Form Of Tweets Research Articles

Gemoy and Samsul: How The Propaganda Won The 2024 Indonesian Election

This study aims to determine the forms of political buzzer propaganda ahead of the 2024 election on Twitter/X. The research method used is a qualitative approach using content analysis and the type of data used is secondary data in the form of tweets or uploads that themed the 2024 presidential election. The data was inputted manually using Microsoft Excel and then analyzed systematically using the SPSS 25 program. The chi square test result is .001, which means that there are significant differences between candidates in the forms of propaganda. Tweets discussing Prabowo-Gibran were the most propaganda-laden which included name calling, transfer, testimonial, card stacking, and bandwagon, with the exception of plain folks, which is propaganda that shows the efforts of the presidential and vice presidential candidates' closeness to the public, in this category, candidate pair number three, namely Ganjar-Mahfud, is significantly higher than other candidate pairs, and gliterring generalities, namely propaganda with positive connotations on the presidential and vice presidential candidate pairs being discussed, in this case candidate pair number one Anies Imin is significantly higher than other pairs. Name-calling was the most significantly utilized in tweets discussing Prabowo-Gibran, compared to other form of propaganda in tweets discussing the other two candidate pairs. This highlighted the importance of spinning the unfavorable attributes to become a favorable one.

The weather today rocks or sucks for my tree: exploring the understanding of climate impacts on forests at high school level through tweets

Abstract. With the progression of global warming, impacts on the human sphere will undoubtedly increase. One prominent example at mid-latitudes is the stress on forests caused by climate change, which the BayTreeNet project (https://baytreenet.de/, last access: 30 July 2024) addresses from an interdisciplinary viewpoint. Scientists from physical climatology, dendroecology, and educational research collaborate to examine how long-term changes in weather patterns affect the state of trees, and how the atmosphere–tree relation can be used to the advantage of improving the communication of climate change effects to, in particular, high school students. This article presents a 1-week case study for the summer of 2021, when a distinct variability in weather patterns induced significant tree responses. The students of seven selected partner schools commented on the measured tree and weather data, which were available in real time, in the form of tweets and in conjunction with their educational geography program. The analyses of the tweets reveal that the students succeed in verbalizing the measured weather and, furthermore, manage to draw links between the stem diameter changes in trees and the weather variability. Problems arise with the use of less perceivable variables like the sap flow in the trees; also, the student posts exhibit shortcomings in establishing causal connections. Hence, the case study points to a discrepancy between describing basic environmental information and appreciating or understanding the underlying mechanistic links. This point will serve to refine future classroom concepts and, moreover, to enhance the communication of climate change effects on forests to the general public.

Sentiment Analysis of Handling "Klitih" in Yogyakarta Using Naïve Bayes

Activities that lead to crimes called "klitih" often occur and disturb the community. The community's response to the handling carried out by the regional government also varied. The public expressed this response using various types of social media, one of which was Twitter. This research analyzes sentiment or responses given by the public by utilizing the social media Twitter to collect data. Data in the form of tweets that have been taken will go through text processing. After that, the text will be weighted using two methods as a comparison, namely tf-idf and count vector. Then the data will be divided into training data and test data to proceed to the classification stage. Classification is carried out using the Naïve Bayes algorithm. To evaluate the results of Naïve Bayes classification, researchers used the Confusion Matrix, by comparing weighting methods and dividing the training data and test data into several different ratios, to find out the scenario that produces the best level of accuracy. The sentiment obtained was dominated by negative sentiment at 75.8%, while positive sentiment was 24.2%. By using existing data, it was found that weighting with count vector had an accuracy rate of 82%. Meanwhile, weighting using TF-IDF obtained an accuracy of 80%.

Tindak Tutur Ilokusi Dalam Cuitan Twitter Cawapres Gibran Rakabuming Raka Dalam Masa Kampanye Kontestasi Pilpres 2024

This article discusses the use of language in communication, specifically focusing on the concept of pragmatics and expanding it to understanding the meaning behind words. This highlights the importance of considering the context in which language is used, as well as the intention of the speaker, to fully understand the message conveyed. This article aims to describe illocutionary speech acts in tweets on the Twitter account of vice presidential candidate Gibran Rakabuming Raka during the 2024 presidential election contestation campaign. This research is a type of qualitative descriptive research, with data in the form of tweets. This research shows the existence of illocutionary speech acts in several tweets on the Twitter account @gibran_tweet. The data collection technique in this research uses the note-taking technique. The research results found that there were 26 illocutionary speech acts with details of 7 assertive speech acts, 5 directive speech acts, 4 expressive speech acts, 6 commissive speech acts, and 4 expressive speech acts. The benefits of this research can be used as a reference for further relevant research.

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.

Real-Time Twitter Trends Analysis Using Latent Dirichlet Allocation and Machine Learning

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.

Real-Time Twitter Trends Analysis Using Latent Dirichlet Allocation and Machine Learning

As far as social media is concerned twitter has become a major source of public data in the form of tweets. Twitter is an emerging source of large textual data(big data).People can easily express their opinions , reviews, interests and tastes about a particular event, topic ,product etc., occurring worldwide. This makes twitter a good source of valuable data which can be further used to perform sentiment analysis, know trending topics of public intrests and public opinion on a particular product or event which can be beneficial for business growth and political parties to know public choices and they can take actions accordingly. The extraction of meaningful insights from the vast and dynamic stream of social media data is greatly facilitated by the implementation of topic modeling and sentiment analysis on Twitter tweets. The identification of recurring themes or subjects within a collection of tweets, which is the essence of topic modeling, serves to reveal patterns in discussions, thereby aiding in the comprehension of prevalent topics and trends. Concurrently, sentiment analysis, through the evaluation of the emotional tone of tweets, enables the discernment of whether the expressed sentiments are positive, negative, or neutral. The combination of these techniques provides researchers and businesses with valuable perspectives on public opinions, emerging issues, and user sentiments, thereby empowering them to make informed decisions and develop effective engagement strategies in the ever-evolving landscape of social media. But twitter data(tweets) are unstructured ,contains noisy data, urls , stop words, re-tweets video ,emoji etc., which need to be cleaned and preprocessed to perform proper sentiment analysis and use it to extract meaningful effective insights from it. This paper focuses on various methods of topic modeling and discovering latent topics ,text-mining approaches ,micro-blogging methods used in various researches .This paper focuses on latent dirichlet allocation method of topic modeling and text-mining to discover latent topics in tweets, micro-blogging ,text-mining approaches .A proper survey is done on previous researches on this topic in this article. Keywords:-LDA, text-mining, topic modeling, micro-blogging ,machine learning ,sentiment analysis, NLP.

Analisa Sentimen Tweet Berbahasa Indonesia Dengan Menggunakan Metode Lexicon Pada Topik Perpindahan Ibu Kota Indonesia

This study proposes a classification of public response to the government's decision to move the Indonesian capital using the lexicon method. The results of testing accuracy are measured using a confusion matrix. The data in this study use data from Twitter in the form of tweets. The data contains tweets of community responses to the decision to move the Indonesian capital. Data passes through 5 preprocessing processes, namely case folding, punctuation removal, stopword removal, stemming, and tokenizing. Lexicon is used because it produces good accuracy values. In this study also will look for a dictionary that has the best classification results. The results of this study show the results of a good classification by approaching the results by experts.

Perbandingan Analisis Sentimen Aplikasi Traveloka dan Tiket.com pada Twitter dengan Metode Support Vector Machine

The emergence of the COVID-19 pandemic in Indonesia resulted in an economic crisis, including in the world of tourism, which caused a decline in the national economy. With the existence of Online Travel Agencies (OTA) such as Traveloka and Tiket.com, it is hoped that they can help improve the tourism sector for the Indonesian economy. As a popular OTA and to see the opinion of the Indonesian people, it can be seen from public opinion in the form of tweets on the Twitter application. The tweets data will be taken and sentiment analysis will be carried out on the OTA Traveloka and Tiket.com applications which will be classified into certain classes based on opinions and modeling will be carried out using the Support Vector Machine (SVM) algorithm method. This research aims to determine the level of accuracy of the SVM algorithm and find out how sentiment analysis compares between Traveloka and Tiket.com. In the sentiment analysis comparison, in terms of price, Traveloka is superior and in terms of service, Tiket.com is superior. After modeling by comparing splitting data and handling imbalanced data using Synthetic Minority Oversampling Technique (SMOTE), the best SVM accuracy results for the Tiket.com price dataset were 68%, for Traveloka prices it was 97%, for Tiket.com services it was 92%, and for Traveloka services it is 89%.

ANALISIS SENTIMEN DI MEDIA SOSIAL TWITTER DENGAN STUDI KASUS KARTU PRAKERJA

Kartu Prakerja is one of the government's flagship programs in providing training to the workforce. In its implementation there is a lot of information scattered, especially on social media Twitter both in the pros and cons of Kartu Prakerja program. Based on information in the form of tweets that have not been analyzed in depth, it is necessary to analyze sentiment on the Kartu Prakerja in order to obtain appropriate information based on the opinions of netizen s on Twitter. This study discusses sentiment analysis of tweet data with the keyword “Kartu Prakerja” which uses data as many as 6658 tweet data taken in the period May 27 - August 5, 2021. This research uses the Naive Bayes Classification method which has several stages, namely data retrieval, data preprocessing, manual labeling, data training and testing. The solution offered in this study is to create an analysis model that can be used to perform sentiment analysis about Kartu Prakerja on Twitter. Based on the results of this study obtained that the calculation of accuracy obtained a value of 86% for training data and 87% for data testing. This study concluded that the Kartu Prakerja has a positive sentiment by Twitter netizens based on the results of Classification that discusses many positive sentiments such as the benefits, effectiveness and addition of the Kartu Prakerja budget.

Brand Positioning untuk Keunggulan Bersaing pada Aplikasi Transportasi Online Menggunakan Sentiment Analysis (Studi pada: Go-Jek dan Grab)

The development of technology and information in the industrial revolution 4.0 an impact on the increasing number of internet users. Social media is a place to express opinions, share and receive information in a short time to produce User Generated Content (UGC). Not only individuals, but companies also use social media to interact with their customers to understand the company's position in the eyes of customers. Through brand positioning on social media, companies can compare with competing companies to measure their competitive advantage on social media. This study aims to determine the brand positioning and competitive advantage of the top brands of online transportation applications in Indonesia, namely Go-Jek and Grab via Twitter. The method used in this research is a mixed method. This study obtained data through crawling data on Twitter using RStudio version 4.2.2 software. The data processed in this study is UGC in the form of tweets with the keywords "goride" and "grabbike" which are products of each online transportation brand. Data collection in this study was carried out from November 1 2022 to November 30, 2022. Then the data was processed using sentiment analysis and text visualization using Wordcloud to analyze what topics are frequently discussed by users. The results show the position as well as the advantages and disadvantages of each online transportation application.

Leveraging Sentiment Analysis for Twitter Data to Uncover User Opinions and Emotions

Huge amounts of emotion are expressed on social media in the form of tweets, blogs, and updates to posts, statuses, etc. Twitter, one of the most well-known microblogging platforms, is used in this essay. Twitter is a social networking site that enables users to post status updates and other brief messages with a maximum character count of 280. Twitter sentiment analysis is the application of sentiment analysis to Twitter data (tweets) in order to derive user sentiments and opinions. Due to the extensive usage, we intend to reflect the mood of the general people by examining the thoughts conveyed in the tweets. Numerous applications require the analysis of public opinion, including businesses attempting to gauge the market response to their products, the prediction of political outcomes, and the analysis of socioeconomic phenomena like stock exchange. Sentiment classification attempts to estimate the sentiment polarity of user updates automatically. So, in order to categorize a tweet as good or negative, we need a model that can accurately discern sarcasm from the lexical meaning of the text. The main objective is to create a practical classifier that can accurately classify the sentiment of twitter streams relating to GST and Tax. Python is used to carry out the suggested algorithm.

Pergeseran Tradisi Nanakusa-Gayu bagi Masyarakat Jepang Dewasa Ini

Shortly after the New Year celebration (oshogatsu) in Japan, there is a hereditary tradition of serving nanakusa-gayu dishes. Nanakusa-gayu is rice porridge especially served by mixing seven kinds of leaves that are considered sacred by the Japanese. The seven types of leaves include seri, nazuna, hakobera, suzuna, suzushiro, hotokenoza, and gogyou leaves. This research discusses the tradition of eating nanakusa-gayu dishes for Japanese society in this modern era. The method used in this study is descriptive qualitative with data in the form of tweets from social media twitter on January 7, 2023. The results of the research show that there are still many Japanese people who carry out the tradition of eating nanakusa porridge. However, there have been several shifts in tradition, such as the ingredients, when to eat, how to get the ingredients, to the members of the family who participate in eating the nanakusa porridge. In addition, it is also known that there is correlation between the implementation of the nanakusa-gayu tradition with the hope of avoiding various kinds of diseases. This correlation cannot be separated from the activity of picking baby vegetables which is the basic core of the nanakusa-gayu tradition. This activity requires a person to keep moving along the streets while looking for baby vegetables. By doing so, the Japanese will have a healthy body, so they are immune from disease. Keywords: nanakusa-gayu; oseichi ryouri; oshougatsu, tweet.

AI model GPT-3 (dis)informs us better than humans.

Artificial intelligence (AI) is changing the way we create and evaluate information, and this is happening during an infodemic, which has been having marked effects on global health. Here, we evaluate whether recruited individuals can distinguish disinformation from accurate information, structured in the form of tweets, and determine whether a tweet is organic or synthetic, i.e., whether it has been written by a Twitter user or by the AI model GPT-3. The results of our preregistered study, including 697 participants, show that GPT-3 is a double-edge sword: In comparison with humans, it can produce accurate information that is easier to understand, but it can also produce more compelling disinformation. We also show that humans cannot distinguish between tweets generated by GPT-3 and written by real Twitter users. Starting from our results, we reflect on the dangers of AI for disinformation and on how information campaigns can be improved to benefit global health.

Cancel Culture di Twitter: Etnografi Virtual Kasus Gofar Hilman dan Radio Prambors di Akun @Prambors

The cancel culture phenomenon is an action that has become a culture in today's digital era, especially on social media platforms. The case of Gofar Hilman and Radio Prambors that went viral at that time made us as researchers want to take a deeper look at the virtual ethnography of the case, especially on Twitter. The purpose of this research is to get an idea about the effect of cancel culture on Twitter on people's responses in Indonesia. This study uses a constructivist paradigm using qualitative research methods and descriptive studies. To strengthen the validity of this study, data collection techniques were used through literature studies about new media and document analysis in the form of Tweets in the comments column uploaded by Radio Prambors’s account, @Prambors on Twitter. The results of this study indicates that the cancel culture in Indonesia often shows their cohesiveness by throwing comments in the form of negative sentiments, but in the end sometimes these actions produce something with action after a phenomenon has gone viral.

TWITTER SENTIMENT ANALYSIS

Sentiment analysis is a popular research field that aims to extract opinions, attitudes, and emotions from social media platforms like Twitter. Traditionally, sentiment analysis has focused on analyzing textual data. Twitter, being a well-known microblogging platform where users post updates in the form of tweets, is often used as a data source for sentiment analysis studies. In this particular paper, the authors utilize a publicly available labeled dataset from Kaggle. They also propose a set of preprocessing steps to enhance the manageability of tweets for natural language processing techniques. Since the dataset consists of pairs of tweets and their corresponding sentiment labels, supervised machine learning is employed. The authors propose sentiment analysis models based on naive Bayes, logistic regression, and support vector machine algorithms with the objective of achieving more effective sentiment analysis. In Twitter sentiment analysis, tweets are typically categorized into positive or negative sentiment. Machine learning classifiers can be utilized for this purpose. These classifiers can be valuable for various entities such as businesses, political parties, and analysts, as they can evaluate the sentiments expressed towards them. By appropriately training the machine learning models with labeled data, tweets can be accurately classified without the need for a predefined word database. Consequently, machine learning techniques offer a superior and faster approach to performing sentiment analysis. Keywords: Twitter; sentiment; Web data; text mining; SVM; Bayesian algorithm; hybrid; ensembles

Klasifikasi Sentimen Tragedi Kanjuruhan Pada Twitter Menggunakan Algoritma Naïve Bayes

The Kanjuruhan Malang incident occurred on October 1 and resulted in 132 deaths, 96 serious injuries and 484 minor injuries. The cause of the riot occurred due to provocation between Arema Malang supporters and Persebaya Surabaya supporters who mentioned harsh words and other provocative actions that caused anger on both sides. Sentiment analysis of the Kanjuruhan tragedy using the Naive Bayes method was conducted through tweets taken through Twitter to understand the public's perception of the incident. The Naïve Bayes algorithm is performed for the sentiment classification of tweet data which is applied by processing the tweet text and classifying it into positive, negative, and neutral. In this study using data as much as 4843 data and carried out with tweet data that has been crawled resulting in 2,042 data. This research aims to classify sentiment and determine the level of accuracy in the Multinomial Naïve Bayes algorithm in the Kanjuruhan tragedy using a dataset in the form of tweets from twitter social media. The processed tweet data is divided into two types, namely 90% training data and 10% test data. The results of this classification get a Naïve Bayes accuracy of 75% with a precission of 73%, recall of 75%, and f1-score value of 74%. The results of the tweet data used in this study can be concluded that the Naïve Bayes algorithm has a fairly good accuracy value.

Sentimental Analysis on Product Reviews Using Support Vector Machine and Nave Bayes

People nowadays use internet platforms to exchange ideas, share opinions, and learn online. Huge amounts of data are being poured into social media in the form of tweets, blogs, and updates on articles and items, among other things. The data is all unorganized and unprocessed. It is necessary to arrange and examine it. It takes a long time to analyze and process information using traditional methods and is impossible to analyze each and every sentence. So, there is a need to have a better approach. It can be done through sentimental analysis which extracts the opinion of a user in a piece of text data. This sentimental analysis will predict the polarity of the sentence, whether the given sentence is positive or a negative one. The sentimental analysis can be achieved through three approaches namely lexicon based, machine learning based and hybrid approach. This sentimental analysis is a part of NLP. This project aims to perform sentimental analysis using machine learning techniques and few natural language processing techniques on a product reviews dataset.

Sentiment Analysis From Twitter About Covid-19 Vaccination in Indonesia Using Naive Bayes and Xgboost Classifier Algorithm

The pandemic that hit the world has greatly impacted our life. But after some time, it seems that it will be going to end because the vaccine has already been made. In response to this, some people expressed their opinions about this vaccination on social media, for example, in the form of tweets on Twitter. The authors use those opinions or tweets as sentiment analysis material to determine the assessment of this vaccination. The tweet data in this study was obtained through data crawling using the Twitter API with the Python programming language. The variables used in this case are public tweets and their sentiments. This sentiment analysis process uses the Classification method with the Naive Bayes Classifier and will be compared with the XGBoost Classifier algorithm. The results of this study indicate that people are more likely to respond positively to this vaccination. In this case, the Naive Bayes Classifier got better performance with 0.95 from ROC - AUC Score and 134 ms in runtime compared to the XGBoost Classifier algorithm with 0.882 in ROC - AUC Score and 1 minute and 59 seconds in runtime.

Analyzing Cyberbullying Negative Content on Twitter Social Media with the RoBERTa Method

Social media is an online-based communication tool that can make it easier for users to interact among fellow users without any area and time restrictions. Indonesia has the highest number of social media users in the world. The Twitter social media platform is a place where users can pour out their whole hearts in the form of tweets. Free and diverse interactions on Twitter have a considerable influence on the psychological condition of its users. Cyberbullying or online bullying is an act of humiliating or hurting other people's feelings intentionally and repeatedly on social media, messages, or in other ways. In this study, the RoBERTa classification method was used to detect cyberbullying tweets with a best accuracy score of 86.9% and an f1-score of 77.5%.