A novel method for clustering tweets in Twitter

Poomagal, S.; Visalakshi, P.; Hamsapriya, T.

doi:10.1504/ijwbc.2015.068540

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…The literature presents many applications that employ text clustering for different purposes. For instance, some of the text applications employed were Tweets clustering [3], identifying crime patterns on the news [4], document clustering for scientific texts using citation contexts [5], short text clustering using Wikipedia as an additional knowledge source [6], automatic topic clustering of the transcribed speech documents [7], Arabic web pages clustering and annotation using semantic class features [8], clustering for semantically related words [9], multilingual corpora clustering [10], clustering of DNA texts [11]. Other applications include data compression, image processing, visualization, exploratory data analysis, pattern recognition, and time series prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring bigram character features for Arabic text clustering

AbuZeina¹

2019

Turk J Elec Eng & Comp Sci

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The vector space model (VSM) is an algebraic model that is widely used for data representation in text mining applications. However, the VSM poses a critical challenge, as it requires a high-dimensional feature space. Therefore, many feature selection techniques, such as employing roots or stems (i.e. words without infixes and prefixes, and/or suffixes) instead of using complete word forms, are proposed to tackle this space challenge problem. Recently, the literature shows that one more basic unit feature can be used to handle the textual features, which is the twoneighboring character form that we call microword. To evaluate this feature type, we measure the accuracy of the Arabic text clustering using two feature types: the complete word form and the microword form. Hence, the microword is two consecutive characters which are also known as the Bigram character feature. In the experiment, the principal component analysis (PCA) is used to reduce the feature vector dimensions while the k-means algorithm is used for the clustering purposes. The testing set includes 250 documents of five categories. The entire corpus contains 54,472 words, whereas the vocabulary contains 13,356 unique words. The experimental results show that the complete word form score accuracy is 97.2% while the two-character form score is 96.8%. In conclusion, the accuracies are almost the same; however, the two-character form uses a smaller vocabulary as well as less PCA subspaces. The study experiments might be a significant indication of the necessity to consider the Bigram character feature in the future text processing and natural language processing applications.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring bigram character features for Arabic text clustering

AbuZeina¹

2019

Turk J Elec Eng & Comp Sci

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“…For example, in the field of customer aid, clustering support tickets is helpful for identifying classes of user complaints and estimating their volume [ 4 , 5 ]. In research on public opinion, clustering data from social media such as Twitter or Reddit is useful for discovering active topics and learning about user engagement [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Revisiting Sequential Information Bottleneck: New Implementation and Evaluation

Toledo

Venezian

Slonim

2022

Entropy

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We introduce a modern, optimized, and publicly available implementation of the sequential Information Bottleneck clustering algorithm, which strikes a highly competitive balance between clustering quality and speed. We describe a set of optimizations that make the algorithm computation more efficient, particularly for the common case of sparse data representation. The results are substantiated by an extensive evaluation that compares the algorithm to commonly used alternatives, focusing on the practically important use case of text clustering. The evaluation covers a range of publicly available benchmark datasets and a set of clustering setups employing modern word and sentence embeddings obtained by state-of-the-art neural models. The results show that in spite of using the more basic Term-Frequency representation, the proposed implementation provides a highly attractive trade-off between quality and speed that outperforms the alternatives considered. This new release facilitates the use of the algorithm in real-world applications of text clustering.

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“…At the same time, the lawbreakers ensure that effective breaking mechanisms are enforced to the systems using methodologies like keyloggers software,sending a virus through push messages and also trying to break the cryptographic algorithms using brute force technologies by creating a mess of parallel computing techniques and applying all possibilities and feasibilities to break the confidentiality. There are of late new technologies deployed for the purpose which are mostly based on the latent semantic analysis, N-Gram analysis (S.Praveen et al 2018) methodologies based on dimensionality reduction techniques using clustering techniques and machine learning techniques for annotating the text etc [1], [2], [3], [4], [5], [6].However, each of these techniques has their own limitations such as identifying the relevant text, correlating the text, interpreting the inherent meaning etc. Most of the technologies, therefore, are developed by associating a tag to every transmitted data and trying to identify the tag at the time of decryption.…”

Section: Introductionmentioning

confidence: 99%

A mechanism for identifying the guilt agent in a network using vector quantization and skew Gaussian distribution

Kumar¹,

Srinivas²,

Krishna³

2018

IJET

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The recent state of art technologies has facilitated towards the ease of transfer of information from source to destination in a most comfortable and optimized medium. The channel transmitting discrepancies are rotted out by the sophisticated mechanisms that are being used currently using fiber optical cables etc… However sophisticated transmission mechanisms are available, the data transmission is always under threat due to intruder’s hackers and guilt agents, who try to either steal the information, override the information or share the information to miscreants. Therefore techniques are to be developed to identify these guilt agents so that the data transmission can be transformed in a more secure way without any pitfalls. This paper addresses in this direction by proposing models based on vector quantization and statistical models.

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A novel method for clustering tweets in Twitter

Cited by 12 publications

References 25 publications

Exploring bigram character features for Arabic text clustering

Exploring bigram character features for Arabic text clustering

Revisiting Sequential Information Bottleneck: New Implementation and Evaluation

A mechanism for identifying the guilt agent in a network using vector quantization and skew Gaussian distribution

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