Relevant bibliographies by topics / Twitter stream analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Twitter stream analysis'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "Twitter stream analysis"

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D'Andrea, Eleonora, Pietro Ducange, Beatrice Lazzerini, and Francesco Marcelloni. "Real-Time Detection of Traffic From Twitter Stream Analysis." IEEE Transactions on Intelligent Transportation Systems 16, no. 4 (2015): 2269–83. http://dx.doi.org/10.1109/tits.2015.2404431.

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Srivastava, Ritesh, and M. P. S. Bhatia. "Real-Time Unspecified Major Sub-Events Detection in the Twitter Data Stream That Cause the Change in the Sentiment Score of the Targeted Event." International Journal of Information Technology and Web Engineering 12, no. 4 (2017): 1–21. http://dx.doi.org/10.4018/ijitwe.2017100101.

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Twitter behaves as a social sensor of the world. The tweets provided by the Twitter Firehose reveal the properties of big data (i.e. volume, variety, and velocity). With millions of users on Twitter, the Twitter's virtual communities are now replicating the real-world communities. Consequently, the discussions of real world events are also very often on Twitter. This work has performed the real-time analysis of the tweets related to a targeted event (e.g. election) to identify those potential sub-events that occurred in the real world, discussed over Twitter and cause the significant change in the aggregated sentiment score of the targeted event with time. Such type of analysis can enrich the real-time decision-making ability of the event bearer. The proposed approach utilizes a three-step process: (1) Real-time sentiment analysis of tweets (2) Application of Bayesian Change Points Detection to determine the sentiment change points (3) Major sub-events detection that have influenced the sentiment of targeted event. This work has experimented on Twitter data of Delhi Election 2015.
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Anbu Durai, Srinaath. "Resale HDB Price Prediction Considering Covid-19 through Sentiment Analysis." European Conference on Social Media 10, no. 1 (2023): 276–85. http://dx.doi.org/10.34190/ecsm.10.1.1020.

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon-based tool, VADER, is used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid-19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. In a micro sense, this paper demonstrates the use of sentiment analysis of Twitter data in urban economics. In a macro sense, the paper demonstrates the extent to which social media is able to capture the behavioral economic cues of a population.
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Smetanin, Sergey. "RuSentiTweet: a sentiment analysis dataset of general domain tweets in Russian." PeerJ Computer Science 8 (July 19, 2022): e1039. http://dx.doi.org/10.7717/peerj-cs.1039.

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The Russian language is still not as well-resourced as English, especially in the field of sentiment analysis of Twitter content. Though several sentiment analysis datasets of tweets in Russia exist, they all are either automatically annotated or manually annotated by one annotator. Thus, there is no inter-annotator agreement, or annotation may be focused on a specific domain. In this article, we present RuSentiTweet, a new sentiment analysis dataset of general domain tweets in Russian. RuSentiTweet is currently the largest in its class for Russian, with 13,392 tweets manually annotated with moderate inter-rater agreement into five classes: Positive, Neutral, Negative, Speech Act, and Skip. As a source of data, we used Twitter Stream Grab, a historical collection of tweets obtained from the general Twitter API stream, which provides a 1% sample of the public tweets. Additionally, we released a RuBERT-based sentiment classification model that achieved F1 = 0.6594 on the test subset.
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Rasul, Hakar Mohammed, and Alaa Khalil Jumaa. "Real-Time Twitter Data Analysis: A Survey." UHD Journal of Science and Technology 6, no. 2 (2022): 147–55. http://dx.doi.org/10.21928/uhdjst.v6n2y2022.pp147-155.

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Internet users are used to a steady stream of facts in the contemporary world. Numerous social media platforms, including Twitter, Facebook, and Quora, are plagued with spam accounts, posing a significant problem. These accounts are created to trick unwary real users into clicking on dangerous links or to continue publishing repetitious messages using automated software. This may significantly affect the user experiences on these websites. Effective methods for detecting certain types of spam have been intensively researched and developed. Effectively resolving this issue might be aided by doing sentiment analysis on these postings. Hence, this research provides a background study on Twitter data analysis, and surveys existing papers on Twitter sentiment analysis and fake account detection and classification. The investigation is restricted to the identification of social bots on the Twitter social media network. It examines the methodologies, classifiers, and detection accuracies of the several detection strategies now in use.
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Sarawagi, Ankit, Rajeev Pandey, Raju Barskar, and S. P. "A Real Time Stream Data Processing and Analysis Model and Catchments over Twitter Stream Data." International Journal of Computer Applications 179, no. 1 (2017): 22–33. http://dx.doi.org/10.5120/ijca2017915663.

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Mamo, Nicholas, Joel Azzopardi, and Colin Layfield. "An Automatic Participant Detection Framework for Event Tracking on Twitter." Algorithms 14, no. 3 (2021): 92. http://dx.doi.org/10.3390/a14030092.

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Topic Detection and Tracking (TDT) on Twitter emulates human identifying developments in events from a stream of tweets, but while event participants are important for humans to understand what happens during events, machines have no knowledge of them. Our evaluation on football matches and basketball games shows that identifying event participants from tweets is a difficult problem exacerbated by Twitter’s noise and bias. As a result, traditional Named Entity Recognition (NER) approaches struggle to identify participants from the pre-event Twitter stream. To overcome these challenges, we describe Automatic Participant Detection (APD) to detect an event’s participants before the event starts and improve the machine understanding of events. We propose a six-step framework to identify participants and present our implementation, which combines information from Twitter’s pre-event stream and Wikipedia. In spite of the difficulties associated with Twitter and NER in the challenging context of events, our approach manages to restrict noise and consistently detects the majority of the participants. By empowering machines with some of the knowledge that humans have about events, APD lays the foundation not just for improved TDT systems, but also for a future where machines can model and mine events for themselves.
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Rahmat, Al Fauzi, and M. Rafi. "Social Media Network Analysis on Twitter Users Network to the Pension Plan Policy." Communicare : Journal of Communication Studies 8, no. 1 (2022): 62. http://dx.doi.org/10.37535/101009120225.

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This article scrutinizes the network of Twitter users on the dissemination of tweets on the Old-Age Guarantee policy in Indonesia. A qualitative method with social media network analysis approach was used. Then, data sources were obtained from Twitter social media through #JHT_JokowiHarusTurun, #jaminanharitua, and #JHT. Furthermore, to manage data source, NVivo 12 plus software was used to analyze qualitative data from Twitter social media – including dissemination rate of tweets, followed by geographical map tweet stream, Twitter user’ network pattern, sentiment proportion, as well as words frequencies. Our results indicate that there are networks found from Twitter users with some account backgrounds in politicians, political parties, governments, online news media, actors and also cultural practitioners who participate in disseminating tweets. Even this network generated the significant distribution patterns and sentiments to a moderately negative value, coupled with pleasantries that are echoed between protests and support by words cloud to this movement. Overall, our research contributes to better understanding of how social media-promoted collective protest movements have the power to impact public opinion and policy and that their evolution is unexpected.
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Domade, Ashwini S. "Twitter sentiment Analysis Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41623.

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abstract on page 1 With the development and expansion of web technology, a vast amount of data is generated and available to internet users, and the internet has evolved into a platform for online learning, idea exchange, and opinion sharing. Because they enable people to share and express their opinions on various topics, engage in discussions with various communities, or post messages globally, social networking sites like Facebook, Google, and Twitter are quickly becoming more and more popular. A lot of work has been done in the field of sentiment analysis of Twitter data, which is useful for analyzing the information in tweets where opinions are highly unstructured and heterogeneous and are either This paper presents a survey and comparative analyses of current techniques for opinion mining, such as machine learning and lexicon-based approaches, along with evaluation metrics using various machine learning algorithms, such as naive bayes max entropy and support vector machine. Given the growth and advancement of web technology, there is a huge volume of data available on the internet for internet users, and a lot of data is generated too. In some cases, this data is neutral, positive, or negative. We offer data stream research on Twitter. Additionally, we talked about the basic difficulties and uses of sentiment analysis on Twitter keywords. Opinion mining machine learning using sentiment analysis on Twitter Maximum Entropy Support Vector Machine (SVM) naïve Bayes NB Key Words: Twitter, Sentiment analysis (SA), Opinion mining, Machine learning, Naive Bayes (NB), Maximum Entropy, Support Vector Machine (SVM).
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Kim, Erin Hea-Jin, Yoo Kyung Jeong, Yuyoung Kim, Keun Young Kang, and Min Song. "Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news." Journal of Information Science 42, no. 6 (2016): 763–81. http://dx.doi.org/10.1177/0165551515608733.

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The present study investigates topic coverage and sentiment dynamics of two different media sources, Twitter and news publications, on the hot health issue of Ebola. We conduct content and sentiment analysis by: (1) applying vocabulary control to collected datasets; (2) employing the n-gram LDA topic modeling technique; (3) adopting entity extraction and entity network; and (4) introducing the concept of topic-based sentiment scores. With the query term ‘Ebola’ or ‘Ebola virus’, we collected 16,189 news articles from 1006 different publications and 7,106,297 tweets with the Twitter stream API. The experiments indicate that topic coverage of Twitter is narrower and more blurry than that of the news media. In terms of sentiment dynamics, the life span and variance of sentiment on Twitter is shorter and smaller than in the news. In addition, we observe that news articles focus more on event-related entities such as person, organization and location, whereas Twitter covers more time-oriented entities. Based on the results, we report on the characteristics of Twitter and news media as two distinct news outlets in terms of content coverage and sentiment dynamics.
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Dissertations / Theses on the topic "Twitter stream analysis"

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RezaeiDivkolaei, Pouya. "DETECTION, CLASSIFICATION, AND LOCATION IDENTIFICATION OF TRAFFIC CONGESTION FROM TWITTER STREAM ANALYSIS." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2257.

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Social media today is an important source of information about various events happening around the world. Among various social networking platforms, microtext based ones such as Twitter are of special interest as they are also a rich source of real-time events. In this thesis, our goal is to study the effectiveness of using Twitter as a social sensor for obtaining real-time information on road traffic conditions. Specifically, we focus on: i) identifying tweets that contain traffic event related information, ii) classify such tweets into six main groups of accident, fire, road construction, police activities, weather and others, iii) extract fine-grained location information about the traffic incident by analyzing tweet text. Our experimental results show that using Twitter as a social sensor for obtaining rich information about traffic events is indeed a promising approach. We show that we can correctly detect traffic related tweets with an accuracy of 81%. Moreover, the accuracy of correctly classifying traffic related tweets into one of the six categories is 97%. Lastly, our experimental results show that using only geo-tags of tweets is not sufficient for fine-grained localization of traffic incidents due to two reasons: i) a vast majority of traffic related tweets do not contain geo-tags, and ii) the location mentioned in the tweet text and the geo-tag of a tweet do not always agree. Such observations prove that fine-grained localization of traffic incidents from tweet must also include analysis of the tweet text using Natural Language Processing techniques.
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Santos, Augusto Dias Pereira dos. "Descobrindo eventos locais utilizando análise de séries temporais nos dados do Twitter." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/71953.

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O crescente uso de redes sociais gera quantidades enormes de dados que podem ser empregados em vários tipos de análises. Alguns desses dados têm informação temporal e geográfica, as quais podem ser usadas para posicionar precisamente a informação no tempo e no espaço. Nesse contexto, neste trabalho é proposto um novo método para a análise do volume massivo de mensagens disponível no Twitter, com o objetivo de identificar eventos como programas de TV, mudanças climáticas, desastres e eventos esportivos que estejam ocorrendo em regiões específicas do globo. A abordagem proposta é baseada no uso de uma rede neural para detecção de outliers em séries temporais, as quais são formadas por estatísticas coletadas em tweets localizados em diferentes divisões políticas (i.e., países, cidades). Esses outliers são usados para identificar eventos como um comportamento anormal nos dados Twitter. A efetividade do método é avaliada comparando os eventos identificados com notícias nos meios de comunicação.<br>The increasing use of social networks generates enormous amounts of data that can be employed for various types of analysis. Some of these data have temporal and geographical information, which can be used to precisely position information in time and space. In this document, a new method is proposed to analyze the massive volume of messages available in Twitter to identify events such as TV shows, climate change, disasters, and sports that are occurring in specific regions of the globe. The proposed approach is based on a neural network used to detect outliers from a time series, which is built upon statistical data from tweets located in different political divisions (i.e., countries, cities). These outliers are used to identify events as an abnormal behavior in Twitter's data. The effectiveness of the method is evaluated by comparing the events identified on the news media.
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Balasuriya, Lakshika. "Finding Street Gang Member Profiles on Twitter." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1516054679956178.

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Song, Le. "Multimodal Interactional Practices in Live Streams on Twitter." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT019.

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En tant que forme émergente d'interaction médiatisée, la diffusion en direct (live streaming) est devenue une pratique en pleine expansion qui combine les caractéristiques techniques et interactionnelles de l'interaction vidéo-médiatisée et du chat multi-participants. Le live streaming à l'aide d'appareils mobiles sur plusieurs plateformes est donc une pratique dans laquelle les diffuseurs (streamers) et les spectateurs interagissent sous des formes hautement asymétriques: l'affichage vidéo du diffuseur et le texte écrit du spectateur. Cette thèse de doctorat s'intéresse au live streaming en tant que phénomène interactionnel d'un point de vue séquentiel. S'appuyant sur des données vidéo enregistrées d'activités advenant naturellement dans des live streams orientés vers la vie quotidienne sur Twitter (maintenant ‘X') et sur l'ethnométhodologie et l'analyse conversationnelle (EMCA) comme perspective théorique et méthodologique, la thèse explore comment l'utilisation de multiples ressources (par exemple, parlées, écrites et corporelles), ainsi que la manipulation des ‘affordances' des appareils permettent de produire les cadres de participation propres aux live streams et l'accomplissement de différentes actions conjointes de manière séquentielle. La dissertation se compose de quatre articles de recherche principaux, traitant quatre phénomènes interactionnels caractéristiques des live streams. Le premier analyse les séquences d'ouverture des live streams. Contrairement aux conversations téléphoniques et leur séquence ‘canonique' d'ouverture, les ouvertures de live streams apparaissent plus variables, avec de multiples cadres de participation stratifiés, bien qu'il y ait généralement une phase d'installation reconnaissable où l'activité de diffusion commence. La thèse identifie des préoccupations interactionnelles spécifiques aux ouvertures, à savoir l'attente d'un public adéquat, par les streamers, la manière dont ceux-ci gèrent les interactions avec le public à la fois dans son ensemble, et de manière individuelle dans le cadre d'une relation invité/hôte, et les préoccupations affichées par les participants concernant l'intelligibilité immédiate du stream. L'article II discute de la manière dont les diffuseurs et les spectateurs démontrent attention et engagement en formulant ce que les streams rendent remarquables. Il examine ainsi comment les streamers et les spectateurs produisent des des séquences initiées par des remarques (noticings), et comment l'affinité particulière des live streams avec cette pratique à la fois attentionnelle et interactionnelle peut conduire à une 'effervescence attentionnelle' caractéristique. L'article III inspecte l'activité de dégustation dans le live streaming, où la dégustation est accomplie comme un processus interactif et multimodal qui combine l'expérience sensorielle individuelle avec une dimension publique, et intersubjective. L'article IV enquête les séquences de clôture dans le live streaming. Nous montrons comment les participants s'y orientent vers l'organisation des clôtures caractéristique de la conversation ordinaire, mais d'une manière très sensible aux affordances des diffusions vidéo en direct. La thèse fournit donc une analyse systématique des propriétés interactionnelles les plus caractéristiques du live streaming<br>As an emerging form of mediated interaction, live streaming has become a rapidly growing practice that combines the technical and interactional features of video-mediated interaction and multi-party chat. Live streaming with mobile devices on multiple platforms has thus been a practice in which streamers and viewers interact in highly asymmetric forms—the streamer's video display and the viewer's written text. This doctoral dissertation focuses on live streams as interactional phenomena from a sequential perspective. Drawing on video-recorded data from ordinary users' naturally unfolding activities in daily life-oriented live streams on Twitter (now ‘X') and taking ethnomethodology and conversation analysis (EMCA) as its theoretical and methodological perspective, the thesis explores how the use of multiple (e.g., spoken, written and embodied) resources, as well as the manipulation of affordance of the devices in establishing the participation framework of live streaming interactions and achieving different joint actions stepwise. The dissertation consists of four main research articles, each focusing on a typical interactional phenomenon in live streaming. All of the articles have been published or are under review. Article I investigates the openings of live streaming. Unlike phone conversations with a canonical opening sequence, live stream openings appear more variable, with laminated participation frames, although there is usually a recognizable "installation" phase where the stream activity begins. We also identified interactional concerns in the opening, that is, the streamers' wait for an adequate audience, their collective and individual management of viewers within a guest/host relationship, and the concern of participants regarding the immediate intelligibility of the stream. Article II discusses how streamers and viewers manage attention and engagement through noticing-based actions. It looks at how streamers and viewers produce noticing sequences and noticing-based sequences, and how the orientation towards noticing may lead to a distinctive form of ‘noticing effervescence.' Article III inspects the activity of tasting in live streaming, re-examining tasting in this particular ecology as an interactive process that combines individual sensory experience with a public, witnessable, and intersubjective dimension. Article IV investigates the organization of closing sequences in live streaming. It shows that while participants can be seen to orient to the sequential organization of closings in ordinary conversation, they do so in a way that is particularly sensitive to the affordances of live video streams. The thesis thus provides a systematic analysis of the most characteristic interactional properties of live streaming
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Haldenwang, Nils. "Reliable General Purpose Sentiment Analysis of the Public Twitter Stream." Doctoral thesis, 2017. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017092716282.

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General purpose Twitter sentiment analysis is a novel field that is closely related to traditional Twitter sentiment analysis but slightly differs in some key aspects. The main difference lies in the fact that the novel approach considers the unfiltered public Twitter stream while most of the previous approaches often applied various filtering steps which are not feasible for many applications. Another goal is to yield more reliable results by only classifying a tweet as positive or negative if it distinctly consists of the respective sentiment and mark the remaining messages as uncertain. Traditional approaches are often not that strict. Within the course of this thesis it could be verified that the novel approach differs significantly from the traditional approach. Moreover, the experimental results indicated that the archetypical approaches could be transferred to the new domain but the related domain data is consistently sub par when compared to high quality in-domain data. Finally, the viability of the best classification algorithm could be qualitatively verified in a real-world setting that was also developed within the course of this thesis.
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Books on the topic "Twitter stream analysis"

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Elzankalony, Mohamed, Mahmoud Hanafy, and Islam Khalil. Stock Market Social Network Analysis: Social Analysis for Twitter/Stocktwits Stream. Independently Published, 2020.

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Book chapters on the topic "Twitter stream analysis"

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Afzaal, Maryam, Nazifa Nazir, Khadija Akbar, et al. "Real Time Traffic Incident Detection by Using Twitter Stream Analysis." In Human Systems Engineering and Design. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02053-8_95.

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Arora, Shruti, and Rinkle Rani. "A Novel Framework for Distributed Stream Processing and Analysis of Twitter Data." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5113-0_11.

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Kiliroor, Cinu C., and C. Valliyammai. "Binary and Continuous Feature Engineering Analysis on Twitter Data Stream for Classification of Spam Messages." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0829-5_55.

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Ibrahim, Rania, Ahmed Elbagoury, Khaled Ammar, Mohamed S. Kamel, and Fakhri Karray. "Real-Time Detection of Topics in Twitter Streams." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_110157-1.

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Ibrahim, Rania, Ahmed Elbagoury, Khaled Ammar, Mohamed S. Kamel, and Fakhri Karray. "Real-Time Detection of Topics in Twitter Streams." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_110157.

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Liang, Yuzhi, Pengcheng Yin, and S. M. Yiu. "New Word Detection and Tagging on Chinese Twitter Stream." In Big Data Analytics and Knowledge Discovery. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22729-0_24.

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D’Auria, Luca, and Vincenzo Convertito. "Real-Time Mapping of Earthquake Perception Areas in the Italian Region from Twitter Streams Analysis." In Earthquakes and Their Impact on Society. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21753-6_26.

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Luber, Mattias, Christoph Weisser, Benjamin Säfken, Alexander Silbersdorff, Thomas Kneib, and Krisztina Kis-Katos. "Identifying Topical Shifts in Twitter Streams: An Integration of Non-negative Matrix Factorisation, Sentiment Analysis and Structural Break Models for Large Scale Data." In Disinformation in Open Online Media. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87031-7_3.

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De Falco, Ciro Clemente, Noemi Crescentini, and Marco Ferracci. "The Spatial Dimension in Social Media Analysis." In Handbook of Research on Advanced Research Methodologies for a Digital Society. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8473-6.ch029.

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In the data revolution era, the availability of “voluntary” and “derived from social media” geographic information allowed the spatial dimension to gain attention in digital and web studies. The purpose of this work is to recognize the impact of this research stream on some methodological and theoretical issues. The first regards “critical algorithm studies” in order to understand what algorithms are used. The second concerns how these works conceive the space. The last two issues concern the disciplinary areas in which these researches take place and which are the ecological units taken into account. The authors answer these questions by analyzing, through a content analysis, the researches extracted with the PRISMA methodology that have used Twitter as a data source. The application of this procedure allows the authors to classify the analysis material, moving simultaneously on the four defined dimensions.
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Priya, Kani, Krishnaveni R., Krishnamurthy M., and Bairavel S. "Analyzing Social Emotions in Social Network Using Graph Based Co-Ranking Algorithm." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch018.

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Twitter has become exceedingly popular, with hundreds of millions of tweets being posted every day on a wide variety of topics. This has helped make real-time search applications possible with leading search engines routinely displaying relevant tweets in response to user queries. Recent research has shown that a considerable fraction of these tweets are about “events,” and the detection of novel events in the tweet-stream has attracted a lot of research interest. However, very little research has focused on properly displaying this real-time information about events. For instance, the leading search engines simply display all tweets matching the queries in reverse chronological order. Online content exhibits rich temporal dynamics, and diverse real-time user generated content further intensifies this process. However, temporal patterns by which online content grows and fades over time, and by which different pieces of content compete for attention remain largely unexplored. This article describes tracking and analyzing public sentiment on social networks and finding the possible reasons causing these variations. It is important to find the decision from public views and opinion in different domain. They can be used to discover special topics or aspects in one text collection in comparison with another background text collection. The implemented method attains the 95% accuracy while predict the sentiments from the social websites and the 96.3% of the opinion rate with minimum time.
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Conference papers on the topic "Twitter stream analysis"

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Kumar, Praveen, Tanupriya Choudhury, Seema Rawat, and Shobhna Jayaraman. "Expression of Concern for: Analysis of Various Machine Learning Algorithms for Enhanced Opinion Mining Using Twitter Data Streams." In 2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE). IEEE, 2016. http://dx.doi.org/10.1109/icmete38202.2016.10702624.

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R-Moreno, María D., Álvaro Cuesta, and David F. Barrero. "Twitter stream analysis in Spanish." In the 3rd International Conference. ACM Press, 2013. http://dx.doi.org/10.1145/2479787.2479819.

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Lancieri, Luigi, and Romain Giovanetti. "Multilevel exploration in Twitter social stream." In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2016. http://dx.doi.org/10.1109/asonam.2016.7752317.

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Rehman, N. U., S. Mansmann, A. Weiler, and M. H. Scholl. "Building a Data Warehouse for Twitter Stream Exploration." In 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). IEEE, 2012. http://dx.doi.org/10.1109/asonam.2012.230.

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Lyebyedyev, Yehor, and Mykola Makhortykh. "#Euromaidan: Quantitative Analysis of Multilingual Framing 2013–2014 Ukrainian Protests on Twitter." In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2018. http://dx.doi.org/10.1109/dsmp.2018.8478462.

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Hakdagli, Ozlem, Caner Ozcan, and Iskender Ulgen Ogul. "Stream text data analysis on twitter using apache spark streaming." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404540.

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Gokulakrishnan, Balakrishnan, Pavalanathan Priyanthan, Thiruchittampalam Ragavan, Nadarajah Prasath, and AShehan Perera. "Opinion mining and sentiment analysis on a Twitter data stream." In 2012 International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 2012. http://dx.doi.org/10.1109/icter.2012.6423033.

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Wadera, Mohit, Mukul Mathur, and Dinesh Kumar Vishwakarma. "Sentiment Analysis of Tweets- A Comparison of Classifiers on Live Stream of Twitter." In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2020. http://dx.doi.org/10.1109/iciccs48265.2020.9121166.

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Hao, Ming, Christian Rohrdantz, Halldor Janetzko, et al. "Visual sentiment analysis on twitter data streams." In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2011. http://dx.doi.org/10.1109/vast.2011.6102472.

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Balasuriya, Lakshika, Sanjaya Wijeratne, Derek Doran, and Amit Sheth. "Finding street gang members on Twitter." In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2016. http://dx.doi.org/10.1109/asonam.2016.7752311.

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