Using Wavelets to Classify Documents

Xexéo, Geraldo; Souza, J. de; Castro, Patrícia F.; Pinheiro, Wallace A.

doi:10.1109/wiiat.2008.221

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…For classification, particularly representing a document as a set of key terms, DWTs achieved better results than the classic Space Vector Model as seen in Thaicharoen et al (2008). Using the same principle, but a different model, Xexéo et al (2008) also performed document classification successfully, this approach took advance of the dimensionality reduction by DWT and represented the document based on the reorganization of its terms by the analysis of the correlation between them.…”

Section: Wavelets On Text Descriptionmentioning

confidence: 99%

Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets

Barbon

Campos

Tavares

et al. 2018

ACM Trans. Multimedia Comput. Commun. Appl.

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Social interactions take place in environments that influence people’s behaviours and perceptions. Nowadays, the users of Online Social Network (OSN) generate a massive amount of content based on social interactions. However, OSNs wide popularity and ease of access created a perfect scenario to practice malicious activities, compromising their reliability. To detect automatic information broadcast in OSN, we developed a wavelet-based model that classifies users as being human, legitimate robot, or malicious robot, as a result of spectral patterns obtained from users’ textual content. We create the feature vector from the Discrete Wavelet Transform along with a weighting scheme called Lexicon-based Coefficient Attenuation. In particular, we induce a classification model using the Random Forest algorithm over two real Twitter datasets. The corresponding results show the developed model achieved an average accuracy of 94.47% considering two different scenarios: single theme and miscellaneous one.

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Section: Wavelets On Text Descriptionmentioning

confidence: 99%

Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets

Barbon

Campos

Tavares

et al. 2018

ACM Trans. Multimedia Comput. Commun. Appl.

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“…When word signals are used to represent the same documents, instead of the classic Vector Space Model, better results are obtained, as reported in [50]. The use of different signal models, but still based on wavelets, makes another possibility, as explored in [59].…”

Section: Text Mining and The Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Account classification in online social networks with LBCA and wavelets

Igawa

Barbon

Paulo

et al. 2016

Information Sciences

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We developed a wavelet-based approach for account classification that detects textual dissemination by bots on an Online Social Network (OSN). Its main objective is to match account patterns with humans, cyborgs or robots, improving the existing algorithms that automatically detect frauds. With a computational cost suitable for OSNs, the proposed approach analyses the distribution of key terms. The descriptors, a wavelet-based feature vector for each user's account, work in conjunction with a new weighting scheme, called Lexicon Based Coefficient Attenuation (LBCA) and serve as inputs to one of the classifiers tested: Random Forests and Multilayer Perceptrons. Experiments were performed using a set of posts crawled during the 2014 FIFA World Cup, obtaining accuracies within the range from 94 to 100%.

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“…Using the signal concept as representation model with DWT led to improvement in the performance of text mining tasks like document clustering [30], document classification [31,32,33] and recommender system on Twitter [34].…”

Section: A Proximity-base Information Retrieval Modelmentioning

confidence: 99%

A Novel Information Retrieval Approach using Query Expansion and Spectral-based

Alnofaie¹,

Dahab²,

Kamal³

2016

ijacsa

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Abstract-Most of the information retrieval (IR) models rank the documents by computing a score using only the lexicographical query terms or frequency information of the query terms in the document. These models have a limitation as they does not consider the terms proximity in the document or the term-mismatch or both of the two. The terms proximity information is an important factor that determines the relatedness of the document to the query. The ranking functions of the Spectral-Based Information Retrieval Model (SBIRM) consider the query terms frequency and proximity in the document by comparing the signals of the query terms in the spectral domain instead of the spatial domain using Discrete Wavelet Transform (DWT). The query expansion (QE) approaches are used to overcome the word-mismatch problem by adding terms to query, which have related meaning with the query. The QE approaches are divided to statistical approach Kullback-Leibler divergence (KLD) and semantic approach P-WNET that uses WordNet. These approaches enhance the performance. Based on the foregoing considerations, the objective of this research is to build an efficient QESBIRM that combines QE and proximity SBIRM by implementing the SBIRM using the DWT and KLD or P-WNET. The experiments conducted to test and evaluate the QESBIRM using Text Retrieval Conference (TREC) dataset. The result shows that the SBIRM with the KLD or P-WNET model outperform the SBIRM model in precision (P@), R-precision, Geometric Mean Average Precision (GMAP) and Mean Average Precision (MAP).

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Using Wavelets to Classify Documents

Cited by 10 publications

References 9 publications

Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets

Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets

Account classification in online social networks with LBCA and wavelets

A Novel Information Retrieval Approach using Query Expansion and Spectral-based

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