Combination of singular value decomposition and K-means clustering methods for topic detection on Twitter

Nur'aini, Khumaisa; Najahaty, Ibtisami; Hidayati, Lina Nur; Murfi, Hendri; Nurrohmah, Siti

doi:10.1109/icacsis.2015.7415168

Cited by 44 publications

(20 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Probabilistic Model -Latent Dirichlet Allocation (LDA) [18] • Clustering -Document-pivot (Doc-p) [19], BN-gram [11] • Frequent Pattern Mining -Soft Frequent Pattern Mining (SFPM) [11] • Matrix Factorization -SVD-KMean [20], SNMF-Orig, SNMF-KL [17] • Exemplar-Based -Exemplar [14] • Graph-based -Graph-based Feature-pivot (GFeat-p) [21] For the FA Cup dataset, Figure 7 shows the results for T-Recall at K = {2, 4, 6, . .…”

Section: Resultsmentioning

confidence: 99%

Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes]

Saeed

Abbasi

Razzak

et al. 2019

IEEE Comput. Intell. Mag.

View full text Add to dashboard Cite

Tweets about everyday events are published on Twitter. Detecting such events is a challenging task due to the diverse and noisy contents of Twitter. In this paper, we propose a novel approach named Weighted Dynamic Heartbeat Graph (WDHG) to detect events from the Twitter stream. Once an event is detected in a Twitter stream, WDHG suppresses it in later stages, in order to detect new emerging events. This unique characteristic makes the proposed approach sensitive to capture emerging events efficiently. Experiments are performed on three real-life benchmark datasets: FA Cup Final 2012, Super Tuesday 2012, and the US Elections 2012. Results show considerable improvement over existing event detection methods in most cases.

show abstract

Section: Resultsmentioning

confidence: 99%

Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes]

Saeed

Abbasi

Razzak

et al. 2019

IEEE Comput. Intell. Mag.

View full text Add to dashboard Cite

show abstract

“…Clustering By considering the dynamicity and unpredictability of social media data streams, there has been a tendency to use clustering for event detection. McCreadie et al (2013) andNur'Aini et al (2015) showed that K-means clustering can be successfully used to extract events. In order to improve efficiency and effectiveness, they clustered low dimensional document vectors, which were generated using Locality Sensitive Hashing (LSH) and Singular Value Decomposition (SVD), respectively.…”

Section: Related Workmentioning

confidence: 99%

“…As token representations, self-learned word embeddings are used while preserving syntactical and semantical features of the underlying corpus. According to previous research, document clustering approaches were more popularly used with event detection (Nur'Aini et al 2015;Nguyen et al 2019;Comito et al 2019a, b). However, with the recent increments made to character limits by social media services (e.g.…”

Section: Cluster Change Calculationmentioning

confidence: 99%

Embed2Detect: temporally clustered embedded words for event detection in social media

et al. 2021

View full text Add to dashboard Cite

Social media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

show abstract

“…For k-means, the singular value decomposition (SVD) is often used for dimensionality reduction [29]- [31]. The tandem of SVD and k-means clustering was applied to network applications [29], [32], in medical imaging [33] or FC analysis [20] and thoroughly studied in gene expression [30]. The denoising properties of the SVD are well understood, limiting the noise to the reduced signal subspace only [34].…”

Section: Related Work On Dimentionality Reductionmentioning

confidence: 99%

Multiplex Network Inference With Sparse Tensor Decomposition for Functional Connectivity

Frusque

Jung

Borgnat

et al. 2020

IEEE Trans. on Signal and Inf. Process. over Networks

View full text Add to dashboard Cite

Functional connectivity (FC) is a graphlike data structure commonly used by neuroscientists to study the dynamic behaviour of brain activity. However, these analyses rapidly become complex and timeconsuming, since the number of connectivity components to be studied is quadratic with the number of electrodes. In this work, we address the problem of clustering FC into relevant ensembles of simultaneously activated components, yielding a multiplex network that reveals characteristic patterns of the epileptic seizures of a given patient. While k−means is certainly the most popular method for data clustering, it is known to perform poorly on large dimensional data sets, and to be highly sensitive to noise. To overcome the socalled curse of dimensionality, we propose a new tensor decomposition to reduce the size of the data set formed by FC time-series recorded for several seizures, prior to apply k-means. We propose an adapted procedure to infer a multiplex network from several FC time series, and we emphasise one particular variant that imposes sparsity constraint. Then, we conduct a real case study, applying the proposed sparse tensor decomposition to iEEG data to infer a multiplex network corresponding to the different stages of an epileptic seizure.

show abstract

Combination of singular value decomposition and K-means clustering methods for topic detection on Twitter

Cited by 44 publications

References 7 publications

Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes]

Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes]

Embed2Detect: temporally clustered embedded words for event detection in social media

Multiplex Network Inference With Sparse Tensor Decomposition for Functional Connectivity

Contact Info

Product

Resources

About