Littérature scientifique sur le sujet « Twitter data analytics »
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Articles de revues sur le sujet "Twitter data analytics"
Gukanesh, A. V., G. Karthick Kumar et 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 (30 avril 2018) : 1600–1603. http://dx.doi.org/10.31142/ijtsrd11457.
Texte intégralNegara, Edi Surya, Ria Andryani et Prihambodo Hendro Saksono. « Analisis Data Twitter : Ekstraksi dan Analisis Data G eospasial ». Jurnal INKOM 10, no 1 (21 novembre 2016) : 27. http://dx.doi.org/10.14203/j.inkom.433.
Texte intégralHoeber, Orland, Larena Hoeber, Maha El Meseery, Kenneth Odoh et Radhika Gopi. « Visual Twitter Analytics (Vista) ». Online Information Review 40, no 1 (8 février 2016) : 25–41. http://dx.doi.org/10.1108/oir-02-2015-0067.
Texte intégralEt. al., Vedant Karmalkar,. « Twego Trending : Data Analytics Based Search Engine Using Elasticsearch ». Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no 1S (11 avril 2021) : 246–51. http://dx.doi.org/10.17762/turcomat.v12i1s.1764.
Texte intégralAl-Ibrahim, Fatimah, et Zakarya A. Alzamil. « Big Data Contextual Analytics Study on Arabic Tweets Summarization ». International Journal of Knowledge and Systems Science 10, no 4 (octobre 2019) : 18–34. http://dx.doi.org/10.4018/ijkss.2019100102.
Texte intégralLee, George, Jimmy Lin, Chuang Liu, Andrew Lorek et Dmitriy Ryaboy. « The unified logging infrastructure for data analytics at Twitter ». Proceedings of the VLDB Endowment 5, no 12 (août 2012) : 1771–80. http://dx.doi.org/10.14778/2367502.2367516.
Texte intégralHaghighati, Amir, et Kamran Sedig. « VARTTA : A Visual Analytics System for Making Sense of Real-Time Twitter Data ». Data 5, no 1 (19 février 2020) : 20. http://dx.doi.org/10.3390/data5010020.
Texte intégralRodrigues, Anisha P., Roshan Fernandes, Adarsh Bhandary, Asha C. Shenoy, Ashwanth Shetty et M. Anisha. « Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques ». Wireless Communications and Mobile Computing 2021 (25 octobre 2021) : 1–13. http://dx.doi.org/10.1155/2021/3920325.
Texte intégralSholehurrohman, Ridho, et Igit Sabda Ilman. « ANALISIS SENTIMEN TWEET KASUS KEBOCORAN DATA PENGGUNAAN FACEBOOK OLEH CAMBRIGDE ANALYTICA ». Jurnal Pepadun 3, no 1 (1 avril 2022) : 140–47. http://dx.doi.org/10.23960/pepadun.v3i1.108.
Texte intégralChae, 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 (juillet 2015) : 247–59. http://dx.doi.org/10.1016/j.ijpe.2014.12.037.
Texte intégralThèses sur le sujet "Twitter data analytics"
Leis, Machín Angela 1974. « Studying depression through big data analytics on Twitter ». Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/671365.
Texte intégralCarvalho, Eder José de. « Visual analytics of topics in twitter in connection with political debates ». Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-11092017-140904/.
Texte intégralMídias sociais como o Twitter e o Facebook atuam, em diversas situações, como canais de iniciativas que buscam ampliar as ações de cidadania. Por outro lado, certas ações e manifestações na mídia convencional por parte de instituições governamentais, ou de jornalistas e políticos como deputados e senadores, tendem a repercutir nas mídias sociais. Como resultado, gerase uma enorme quantidade de dados em formato textual que podem ser muito informativos sobre ações e políticas governamentais. No entanto, o público-alvo continua carente de boas ferramentas que ajudem a levantar, correlacionar e interpretar as informações potencialmente úteis associadas a esses textos. Neste contexto, este trabalho apresenta dois sistemas orientados à análise de dados governamentais e de mídias sociais. Um dos sistemas introduz uma nova visualização, baseada na metáfora do rio, para análise temporal da evolução de tópicos no Twitter em conexão com debates políticos. Para tanto, o problema foi inicialmente modelado como um problema de clusterização e um método de segmentação de texto independente de domínio foi adaptado para associar (por clusterização) tweets com discursos parlamentares. Uma versão do algorimo MONIC para detecção de transições entre agrupamentos foi empregada para rastrear a evolução temporal de debates (ou agrupamentos) e produzir um conjunto de agrupamentos com informação de tempo. O outro sistema, chamado ATR-Vis, combina técnicas de visualização com estratégias de recuperação ativa para envolver o usuário na recuperação de tweets relacionados a debates políticos e associa-os ao debate correspondente. O arcabouço proposto introduz quatro estratégias de recuperação ativa que utilizam informação estrutural do Twitter melhorando a acurácia do processo de recuperação e simultaneamente minimizando o número de pedidos de rotulação apresentados ao usuário. Avaliações por meio de casos de uso e experimentos quantitativos, assim como uma análise qualitativa conduzida com três especialistas ilustram a efetividade do ATR-Vis na recuperação de tweets relevantes. Para a avaliação, foram coletados dois conjuntos de tweets relacionados a debates parlamentares ocorridos no Brasil e no Canadá, e outro formado por um conjunto de notícias que receberam grande atenção da mídia no período da coleta.
Carlos, Marcelo Aparecido. « Análise de surtos de doenças transmitidas pelo mosquito Aedes aegypti utilizando Big-Data Analytics e mensagens do Twitter ». reponame:Repositório Institucional da UFABC, 2017.
Trouver le texte intégralDissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, 2017.
O uso do big-data aliado a técnicas de mineração de textos vem crescendo a cada ano em diversas áreas da ciência, especialmente na área da saúde, na medicina de precisão, em prontuários eletrônicos entre outros. A motivação desse trabalho parte da hipótese de que é possível usar conceitos de big-data para analisar grandes quantidades de dados sobre as doenças da dengue, chikungunya e zika vírus, para monitorar e antecipar informações sobre possíveis surtos dessas doenças. Entretanto, a análise de grandes volumes de dados - inerente ao estudo em big-data - possui desafios, particularmente, devido à falta de escalabilidade dos algoritmos e à complexidade do gerenciamento dos mais diferentes tipos e estruturas dos dados envolvidos. O principal objetivo desse trabalho é apresentar uma implementação de técnicas de mineração de textos, em especial, aqueles oriundos de redes sociais, tais como o Twitter, aliadas à abordagem de análises em big-data e aprendizado de máquina, para monitorar a incidência das doenças da dengue, chikungunya e zika vírus, todas transmitidas pelo mosquito Aedes aegypti. Os resultados obtidos indicam que a implementação realizada, baseado na junção dos algoritmos de aprendizado de máquina K-Means e SVM, teve rendimento satisfatório para a amostra utilizada em comparação aos registros do Ministério da Saúde, indicando, assim, um potencial para utilização do seu propósito. Observa-se que a principal vantagem das análises em big-data está relacionada à possibilidade de empregar dados não estruturados os quais são obtidos em redes sociais, sites de e-commerce, dentre outros. Nesse sentido, dados que antes pareciam, de certo modo, de pouca importância, passam a ter grande potencial e relevância.
The use of the big-data allied to techniques of text mining has been growing every year in several areas of science, especially in the area of health, precision medicine, electronic medical records among others. The motivation from this work, is based on the hypothesis that it is possible to use big-data concepts to analyze large volumes of data about the dengue disease, chikungunya and zika virus, to monitor and anticipate information about possible outbreaks for diseases. However, the analysis of large volumes of data - inherent in the big-data study - has some challenges, particularly due to the lack of scalability of the algorithms and the complexity of managing the most different types and structures of the data involved. The main objective of this work is to present the implementation of text mining techniques - especially from social networks such as Twitter - allies to the approach of big-data and machine-learned analyzes to monitor the incidence of Dengue, Chikungunya and Zika virus, all transmissions by the mosquito Aedes aegypti. The results obtained indicate that the implementation made based on the combination of machine learning algorithms, K-Means and SVM, got a satisfactory yield for a sample used, if compared the publications of the records of the Ministry of Health, thus indicating a potential for the purpose. It is observed that a main advantage of the big-data analyzes is related to the possibility of employing unstructured data, e-commerce sites, among others. In this sense, data that once seemed, in a way, of little importance, have great potential and relevance.
Gröbe, Mathias. « Konzeption und Entwicklung eines automatisierten Workflows zur geovisuellen Analyse von georeferenzierten Textdaten(strömen) / Microblogging Content ». Master's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-210672.
Texte intégralThis Master's Thesis deals with the conception and exemplary implementation of a workflow for georeferenced Microblogging Content. Data from Twitter is used as an example and as a starting point to think about how to build that workflow. In the field of Data Mining and Text Mining, there was found a whole range of useful software modules that already exist. Mostly, they only need to get lined up to a process pipeline using appropriate preferences. Although a logical order can be defined, further adjustments according to the research question and the data are required. The process is supported by different forms of visualizations such as histograms, tag clouds and maps. This way new knowledge can be discovered and the options for the preparation can be improved. This way of knowledge discovery is already known as Geovisual Analytics. After a review of multiple existing software tools, the programming language R is used to implement the workflow as this language is optimized for solving statistical problems. Finally, the workflow has been tested using data from Twitter and Flickr
Willis, Margaret Mary. « Interpreting "Big Data" : Rock Star Expertise, Analytical Distance, and Self-Quantification ». Thesis, Boston College, 2015. http://hdl.handle.net/2345/bc-ir:104932.
Texte intégralThe recent proliferation of technologies to collect and analyze “Big Data” has changed the research landscape, making it easier for some to use unprecedented amounts of real-time data to guide decisions and build ‘knowledge.’ In the three articles of this dissertation, I examine what these changes reveal about the nature of expertise and the position of the researcher. In the first article, “Monopoly or Generosity? ‘Rock Stars’ of Big Data, Data Democrats, and the Role of Technologies in Systems of Expertise,” I challenge the claims of recent scholarship, which frames the monopoly of experts and the spread of systems of expertise as opposing forces. I analyze video recordings (N= 30) of the proceedings of two professional conferences about Big Data Analytics (BDA), and I identify distinct orientations towards BDA practice among presenters: (1) those who argue that BDA should be conducted by highly specialized “Rock Star” data experts, and (2) those who argue that access to BDA should be “democratized” to non-experts through the use of automated technology. While the “data democrats” ague that automating technology enhances the spread of the system of BDA expertise, they ignore the ways that it also enhances, and hides, the monopoly of the experts who designed the technology. In addition to its implications for practitioners of BDA, this work contributes to the sociology of expertise by demonstrating the importance of focusing on both monopoly and generosity in order to study power in systems of expertise, particularly those relying extensively on technology. Scholars have discussed several ways that the position of the researcher affects the production of knowledge. In “Distance Makes the Scholar Grow Fonder? The Relationship Between Analytical Distance and Critical Reflection on Methods in Big Data Analytics,” I pinpoint two types of researcher “distance” that have already been explored in the literature (experiential and interactional), and I identify a third type of distance—analytical distance—that has not been examined so far. Based on an empirical analysis of 113 articles that utilize Twitter data, I find that the analytical distance that authors maintain from the coding process is related to whether the authors include explicit critical reflections about their research in the article. Namely, articles in which the authors automate the coding process are significantly less likely to reflect on the reliability or validity of the study, even after controlling for factors such as article length and author’s discipline. These findings have implications for numerous research settings, from studies conducted by a team of scholars who delegate analytic tasks, to “big data” or “e-science” research that automates parts of the analytic process. Individuals who engage in self-tracking—collecting data about themselves or aspects of their lives for their own purposes—occupy a unique position as both researcher and subject. In the sociology of knowledge, previous research suggests that low experiential distance between researcher and subject can lead to more nuanced interpretations but also blind the researcher to his or her underlying assumptions. However, these prior studies of distance fail to explore what happens when the boundary between researcher and subject collapses in “N of one” studies. In “The Collapse of Experiential Distance and the Inescapable Ambiguity of Quantifying Selves,” I borrow from art and literary theories of grotesquerie—another instance of the collapse of boundaries—to examine the collapse of boundaries in self-tracking. Based on empirical analyses of video testimonies (N=102) and interviews (N=7) with members of the Quantified Self community of self-trackers, I find that ambiguity and multiplicity are integral facets of these data practices. I discuss the implications of these findings for the sociological study of researcher distance, and also the practical implications for the neoliberal turn that assigns responsibility to individuals to collect, analyze, and make the best use of personal data
Thesis (PhD) — Boston College, 2015
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Sociology
Mullassery, Mohanan Shalini. « Social media data analytics for the NSW construction industry : a study on Twitter ». Thesis, 2022. http://hdl.handle.net/1959.7/uws:67977.
Texte intégral« Event Analytics on Social Media : Challenges and Solutions ». Doctoral diss., 2014. http://hdl.handle.net/2286/R.I.27510.
Texte intégralDissertation/Thesis
Doctoral Dissertation Computer Science 2014
Zaza, Imad. « Ontological knowledge-base for railway control system and analytical data platform for Twitter ». Doctoral thesis, 2018. http://hdl.handle.net/2158/1126141.
Texte intégralSingh, Asheen. « Social media analytics and the role of twitter in the 2014 South Africa general election : a case study ». Thesis, 2018. https://hdl.handle.net/10539/25757.
Texte intégralSocial network sites such as Twitter have created vibrant and diverse communities in which users express their opinions and views on a variety of topics such as politics. Extensive research has been conducted in countries such as Ireland, Germany and the United States, in which text mining techniques have been used to obtain information from politically oriented tweets. The purpose of this research was to determine if text mining techniques can be used to uncover meaningful information from a corpus of political tweets collected during the 2014 South African General Election. The Twitter Application Programming Interface was used to collect tweets that were related to the three major political parties in South Africa, namely: the African National Congress (ANC), the Democratic Alliance (DA) and the Economic Freedom Fighters (EFF). The text mining techniques used in this research are: sentiment analysis, clustering, association rule mining and word cloud analysis. In addition, a correlation analysis was performed to determine if there exists a relationship between the total number of tweets mentioning a political party and the total number of votes obtained by that party. The VADER (Valence Aware Dictionary for sEntiment Reasoning) sentiment classifier was used to determine the public’s sentiment towards the three main political parties. This revealed an overwhelming neutral sentiment of the public towards the ANC, DA and EFF. The result produced by the VADER sentiment classifier was significantly greater than any of the baselines in this research. The K-Means cluster algorithm was used to successfully cluster the corpus of political tweets into political-party clusters. Clusters containing tweets relating to the ANC and EFF were formed. However, tweets relating to the DA were scattered across multiple clusters. A fairly strong relationship was discovered between the number of positive tweets that mention the ANC and the number of votes the ANC received in election. Due to the lack of data, no conclusions could be made for the DA or the EFF. The apriori algorithm uncovered numerous association rules, some of which were found to be interest- ing. The results have also demonstrated the usefulness of word cloud analysis in providing easy-to-understand information from the tweet corpus used in this study. This research has highlighted the many ways in which text mining techniques can be used to obtain meaningful information from a corpus of political tweets. This case study can be seen as a contribution to a research effort that seeks to unlock the information contained in textual data from social network sites.
MT 2018
Gröbe, Mathias. « Konzeption und Entwicklung eines automatisierten Workflows zur geovisuellen Analyse von georeferenzierten Textdaten(strömen) / Microblogging Content ». Master's thesis, 2015. https://tud.qucosa.de/id/qucosa%3A29848.
Texte intégralThis Master's Thesis deals with the conception and exemplary implementation of a workflow for georeferenced Microblogging Content. Data from Twitter is used as an example and as a starting point to think about how to build that workflow. In the field of Data Mining and Text Mining, there was found a whole range of useful software modules that already exist. Mostly, they only need to get lined up to a process pipeline using appropriate preferences. Although a logical order can be defined, further adjustments according to the research question and the data are required. The process is supported by different forms of visualizations such as histograms, tag clouds and maps. This way new knowledge can be discovered and the options for the preparation can be improved. This way of knowledge discovery is already known as Geovisual Analytics. After a review of multiple existing software tools, the programming language R is used to implement the workflow as this language is optimized for solving statistical problems. Finally, the workflow has been tested using data from Twitter and Flickr.
Livres sur le sujet "Twitter data analytics"
Kumar, Shamanth, Fred Morstatter et Huan Liu. Twitter Data Analytics. New York, NY : Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9372-3.
Texte intégralLiu, Huan, Shamanth Kumar et Fred Morstatter. Twitter Data Analytics. Springer London, Limited, 2013.
Trouver le texte intégralTwitter Data Analytics. Springer-Verlag New York Inc., 2013.
Trouver le texte intégralLiu, Huan, Shamanth Kumar et Fred Morstatter. Twitter Data Analytics. Springer, 2013.
Trouver le texte intégralChatterjee, Siddhartha, et Michal Krystyanczuk. Python Social Media Analytics : Analyze and visualize data from Twitter, YouTube, GitHub, and more. Packt Publishing - ebooks Account, 2017.
Trouver le texte intégralChapitres de livres sur le sujet "Twitter data analytics"
Kumar, Shamanth, Fred Morstatter et Huan Liu. « Crawling Twitter Data ». Dans Twitter Data Analytics, 5–22. New York, NY : Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9372-3_2.
Texte intégralKumar, Shamanth, Fred Morstatter et Huan Liu. « Storing Twitter Data ». Dans Twitter Data Analytics, 23–33. New York, NY : Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9372-3_3.
Texte intégralKumar, Shamanth, Fred Morstatter et Huan Liu. « Analyzing Twitter Data ». Dans Twitter Data Analytics, 35–48. New York, NY : Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9372-3_4.
Texte intégralKumar, Shamanth, Fred Morstatter et Huan Liu. « Visualizing Twitter Data ». Dans Twitter Data Analytics, 49–69. New York, NY : Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9372-3_5.
Texte intégralKumar, Shamanth, Fred Morstatter et Huan Liu. « Introduction ». Dans Twitter Data Analytics, 1–3. New York, NY : Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9372-3_1.
Texte intégralGarg, Yogesh, et Niladri Chatterjee. « Sentiment Analysis of Twitter Feeds ». Dans Big Data Analytics, 33–52. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13820-6_3.
Texte intégralMehndiratta, Pulkit, Shelly Sachdeva, Pankaj Sachdeva et Yatin Sehgal. « Elections Again, Twitter May Help!! ! A Large Scale Study for Predicting Election Results Using Twitter ». Dans Big Data Analytics, 133–44. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13820-6_11.
Texte intégralBhargava, Mudit, Pulkit Mehndiratta et Krishna Asawa. « Stylometric Analysis for Authorship Attribution on Twitter ». Dans Big Data Analytics, 37–47. Cham : Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03689-2_3.
Texte intégralPradyumn, Mudit, Akshat Kapoor et Nasseh Tabrizi. « Big Data Analytics on Twitter ». Dans Big Data – BigData 2018, 326–33. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94301-5_26.
Texte intégralJain, Mona, S. Rajyalakshmi, Rudra M. Tripathy et Amitabha Bagchi. « Temporal Analysis of User Behavior and Topic Evolution on Twitter ». Dans Big Data Analytics, 22–36. Cham : Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03689-2_2.
Texte intégralActes de conférences sur le sujet "Twitter data analytics"
Tao, Ke, Claudia Hauff, Geert-Jan Houben, Fabian Abel et Guido Wachsmuth. « Facilitating Twitter data analytics : Platform, language and functionality ». Dans 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004259.
Texte intégralShah, Syed Attique, Sadok Ben Yahia, Keegan McBride, Akhtar Jamil et Dirk Draheim. « Twitter Streaming Data Analytics for Disaster Alerts ». Dans 2021 2nd International Informatics and Software Engineering Conference (IISEC). IEEE, 2021. http://dx.doi.org/10.1109/iisec54230.2021.9672370.
Texte intégralAlzahrani, Sabah M. « Big Data Analytics Tools : Twitter API and Spark ». Dans 2021 International Conference of Women in Data Science at Taif University (WiDSTaif ). IEEE, 2021. http://dx.doi.org/10.1109/widstaif52235.2021.9430205.
Texte intégralHurayn, Safa, Aditya M. Shetty, Srujan Vasudevrao Deshpande, Vaibhav Gupta, U. Ananthanagu et Assistant Professor. « Analysis of Mental Illness using Twitter Data ». Dans 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS). IEEE, 2021. http://dx.doi.org/10.1109/fabs52071.2021.9702565.
Texte intégralAziz, Khadija, Dounia Zaidouni et Mostafa Bellafkih. « Social Network Analytics : Natural Disaster Analysis Through Twitter ». Dans 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 2019. http://dx.doi.org/10.1109/icds47004.2019.8942337.
Texte intégralHao, Ming, Christian Rohrdantz, Halldor Janetzko, Umeshwar Dayal, Daniel A. Keim, Lars-Erik Haug et Mei-Chun Hsu. « Visual sentiment analysis on twitter data streams ». Dans 2011 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2011. http://dx.doi.org/10.1109/vast.2011.6102472.
Texte intégralPopović, Miloš, et Milan Milosavljević. « Twitter Data Analytics in Education Using IBM Infosphere Biginsights ». Dans Sinteza 2016. Belgrade, Serbia : Singidunum University, 2016. http://dx.doi.org/10.15308/sinteza-2016-74-80.
Texte intégralCheng, Daniel, Peter Schretlen, Nathan Kronenfeld, Neil Bozowsky et William Wright. « Tile based visual analytics for Twitter big data exploratory analysis ». Dans 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691787.
Texte intégralYadranjiaghdam, Babak, Seyedfaraz Yasrobi et Nasseh Tabrizi. « Developing a Real-Time Data Analytics Framework for Twitter Streaming Data ». Dans 2017 IEEE International Congress on Big Data (BigData Congress). IEEE, 2017. http://dx.doi.org/10.1109/bigdatacongress.2017.49.
Texte intégralPrema Arokia Mary, G., M. S. Hema, R. Maheshprabhu et M. Nageswara Guptha. « Sentimental Analysis of Twitter Data using Machine Learning Algorithms ». Dans 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS). IEEE, 2021. http://dx.doi.org/10.1109/fabs52071.2021.9702681.
Texte intégral