Relevance Feature Discovery for Text Mining

Li, Yuefeng; Algarni, Abdulmohsen; Albathan, Mubarak; Shen, Yan; Bijaksana, Moch Arif

doi:10.1109/tkde.2014.2373357

Cited by 59 publications

(15 citation statements)

References 60 publications

(95 reference statements)

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“…This weight is pattern‐level information about a term, which represents the closeness of the term to the main theme of the pattern set. – Ontology‐level information : If a term appears in many concepts in an ontology, the term is general. The ontological specificity of a term is inversely related to the frequency of concepts that contain the term . The more specific a term is, the more important a role it can play in representing the subject matter of a pattern set.…”

Section: Proposed Modelmentioning

confidence: 99%

“…The ontological specificity of a term is inversely related to the frequency of concepts that contain the term. 53 The more specific a term is, the more important a role it can play in representing the subject matter of a pattern set. If the same ontology is used for annotating all the patterns in a collection, ontology size (ie, number of concepts in the ontology) does not play an important role in the estimation of ontological specificity, ie, normalization is not needed.…”

Section: Five Information Levelsmentioning

confidence: 99%

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Conceptual annotation of text patterns

Bashar

Shen

et al. 2017

Computational Intelligence

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Patterns are used as a fundamental means for analyzing data in many data mining applications. Many efficient techniques have been developed to discover patterns. However, the excessive number of discovered patterns and the lack of semantic information have made it difficult for a user to interpret and explore the patterns. A rough idea of the meanings of patterns can benefit the user in the process of exploring them. To address this issue, this paper presents a model for automatically annotating patterns with concepts. In addition, in a given context, the relative importance of each term that defines a concept is not the same. To define a context, there are a number of related information sources, such as documents, patterns, concepts, and an ontology. The question is which information sources are useful for estimating the relative importance of the terms? Should the most accurate one to be focused on or all of them be used to define the context? This research investigated these questions and defined an effective annotation context to estimate the relative importance of the terms, where the aim is to improve the performance of a machine that relies on the subject matter of a pattern set. The model is evaluated by comparing it with different baseline models on 2 standard datasets. The results show that the performance of the proposed model is significantly better.

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Section: Proposed Modelmentioning

confidence: 99%

Section: Five Information Levelsmentioning

confidence: 99%

Conceptual annotation of text patterns

Bashar

Shen

et al. 2017

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hence, this section will update more research work pertaining to text mining. Li et al [12] have presented a framework for exploring the relevancy among the documents using text mining approach in order to excavate more information about document level feature extraction. A treebased mechanism for identifying the interaction of a person was introduced by Chang et al [13] by representing semantics, context, and syntactic data over a convolution kernel.…”

Section: A Backgroundmentioning

confidence: 99%

RKE-CP: Response-based Knowledge Extraction from Collaborative Platform of Text-based Communication

Jalaja¹,

Kavitha²

2017

ijacsa

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Abstract-With the generation of massive amount of productcentric responses from existing applications on collaborative platform, it is necessary to perform a discrete analytical operation on it. As majority of such responses are textual in nature, it increases the applicability of using text mining approaches on it. We review the existing research contribution in text mining to find that there are significant research gap. Therefore, the proposed study presents a technique called as RKE-CP i.e. Response-based Knowledge Extraction from Collaborative Platform where the term Collaborative points towards cloud environment. The proposed technique is designed using mathematical modelling where the maximum focus of design and implementation lies on accomplishing a good balance between faster response time in mining operation and higher precision/recall rate. The study outcome possess' better precision score, recall, and lowered processing time as compared with the most relevant work text mining.

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“…Such huge volume of data is analyzed by using text mining. Text mining [1], [2] is used to extract relevant information from unstructured type of data. An important application of the text mining [3] which could be also related with the social big data is web news mining [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Categorizing online news articles using penguin search optimization algorithm

Nithya

Sivakumari

2018

IJET

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Online news is an emerging channel where the internet users can get news. Analyzing huge volume of online news articles is a challenging one, because online news articles are generated and updated time to time. Big data techniques are used to tackle this problem. In order to classify the news articles into different categories, an approach based on Evolving Fuzzy Systems(EFS) was used. It categories news articles based on the changes in the content of the corresponding articles. However, it has the problem in the selection of threshold value. Moreover Gaussian membership function is used in EFS that describes the closeness to the prototype. Sometimes it is ha rd to justify. So in this paper, a Penguins Search Optimization Algorithm(PeSOA) is introduced to optimize the pruning threshold value and a bell shaped fuzzy membership function is introduced to define the closeness to the prototype. The optimized pruning threshold is used in term filtering which prune the generated terms based on their frequencies of occurrence throughout the collection. Then the fuzzy rules are generated by EFS where bell shaped fuzzy membership function is used to define the closeness to the prototype. Based on t he fuzzy rules the online news articles are categorized.

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Relevance Feature Discovery for Text Mining

Cited by 59 publications

References 60 publications

Conceptual annotation of text patterns

Conceptual annotation of text patterns

RKE-CP: Response-based Knowledge Extraction from Collaborative Platform of Text-based Communication

Categorizing online news articles using penguin search optimization algorithm

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