Identifying Concept-drift in Twitter Streams

Lifna, C. S.; Vijayalakshmi, M.

doi:10.1016/j.procs.2015.03.093

Cited by 17 publications

(10 citation statements)

References 6 publications

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“…For intuition 2), let's assume there is a comment node i in the discussion tree t. Users can choose any comment to reply in t, but if i is chosen, it indicates that the topics in comment node i attract the users more than other nodes. The newly added child node to i continue the topics discussed in i, making topic transitive from i to its children, but the topic shift [32,38] and the topic drift [16,25] phenomenon make the transitivity process with some "loss", so the "topic influence" of a root decreases when the discussion thread gets longer. In Fig.…”

Section: A Topic Generation With Popularitymentioning

confidence: 99%

Conversational Structure Aware and Context Sensitive Topic Model for Online Discussions

Sun

Loparo

Kolacinski

2020

2020 IEEE 14th International Conference on Semantic Computing (ICSC)

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Millions of online discussions are generated everyday on social media platforms. Topic modelling is an efficient way of better understanding large text datasets at scale. Conventional topic models have had limited success in online discussions, and to overcome their limitations, we use the discussion thread tree structure and propose a "popularity" metric to quantify the number of replies to a comment to extend the frequency of word occurrences, and the "transitivity" concept to characterize topic dependency among nodes in a nested discussion thread. We build a Conversational Structure Aware Topic Model (CSATM) based on popularity and transitivity to infer topics and their assignments to comments. Experiments on real forum datasets are used to demonstrate improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.

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Section: A Topic Generation With Popularitymentioning

confidence: 99%

Conversational Structure Aware and Context Sensitive Topic Model for Online Discussions

Sun

Loparo

Kolacinski

2020

2020 IEEE 14th International Conference on Semantic Computing (ICSC)

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“…Some examples where a data drift may occur in smart cities are related to the replacement of sensors (different calibration), sensor wear and tear [41] or drastic changes to the topics of discussion in social media used for crowdsensing [42].…”

Section: Motivation and Contributionsmentioning

confidence: 99%

BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation

González-Vidal

Barnaghi

Skarmeta

2018

IEEE Trans. Knowl. Data Eng.

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Abstract-The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the observations in the time series analysis domain. The IoT time series require aggregation methods that can preserve and represent the key characteristics of the data. In this paper, we propose a segmentation algorithm that adapts to unannounced mutations of the data (i.e. data drifts). The algorithm splits the data streams into blocks and groups them in square matrices, computes the Discrete Cosine Transform (DCT) and quantizes them. The key information is contained in the upper-left part of the resulting matrix. We extract this sub-matrix, compute the modulus of its eigenvalues and remove duplicates. The algorithm, called BEATS, is designed to tackle dynamic IoT streams, whose distribution changes over time. We implement experiments with six datasets combining real, synthetic, real-world data, and data with drifts. Compared to other segmentation methods like Symbolic Aggregate approXimation (SAX), BEATS shows significant improvements. Trying it with classification and clustering algorithms it provides efficient results. BEATS is an effective mechanism to work with dynamic and multi-variate data, making it suitable for IoT data sources. The datasets, code of the algorithm and the analysis results can be accessed publicly at: https://github.com/auroragonzalez/BEATS.

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“…Text explosion on the web is spreading in variety of forms and this gigantic growth of technology is pumping a huge amount of data in social media. Various schemes and training mechanism are available to share and communicate with others which deal with the repetition of text instead of dealing with text only [26]. Micro-blogging is a common platform for all online users and has become very frequent in past few years [27].…”

Section: Introductionmentioning

confidence: 99%