed to extract public opinions and their choices. Twitter data possesses significant power in capturing timely public opinions on a variety of topics, such as preferences for products, political tendencies, and sentiments within the business realm. The extensive user base of this platform provides a wide array of viewpoints, rendering it a valuable resource for comprehending consumer behaviour, political developments, and market outlooks. By employing sentiment analysis, organizations can measure levels of customer contentment, policymakers can evaluate public reception, and corporations can monitor how their brands are perceived to make well-informed decisions. The real-time aspect and high level of user involvement associated with Twitter data render it priceless for shaping strategies related to product innovation, political campaigns, and business expansion efforts. This research paper highlights the power of power BI tool in data visualization of real-time tweets of a user twitter account and builds effective dashboards and also perform sentiment analysis of real-time tweets. Keywords: LDA, Machine Learning, Topic Modelling, Sentiment Analysis, NLP, Social Media.

Twitter Visualitation of Healty Catering Food Promotion Strategy

This analysis aims to determine whether there are differences in marketing strategies and which strategy is superior between the two health catering industries. Intermediary of Twitter as a place to retrieve data, where Twitter is a social media with the fastest spread of news through hashtags and trending topics of a phenomenon that is busy being discussed. They use qualitative methods by utilizing Social Network Analysis (SNA) and are assisted by several supporting tools such as Gephi, Wordji, Notepad++, and Dictionaries to make it easier for researchers to analyze the data obtained so that it is concrete and relevant. From the visualization of Twitter user tweet data, the researcher found that Twitter users discussed Yellowfit Kitchen health catering more than Gorygourmet, as evidenced by a large number of data visualizations obtained. Then on average, customers choose to subscribe to health catering Yellowfit Kitchen with the pure aim of dieting. At the same time, healthy catering Gorygourmet customers subscribe to a more nutritious diet to avoid disease.

Deepfake on Social Media: Harnessing Deep Learning for Identifying Falsified Tweet

Recent advancements in natural language production have enabled the creation of sophisticated deepfake social media messages, posing a significant threat to public discourse. In response, this study focuses on the development of reliable detection methods for identifying automated text on websites such as Twitter. Leveraging Tweepfake, a publicly accessible dataset, a simple deep learning model employing tf-tf-idf, word2vec and tokenizer from keras library word embeddings and a typical architecture for a convolutional neural network (CNN) is proposed. Comparative analysis against baseline methods, including various feature-based approaches and various forms of deep learning, such Long Short-Term Memory (LSTM), demonstrates the suggested method's greater performance. Experimental results showcase an impressive accuracy of 91% in accurately classifying tweet data either bot-generated or human-generated. This research contributes to the ongoing efforts to combat the proliferation of deepfake content on social media platforms. Keywords: Deep Learning, Machine Learning, Deepfake Detection, Text Classification