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Artículos de revistas sobre el tema "Twitter data analytics"

Autor: Grafiati

Publicado: 10 de marzo de 2023

Última modificación: 28 de julio de 2025

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1

Gukanesh, A. V., G. Karthick Kumar, and K. Karthik Raja Kumar N. Saranya. "Twitter Data Analytics – Sentiment Analysis of An Election." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 1600–1603. http://dx.doi.org/10.31142/ijtsrd11457.

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Negara, Edi Surya, Ria Andryani, and Prihambodo Hendro Saksono. "Analisis Data Twitter: Ekstraksi dan Analisis Data G eospasial." Jurnal INKOM 10, no. 1 (2016): 27. http://dx.doi.org/10.14203/j.inkom.433.

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Data geospasial pada media sosial Twitter dapat dimanfaatkan untuk mengetahui informasi spasial (lokasi) yang merupakan lokasi sumber munculnya persepsi publik terhadap sebuah isu di media sosial. Besarnya produksi data geospasial yang dihasilkan oleh Twitter memberikan peluang besar untuk dapat dimanfaatkan oleh berbagai pihak sehingga menghasilkan informasi yang lebih bernilai melalui proses Twitter Data Analytics. Proses pemanfaatan data geospasial Twitter dimulai dengan melakukan proses ekstraksi terhadap informasi spatial berupa titik koordinat pengguna Twitter. Titik koordinat pengguna T

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Hoeber, Orland, Larena Hoeber, Maha El Meseery, Kenneth Odoh, and Radhika Gopi. "Visual Twitter Analytics (Vista)." Online Information Review 40, no. 1 (2016): 25–41. http://dx.doi.org/10.1108/oir-02-2015-0067.

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Purpose – Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues. Design/methodology/approach – Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparkl

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4

Neetu, Anand *1 Tapas Kumar 2. "TWITTER ANALYTICS AND VISUALIZATION USING R." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 496–501. https://doi.org/10.5281/zenodo.829749.

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It is an era of Internet technology various social networking sites are gain popularity worldwide. The data generated from these sites are growing day by day .Social media plays a very important role in most of the people's life. People communicate freely and share their comments, ideas on numerous events on any of the various social media like Twitter, Google+, Facebook, etc. Their views are useful for generating solutions and creating awareness about several problems .Twitter is the micro blogging site that has become very well-liked all over the world. These days, there is a continuing tren

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A., V. Gukanesh, Karthick Kumar G., Karthik Raja Kumar K., and Saranya N. "Twitter Data Analytics - Sentiment Analysis of An Election." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 1600–1603. https://doi.org/10.31142/ijtsrd11457.

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Social media is used to analyze political campaigns, stock market, movies, medicines, agriculture etc. Twitter a microblogging website where users read and write millions of tweets on a variety of topics daily. This project attempts to analyze the sentiment of the people for the election candidates based upon the live opinions and emotions. The focus of our project is to assign the polarity to each tweet that is whether the user expresses a positive or negative opinion. With the tweets that are extracted, we try to find how frequent their emotions change. We are also trying to classify and dif

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P., Seetha*1 &. I. Sivaprasad Manivannan2. "INFERENCE ATTACK TWITTER USERS USING PUBLIC CLICK ANALYTICS AND TWITTER META DATA." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 4 (2019): 1–11. https://doi.org/10.5281/zenodo.2631120.

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<strong>Twitter is one of the most popular microblogging services, which is generally used to share news and updates through short messages restricted to 280 characters. However, its open nature and  large  user  base  are frequently exploited  by  automated spammers,   content polluters,  and other ill-intended users to commit  various  cyber crimes, such as cyberbullying, trolling, rumor dissemination, and stalking. Accordingly, a number of approaches have been proposed by researchers to Address these problems. However, most of these app

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Singh, Ravindra Kumar. "Effective Information Retrieval Framework for Twitter Data Analytics." International Journal of Information Retrieval Research 13, no. 1 (2023): 1–21. http://dx.doi.org/10.4018/ijirr.325798.

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The widespread adoption of opinion mining and sentiment analysis in higher cognitive processes encourages the need for real time processing of social media data to capture the insights about user's sentiment polarity, user's opinions, and current trends. In recent years, lots of studies were conducted around the processing of data to achieve higher accuracy. But reducing the time of processing still remained challenging. Later, big data technologies came into existence to solve these challenges but those have its own set of complexities along with having hardware deadweight on the system. The

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Abdullah, Yusuf, Asep Miftahuddin, Rispiaga Rispiaga, Deni Mulyana, and Isbandi Isbandi. "Leveraging Social Media Data Analytics for Tourism Marketing Policies in West Java, Indonesia." Jurnal Manajemen Pelayanan Publik 7, no. 1 (2023): 112. http://dx.doi.org/10.24198/jmpp.v7i1.45229.

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After the government failed to release updated data on the handling of COVID-19, the status of the pandemic in Indonesia has been declared to have ended and transitioned into an endemic disease. This policy has received various responses from the public, including the tourism sector. Therefore, the objective of this research is to gain a comprehensive understanding of the public perception regarding the issue of tourism during the endemic period by examining how people communicate through social media, particularly Twitter. The research method employed is the collection of Twitter conversation

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Et. al., Vedant Karmalkar,. "Twego Trending: Data Analytics Based Search Engine Using Elasticsearch." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 246–51. http://dx.doi.org/10.17762/turcomat.v12i1s.1764.

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Twitter monitoring enables firms to consider their market, stay on track of what is being said regarding their company and contenders, and uncover emerging market trends. Twego Trending is a platform where data will be viewed and structured by an automated procedure of analyzing and processing tweets data and classifying it into various hash statistics and visualizations. Implementing Twego forecasting analysis on Twitter data using various technologies may help businesses know how consumers talk about their product. Twitter has more than 340 million active users and almost 500 millions tweets

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Al-Ibrahim, Fatimah, and Zakarya A. Alzamil. "Big Data Contextual Analytics Study on Arabic Tweets Summarization." International Journal of Knowledge and Systems Science 10, no. 4 (2019): 18–34. http://dx.doi.org/10.4018/ijkss.2019100102.

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Twitter represents a source of information as well as a free space for people to express their opinions on diverse topics. The use of twitter is rapidly increasing and generates a massive amount of data from several types and forms, in which searching for relevant tweets in a specific topic is hard manually due to irrelevant tweets. There has been much research on English tweets for understanding context; however, in spite of the fact that the Twitter active Arabic users are over hundreds of millions, there are very limited studies that have investigated Arabic tweets to produce an automatic s

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Haghighati, Amir, and Kamran Sedig. "VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data." Data 5, no. 1 (2020): 20. http://dx.doi.org/10.3390/data5010020.

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Through social media platforms, massive amounts of data are being produced. As a microblogging social media platform, Twitter enables its users to post short updates as “tweets” on an unprecedented scale. Once analyzed using machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight into different domains of discussion and public opinion. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types o

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Rodrigues, Anisha P., Roshan Fernandes, Adarsh Bhandary, Asha C. Shenoy, Ashwanth Shetty, and M. Anisha. "Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques." Wireless Communications and Mobile Computing 2021 (October 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/3920325.

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Twitter is a popular microblogging social media, using which its users can share useful information. Keeping a track of user postings and common hashtags allows us to understand what is happening around the world and what are people’s opinions on it. As such, a Twitter trend analysis analyzes Twitter data and hashtags to determine what topics are being talked about the most on Twitter. Feature extraction and trend detection can be performed using machine learning algorithms. Big data tools and techniques are needed to extract relevant information from continuous steam of data originating from

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Lee, George, Jimmy Lin, Chuang Liu, Andrew Lorek, and Dmitriy Ryaboy. "The unified logging infrastructure for data analytics at Twitter." Proceedings of the VLDB Endowment 5, no. 12 (2012): 1771–80. http://dx.doi.org/10.14778/2367502.2367516.

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Sholehurrohman, Ridho, and Igit Sabda Ilman. "ANALISIS SENTIMEN TWEET KASUS KEBOCORAN DATA PENGGUNAAN FACEBOOK OLEH CAMBRIGDE ANALYTICA." Jurnal Pepadun 3, no. 1 (2022): 140–47. http://dx.doi.org/10.23960/pepadun.v3i1.108.

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The case of the Facebook user data leak by Cambridge Analytica has been spotlight in the public lately. Many of the citizens has participated discussing this case, especially in social media Twitter. Sentiment analysis is a computational research of opinions and emotions sentiment that are expressed textually. This study aims to classify positive and negative sentiment from Twitter data and to determine the accuracy of the classification model using Naïve Bayes Classifier method. Based on experiment conducted by tweet data with the “Zuckerberg” and “Cambridge Analytics” keywords, it has been p

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Subhani, Shaik, and Naga Malleswara Rao Nallamothu. "Optimizing Search and Data Analytics of Twitter Data using Elastic Search Algorithms." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 427–33. https://doi.org/10.35940/ijeat.F1535.089620.

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Quick data acquisition and analysis became an important tool in the contemporary era. Real time data is made available in World Wide Web (WWW) and social media. Especially social media data is rich in opinions of people of all walks of life. Searching and analysing such data provides required business intelligence (BI) for applications of various domains in the real world. The application may be in the area of politics or banking or insurance or healthcare industry. With the emergence of cloud computing, volumes of data are added to cloud storage infrastructure and it is growing exponentially.

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Chae, Bongsug (Kevin). "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research." International Journal of Production Economics 165 (July 2015): 247–59. http://dx.doi.org/10.1016/j.ijpe.2014.12.037.

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Arifin, Firman, Budi Nur Iman, Budi Nur Iman, et al. "Data Analytics to Examine Trending Topics for Indonesian Election 2019." INOVTEK Polbeng - Seri Informatika 4, no. 2 (2019): 235. http://dx.doi.org/10.35314/isi.v4i2.984.

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Understanding public interest and opinion are necessary tasks in high intense political competition. Utilizing big data analytics from social media provide an important source of information that candidates can utilize, manage and even engage them in targeted political campaigning agenda. One of the source in big data is social media’s interactions. Social media empowers public to participate proactivelyin the campaigning activities. This paper examines trends gathered from data analytics of two contenders’ group for Indonesian Election in 2019. It tracks the recent patterns of people engageme

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Razis, Gerasimos, Georgios Theofilou, and Ioannis Anagnostopoulos. "Latent Twitter Image Information for Social Analytics." Information 12, no. 2 (2021): 49. http://dx.doi.org/10.3390/info12020049.

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The appearance of images in social messages is continuously increasing, along with user engagement with that type of content. Analysis of social images can provide valuable latent information, often not present in the social posts. In that direction, a framework is proposed exploiting latent information from Twitter images, by leveraging the Google Cloud Vision API platform, aiming at enriching social analytics with semantics and hidden textual information. As validated by our experiments, social analytics can be further enriched by considering the combination of user-generated content, latent

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He, Wu, Weidong Zhang, Xin Tian, Ran Tao, and Vasudeva Akula. "Identifying customer knowledge on social media through data analytics." Journal of Enterprise Information Management 32, no. 1 (2019): 152–69. http://dx.doi.org/10.1108/jeim-02-2018-0031.

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Purpose Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management. Design/methodology/approach The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text min

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Udanor, Collins, Stephen Aneke, and Blessing Ogechi Ogbuokiri. "Determining social media impact on the politics of developing countries using social network analytics." Program 50, no. 4 (2016): 481–507. http://dx.doi.org/10.1108/prog-02-2016-0011.

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Purpose The purpose of this paper is to use the Twitter Search Network of the Apache NodeXL data discovery tool to extract over 5,000 data from Twitter accounts that twitted, re-twitted or commented on the hashtag, #NigeriaDecides, to gain insight into the impact of the social media on the politics and administration of developing countries. Design/methodology/approach Several algorithms like the Fruchterman-Reingold algorithm, Harel-Koren Fast Multiscale algorithm and the Clauset-Newman-Moore algorithms are used to analyse the social media metrics like betweenness, closeness centralities, etc

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Anu, Sharma, and Kumar Vivek. "Machine Learning Prospects: Insights for Social Media Data Mining and Analytics." International Journal of Innovative Research in Computer Science and Technology (IJIRCST) 11, no. 03 (2023): 12–19. https://doi.org/10.5281/zenodo.8109929.

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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 fi

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Park, Hyejin, Ivan Ureta, and Boyoung Kim. "Trend Analysis of Decentralized Autonomous Organization Using Big Data Analytics." Information 14, no. 6 (2023): 326. http://dx.doi.org/10.3390/info14060326.

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Decentralized Autonomous Organizations (DAOs) have gained widespread attention in academia and industry as potential future models for decentralized governance and organization. In order to understand the trends and future potential of this rapidly growing technology, it is crucial to conduct research in the field. This research aims at a data-driven approach for the objective content analysis of big data related to DAOs, using text mining and Latent Dirichlet Allocation (LDA)-based topic modeling. The study analyzed tweets with the hashtag #DAO and all Reddit data with “DAO”. The results were

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Muhammed Jouhar K. K. and K. Aravinthan. "A bigdata analytics method for social media behavioral analysis." Scientific Temper 15, no. 03 (2024): 2692–99. http://dx.doi.org/10.58414/scientifictemper.2024.15.3.37.

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Twitter on web-based entertainment has become an important part of everyday life. This medium provides a list of current events in real time, most of which is difficult to understand, so it must be sorted to find useful information. Human biology, pharmacology, and experimental factors influence their behavior. Twitter tweets are a text store that can reflect human emotions and sentiments. Behavior Analytics (BA) is analyzing the behavior of individuals. BA can be used to filter useful information from tweets in healthcare and business applications. This paper presents the analysis of human be

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Harfoushi, Osama, Dana Hasan, and Ruba Obiedat. "Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison." Modern Applied Science 12, no. 7 (2018): 49. http://dx.doi.org/10.5539/mas.v12n7p49.

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The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals’ opinions, image, views, and twits fro

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Drescher, Larissa S., Carola Grebitus, and Jutta Roosen. "Exploring Food Consumption Trends on Twitter with Social Media Analytics: The Example of #Veganuary." EuroChoices 22, no. 2 (2023): 45–52. http://dx.doi.org/10.1111/1746-692x.12403.

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SummaryUsing the example of the hashtag #veganuary, a neologism of vegan and January, on Twitter with over 52,000 tweets from 2022, this article shows how Social Media Analytics can provide valuable insights into timing, volume and sentiment within any emerging (consumer) trend. Social Media Analytics is increasingly being used for the analysis of Social Media data. Whether consumers, politicians or entrepreneurs, all stakeholders in the food value chain are present on Social Media and talk about various trends in food and agriculture. In the form of an overview article, this contribution uses

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Lou, Shanshan. "Applying Data Analytics to Social Media Advertising: A Twitter Advertising Campaign Case Study." Journal of Advertising Education 21, no. 1 (2017): 26–32. http://dx.doi.org/10.1177/109804821702100106.

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This paper presents a class case study of an assignment that asked students to use a Twitter follower report to design a Twitter advertising campaign. The purpose of this case study is to immerse students in a real social media environment and help them become familiar with analyzing social media data to develop advertising campaigns. Students' interview responses suggest that incorporating a project that requires social media analytics techniques in an advertising class can help them better understand the role of secondary research and database analysis in developing consumer profiles and mak

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Iftikhar, Rehan, and Mohammad Saud Khan. "Social Media Big Data Analytics for Demand Forecasting." Journal of Global Information Management 28, no. 1 (2020): 103–20. http://dx.doi.org/10.4018/jgim.2020010106.

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Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is conc

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Suluh Gembyeng Ciptadi and Alfath Marzuki. "SOCIAL MEDIA ANALYTICS OF BOOSTER VACCINE COVID-19 DISCOURSE ON TWITTER." Sunan Ampel Review of Political and Social Sciences 2, no. 2 (2023): 118–28. http://dx.doi.org/10.15642/sarpass.2023.2.2.118-128.

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People around the world are currently carrying out booster vaccinations in the midst of the outbreak of the Omicron variant of the COVID-19 virus. In Indonesia, vaccine booster activities have been carried out since January 12, 2022. The Indonesian government makes vaccines free and encourages the public to immediately carry out vaccine booster. Vaccine booster information and campaigns also appear on social media. Twitter as one of the social media that is widely used in Indonesia is also enlivened with discussions about vaccine boosters. The objective of this research is to see how the vacci

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Aljojo, Nahla. "Examining Heterogeneity Structured on a Large Data Volume with Minimal Incompleteness." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 9, no. 2 (2021): 30–37. http://dx.doi.org/10.14500/aro.10857.

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While Big Data analytics can provide a variety of benefits, processing heterogeneous data comes with its own set of limitations. A transaction pattern must be studied independently while working with Bitcoin data, this study examines twitter data related to Bitcoin and investigate communications pattern on bitcoin transactional tweet. Using the hashtags #Bitcoin or #BTC on Twitter, a vast amount of data was gathered, which was mined to uncover a pattern that everyone either (speculators, teaches, or the stakeholders) uses on Twitter to discuss Bitcoin transactions. This aim is to determine the

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Awan, Mazhar Javed, Awais Yasin, Haitham Nobanee, et al. "Fake News Data Exploration and Analytics." Electronics 10, no. 19 (2021): 2326. http://dx.doi.org/10.3390/electronics10192326.

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Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through m

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Mukesh Kumar Sah., Rishabh Sharma & Amritpal Singh. "Real Time Data Pipeline for Twitter Trends Analysis." International Journal for Modern Trends in Science and Technology 7, no. 05 (2021): 44–48. http://dx.doi.org/10.46501/ijmtst0705006.

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In social media, Information is present in enormous amount. Extracting data from processed information from social media gives us diverse usages in various fields. In the field of Business Analytics, HealthCare, Technologies and Trending Topics in Social Media posted by the user. Extracting information from social media is providing number of benefits such as knowledge about the latest Technology, Medical field, Business Decisions, etc. Twitter is solitary of the social media which allows the user post tweets of limited number of characters and share the tweet to their followers. Twitter allow

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Manan koli, Abdul, Muqeem Ahmed, and . "Election Prediction Using Big Data Analytics-A Survey." International Journal of Engineering & Technology 7, no. 4.5 (2018): 366. http://dx.doi.org/10.14419/ijet.v7i4.5.20108.

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Social media has received much attention due to it's real-time and interactive nature for political discourse, especially around election times. Recently studies have explored the power of social media platforms such as Twitter or Facebook, on recording current social trends and predicting the voting outcomes of an area. These social media generate a large amount of raw data that can be used in decision making for election predictions. This tremendously generated data is referred to as “Big data”. After scrutinized a lot of research work related to election prediction, a survey paper is presen

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Mazumdar, Suvodeep, and Dhavalkumar Thakker. "Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks." Future Internet 12, no. 12 (2020): 210. http://dx.doi.org/10.3390/fi12120210.

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This paper presents a long-term study on how the public engage with discussions around citizen science and crowdsourcing topics. With progress in sensor technologies and IoT, our cities and neighbourhoods are increasingly sensed, measured and observed. While such data are often used to inform citizen science projects, it is still difficult to understand how citizens and communities discuss citizen science activities and engage with citizen science projects. Understanding these engagements in greater depth will provide citizen scientists, project owners, practitioners and the generic public wit

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Thin, Thin Swe, Phyu Phyu, and Pa Pa Thein Sandar. "Predition Model for Stock Price on Big Data Analytics." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1444–46. https://doi.org/10.5281/zenodo.3590891.

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Prediction in the stock market is very challenging in these days. Large datasets available from Twitter micro blogging platform and widely available stock market records. Machine learning was employ to conduct sentiment analysis of data and to estimate for future stock prices. The relation between sentiments and the stock value is to be determined. A comparative study of these algorithms Multiple linear Regression, Support Vector Machine and Artificial Neural Network are done. Thin Thin Swe | Phyu Phyu | Sandar Pa Pa Thein "Predition Model for Stock Price on Big Data Analytics" Publi

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Balan, Shilpa, and Janhavi Rege. "Mining for Social Media: Usage Patterns of Small Businesses." Business Systems Research Journal 8, no. 1 (2017): 43–50. http://dx.doi.org/10.1515/bsrj-2017-0004.

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AbstractBackground: Information can now be rapidly exchanged due to social media. Due to its openness, Twitter has generated massive amounts of data. In this paper, we apply data mining and analytics to extract the usage patterns of social media by small businesses. Objectives: The aim of this paper is to describe with an example how data mining can be applied to social media. This paper further examines the impact of social media on small businesses. The Twitter posts related to small businesses are analyzed in detail. Methods/Approach: The patterns of social media usage by small businesses a

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Gupta, Vibhuti, and Rattikorn Hewett. "Real-Time Tweet Analytics Using Hybrid Hashtags on Twitter Big Data Streams." Information 11, no. 7 (2020): 341. http://dx.doi.org/10.3390/info11070341.

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Twitter is a microblogging platform that generates large volumes of data with high velocity. This daily generation of unbounded and continuous data leads to Big Data streams that often require real-time distributed and fully automated processing. Hashtags, hyperlinked words in tweets, are widely used for tweet topic classification, retrieval, and clustering. Hashtags are used widely for analyzing tweet sentiments where emotions can be classified without contexts. However, regardless of the wide usage of hashtags, general tweet topic classification using hashtags is challenging due to its evolv

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Pradana, Meirza Luthfi, Vincencius Alvian Pratama, Rika Aulia Ramdhani, and Panji Putranto Nugrahagung. "Memaknai TWK KPK Dalam Reproduksi Wacana Dengan Pemanfaatan Modal Sosial: Studi Kasus Data Percakapan TWK KPK di Media Sosial Twitter." Jurnal PolGov 4, no. 2 (2022): 1–49. http://dx.doi.org/10.22146/polgov.v4i2.3637.

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Kajian ini bertujuan untuk menganalisis pemetaan aktor dan pola sentimen yang terbentuk di media sosial Twitter dalam wacana isu “Tes Wawasan Kebangsaan Komisi Pemberantasan Korupsi (TWK KPK)”. Artikel ini menggunakan pendekatan mix method, yakni kualitatif dan kuantitatif, dengan menggunakan pisau analisis Social Network Analysis (SNA) . Data dalam kajian ini diperoleh dari media sosial Twitter yang diolah dengan Big Data Analytics dari tanggal 16 Mei 2021 sampai dengan 13 September 2021. Tulisan ini mengungkap terdapat tiga akun eks pejabat publik dan dua akun media nasional yang menjadi top

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Grasso, Valentina, Alfonso Crisci, Marco Morabito, et al. "Italian codified hashtags for weather warning on Twitter – who is really using them?" Advances in Science and Research 14 (April 4, 2017): 63–69. http://dx.doi.org/10.5194/asr-14-63-2017.

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Abstract. During emergencies, an increasing number of messages are shared through social media platforms, becoming a primary source of information for lay people and emergency managers. Weather services and institutions have started to employ social media to deliver weather warnings even if sometimes this communication lacks in strategy. In Twitter, for example, hashtagging is very important to associate messages with certain topics; in recent years, codified hashtagging is emerging as a practical way to coordinate Twitter conversations during emergencies and quickly retrieve relevant informat

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Sharma, Anu, and Vivek Kumar. "Machine Learning Prospects: Insights for Social Media Data Mining and Analytics." International Journal of Innovative Research in Computer Science and Technology 11, no. 3 (2023): 12–19. http://dx.doi.org/10.55524/ijircst.2023.11.3.3.

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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 fi

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Gadipally Prasanth. "Advanced Feature Extraction and Visualization Techniques for Enhanced Sentiment Analysis on Twitter Data." Journal of Information Systems Engineering and Management 10, no. 3 (2025): 1536–48. https://doi.org/10.52783/jisem.v10i3.8208.

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The rapid growth of social media platforms, particularly Twitter, has led to an unprecedented surge in user-generated data, which presents both opportunities and challenges for sentiment analysis. Traditional sentiment analysis methods often struggle with the noisy and unstructured nature of Twitter data, necessitating advanced techniques for effective feature extraction and visualization. This paper presents a novel approach to enhancing sentiment analysis on Twitter data through advanced feature extraction and visualization techniques. We propose a multi-faceted feature extraction framework

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Tang, LiYaning, Yiming Zhang, Fei Dai, Yoojung Yoon, Yangqiu Song, and Radhey S. Sharma. "Social Media Data Analytics for the U.S. Construction Industry: Preliminary Study on Twitter." Journal of Management in Engineering 33, no. 6 (2017): 04017038. http://dx.doi.org/10.1061/(asce)me.1943-5479.0000554.

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Queiroz, Maciel M. "A framework based on Twitter and big data analytics to enhance sustainability performance." Environmental Quality Management 28, no. 1 (2018): 95–100. http://dx.doi.org/10.1002/tqem.21576.

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Sheikh, Suhail Ahmad. "Data and Democracy: Social Media Analytics in Political Campaign Strategies." International Journal of English Literature and Social Sciences 9, no. 1 (2024): 204–11. http://dx.doi.org/10.22161/ijels.91.27.

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This research explores the intricate relationship between social media and politics, focusing on the impact of digital media on political communication and engagement. The examined literature encompasses diverse dimensions, including the logic of connective action in contentious politics, the role of social media during uprisings, and the evolving landscape of election campaigns with platforms like Twitter. Case studies scrutinize the use of social media analytics in campaigns, shedding light on predictive modelling, personalized messaging, and ethical challenges. The study delves into the int

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Biswas, Joyeshree. "Decoding COVID-19 Conversations with Visualization: Twitter Analytics and Emerging Trends." Journal of Computer Science Engineering and Software Testing 10, no. 1 (2024): 21–31. http://dx.doi.org/10.46610/jocses.2024.v10i01.003.

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This study delves into the vast landscape of COVID-19 discussions on Twitter, aiming to unveil pertinent insights and emerging trends within this dynamic social media platform. Analyzing a substantial volume of Twitter data related to the pandemic, our research scrutinizes the content, sentiments, and patterns of conversations among users. By employing advanced analytics, we discern key themes, prevalent sentiments, and the evolution of discourse over time. This investigation not only provides a comprehensive overview of the diverse topics encompassed within COVID-19 discussions on Twitter but

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Gunjan, Bedi* &. Anand Tamrakar. "SENTIMENT ANALYSIS OF A MOVIE USING TWITTER AND TEXT ANALYTICS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 4 (2019): 160–65. https://doi.org/10.5281/zenodo.2640752.

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A movie review is an article describing the movie and giving critics on it that allows the viewer or anyone to know and understand whether it is really worth to watch that movie or not. The practice of using analytics, using audience sentiment, to measure movie’s success is not a new phenomenon. The present work is based on the major project work carried out as a part of Bachelor of Engineering programme and is intended to demonstrate gauging audience sentiments about the movie “Alita: Battle Angel”. Text Analytics using Twitter data is used to calculate sentiments using two

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Park, Seunghyun Brian, Jichul Jang, and Chihyung Michael Ok. "Analyzing Twitter to explore perceptions of Asian restaurants." Journal of Hospitality and Tourism Technology 7, no. 4 (2016): 405–22. http://dx.doi.org/10.1108/jhtt-08-2016-0042.

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Purpose The purpose of this paper is to use Twitter analysis to explore diner perceptions of four types of Asian restaurants (Chinese, Japanese, Korean and Thai). Design/methodology/approach Using 86,015 tweets referring to Asian restaurants, this research used text mining and sentiment analysis to find meaningful patterns, popular words and emotional states in opinions. Findings Twitter users held mingled perceptions of different types of Asian restaurants. Sentiment analysis and ANOVA showed that the average sentiment scores for Chinese restaurants was significantly lower than the other thre

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Löchner, Marc, and Dirk Burghardt. "Using HyperLogLog to Prevent Data Retention in Social Media Streaming Data Analytics." ISPRS International Journal of Geo-Information 12, no. 2 (2023): 60. http://dx.doi.org/10.3390/ijgi12020060.

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Social media data are widely used to gain insights about social incidents, whether on a local or global scale. Within the process of analyzing and evaluating the data, it is common practice to download and store it locally. Considerations about privacy protection of social media users are often neglected thereby. However, protecting privacy when dealing with personal data is demanded by laws and ethics. In this paper, we introduce a method to store social media data using the cardinality estimator HyperLogLog. Based on an exemplary disaster management scenario, we show that social media data c

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He, Chao, and Da Hu. "Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework." Applied Sciences 15, no. 8 (2025): 4330. https://doi.org/10.3390/app15084330.

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Social media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders to identify emerging threats, prioritize resource allocation, and optimize relief operations through data-driven insights. We present an AI-powered framework that combines natural language processing with geospatial visualization to analyze disaster-related social media content. Our solution features a text analysis model that achieved an 81.4% F1

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Walker, Rick, Llyr ap Cenydd, Serban Pop, et al. "Storyboarding for visual analytics." Information Visualization 14, no. 1 (2013): 27–50. http://dx.doi.org/10.1177/1473871613487089.

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Analysts wish to explore different hypotheses, organize their thoughts into visual narratives and present their findings. Some developers have used algorithms to ascertain key events from their data, while others have visualized different states of their exploration and utilized free-form canvases to enable the users to develop their thoughts. What is required is a visual layout strategy that summarizes specific events and allows users to layout the story in a structured way. We propose the use of the concept of ‘storyboarding’ for visual analytics. In film production, storyboarding techniques

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Mane, Deepak, Dr Sirbi Kotrappa, and Kiran Shibe. "Sentiment Analytics on Chinese Product Boycott from Multiple Data Sources." Computational Intelligence and Machine Learning 2, no. 1 (2021): 16–25. http://dx.doi.org/10.36647/ciml/02.01.a003.

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Sentiment Analysis and Opinion mining is a technique recognizing and drawing out the personalized information underlying a different kind of documents such as text, audio, images and videos. This area of research tries to exaplain the feeling, opinions, emotions of people on something topics. The most relevant classifying a statement as ‘positive’ , ‘negative’ and ‘neutral’ from records/posts obtained from different source system such as Twitter, Facebook , Reddit etc. To predict the sentiment/result of recent Chinese Product Boycott campaign, This paper direct to operate on data received from

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