Sentiment Knowledge Discovery in Twitter Streaming Data

By Albert Bifet, Eibe Frank. IFIP Working Conference on Database Semantics, 2010

659 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f234c3aab4e3817cafa44051d7bd1aebc9cd394b

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Citations in the corpus

Large Scale Sentiment Learning with Limited Labels (2017, methodology): “…same experiment with a sliding window of three months; for example, we used the labeled instances of months [1-3] for building an initial model, we expand the training set including predictions for unlabeled instances in months [1-3] and we use the derived model to score the next month, month 4.”
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “However, one area that remains largely unexplored is the effect of demographic language variation on subjective language use, and whether these differences may be exploited for automatic sentiment analysis.”
Global Belief Recursive Neural Networks (2014, background): “Sentiment analysis has been the subject of research for some time [4, 2, 3, 6, 1, 23].”
Rumor has it: Identifying Misinformation in Microblogs (2011, background): “With its nearly constant update of new posts and public API, Twitter can be a useful source for collecting data to be used in exploring a number of problems related to natural language processing and information diffusion (Bifet and Frank, 2010).”
Learning in Nonstationary Environments: A Survey (2015, background): “The most general form of a nonstationary environment typically involves streaming or continuously arriving data from time-series applications [35], such as tweet classification [36], or genomics [37], [38], etc.”
A survey on concept drift adaptation (2014, methodology): “Examples of text stream applications include e-mail classification [Carmona-Cejudo et al. 2010], e-mail spam detection [Lindstrom et al. 2010] and sentiment classification [Bifet and Frank 2010].”
A survey of the applications of text mining in financial domain (2016, background): “Various Studies of Phishing Detection Study Dataset Model Performance Measure Abdelhamid et al. (2014) Millersmiles/ PhishTank Associative Rule Accuracy Chen et al. (2011) Millersmiles DT/ NN/ SVM Accuracy He et al. (2011) Millersmiles\ PhishTank\ 3Sharp SVM True Positive/ False Positive Aburrous et”
Towards a Big Data Analytics Framework for IoT and Smart City Applications (2015, methodology): “For addressing the challenge of evaluating models in data streams where the data distribution changes constantly, a sliding window kappa-based measure was proposed [ 6].”
Sentiment in Twitter events (2011, methodology): “In practice, algorithms often employ multiple methods together with various refinements, such as prefiltering the features searched for (Riloff, Patwardhan, & Wiebe, 2006), and methods to cope with changes in data over time (Bifet & Frank, 2010).”
Like It or Not (2016, background): “Bifet and Frank [2010] proposed a data-stream mining approach that could follow the changes of class distributions and allowed sentiment analysis in real time.”
Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-Annotated Tweets (2015, methodology): “This idea has been widely used before to train message-level sentiment classifiers [Bifet and Frank, 2010], [Go et al., 2009].”
Online Machine Learning in Big Data Streams (2018, background): “Examples of such applications include financial applications [241], network monitoring [18, 1], security, sensor networks [66], Twitter analysis [25], and more [83].”
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, neutral): “…in real-time for the occurrences of major events, the outbreak of news stories, and the reactions of the users to events (Benhardus & Kalita, 2013; Bifet & Frank, 2010; Hsieh, Moghbel, Fang, & Cho, 2013; Mathioudakis & Koudas, 2010; Nav-eed, Gottron, Kunegis, & Alhadi, 2011; Petrovic, Osborne, &…”
Survey on mining subjective data on the web (2011, background): “Bifet and Frank (2010) studied the problem of using an adaptable classifier with Twitter data and examined relevant evaluation methods.”
Sentiment Analysis of Twitter Data : A Survey of Techniques (2016, methodology): “Bifet and Frank(2010) [5] used Twitter streaming data provided by Firehouse API , which gave all messages from every user which are publicly available in real-time.”
Ensemble learning for data stream analysis: A survey (2017, background): “switches new incoming examples between copies of classifiers – some of them use it for updating while others for testing [14].”
Sentiment Analysis on Twitter (2012, background): “…analysis on Twitter posts is the next step in the field of sentiment analysis, as tweets give us a richer and more varied resource of opinions and sentiments that can be about anything from the latest phone they bought, movie they watched, political issues, religious views or the individuals…”
Contextual semantics for sentiment analysis of Twitter (2016, background): “…(MaxEnt), and Support Vector Machines (SVMs), from various combinations of features, such as word n-grams (Go et al., 2009; Pak and Paroubek, 2010; Bifet and Frank, 2010), Part-Of-Speech (POS) tags (Barbosa and Feng, 2010; Agarwal et al., 2011) with or without words’ prior sentiment, words’…”