Term Weighting for Information

Ropero, Jorge; Gómez, Ariel; Carrasco, Alejandro; León, Carlos; Luque, J.

doi:10.5772/37837

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…TF-IDF method has simple calculation and allows the multiple document comparison [29], however, it has some deficiencies [75]. Imperfect information, called data sparsity in data mining and text mining, is seen as one of the most crucial problem in analyzing textual data [66].…”

Section: Feature Extraction and Analysismentioning

confidence: 99%

“…Imperfect information, called data sparsity in data mining and text mining, is seen as one of the most crucial problem in analyzing textual data [66]. The VSM using TF-IDF, which yields healthy results in the analysis of long texts, is insufficient in the analysis of short text data [75]. For this reason, TF-IDF method will not be the most proper choice for analyzing consumer evaluation that is short text.…”

Section: Feature Extraction and Analysismentioning

confidence: 99%

“…In the studies on consumer evaluations, the data which consist of products, consumer satisfactions and company ratings are acquired from survey methods or e-commerce sites. Although, these studies have major contributions to the literature, they are inadequate in presenting realistic results because they are not based on the textual views of the consumers [75]. In a few studies based on textual evaluations, semi-structured data that e-commerce sites supply have been analyzed by text mining [97].…”

Section: Introductionmentioning

confidence: 99%

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Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce

Fidan

2020

J. theor. appl. electron. commer. res.

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Companies have gained important advantages by the development of electronic commerce. Consumer evaluations in electronic environment offer great possibilities for analysis. The fact that the consumer opinions are comprised of textual data, analyzes have complicated and challenging process. In recent years, it is seen that text mining methods are used in analyzes in the literature. However, the evaluations of consumers which are formed by short texts make it necessary to realize the analysis with insufficient data. The weighting methods such as Term Frequency and Term Frequency-Inverse Document Frequency as well as common used classification algorithms such as Naïve Bayes and Support Vector Machine have some inadequacies in short text analysis. In this study, a grey relational classification model based on Vector Space Model and Bag of Words has been developed. The model was first applied to the positive-negative categorization of the evaluations, then, applied to the classification of negative evaluations. It was determined that the accuracy level of the model is higher than the classification algorithms commonly used in short text. According to the results of the research, 9637 negative evaluations in 24479 consumer opinion were determined, and 50.4% of the negative evaluations were found to have the most problems related to product.

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Section: Feature Extraction and Analysismentioning

confidence: 99%

Section: Feature Extraction and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce

Fidan

2020

J. theor. appl. electron. commer. res.

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A Term Weighting Scheme Using Fuzzy Logic for Enhancing Candidate Screening Task

Habous

Nfaoui

2021

Lecture Notes in Electrical Engineering

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The candidate screening is an essential task in the recruitment process. It is about choosing a suitable candidate that satisfies the recruiter requirements for a given job position. The evolution of information technologies leads to an increase in the use of the recruitment web portals by the candidates that apply for the job positions published in the job boards. Thus the candidate screening process automation becomes necessary to handle the enormous volume of CVs applying for the job positions. In Information Technology (IT) domain, the technology skills are the key competencies to identify the job profile; Consequently, they have priority to the candidate screening task. In this paper, we enhance the candidate screening task in the IT field. For this purpose, we propose a fuzzy-based weighting scheme using domain ontology for Information Retrieval (IR). Experimental results on a recruiter company data show the effective results of our proposed solution.

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A fuzzy ERM for extracting and modeling uncertain spatial expressions in text

Kanagavalli

Raja²

2014

International Conference on Information Communication and Embedded Systems (ICICES2014)

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The information and knowledge sharing era is exploding with information that people are continuously sharing over various sources across the globe. This has made the retrieval a difficult task. There are plenty of documents everywhere about anything and everything a human mind can think about. All this information is mostly presented transferred and shared using natural language. The biggest challenge and research area has been to enable machines understand and decipher what has been communicated to it through natural language. There are document classification systems that classifies and groups the documents that are speaking about the same concept. But the same type of classification is not successfully handled if it happens to be based on spatial keywords. This is due to the inherent ambiguity and uncertainty that is associated with the spatial terms found in natural language descriptions. Text documents imply the usage of natural language and as such it yields to explicit vague fuzzy descriptions involving linguistic terms such as near to, far from, to the east of, very close and also implicit vague spatial references. Event reporting in case of disasters or in case of special occasions is also generally done using free form text rather than structured methods since it allows more detailed descriptions to be added in. Much of these afore said text documents acting as an information source and the query posed by the user implicitly have a geographic or spatial reference component present in it. This logically leads to the conclusion by the previous studies that more than 80% of the searches are pertaining to geographic locations. Fuzzy logic is an extension to the Boolean crisp logic to accommodate for the fuzziness of an element belonging to a set. This paper studies the feasibility of fuzzy logic techniques in resolving the spatial uncertainty in text and presents a Fuzzy ERM (Extraction, Resolving and Modeling) architecture for the same.

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Term Weighting for Information

Cited by 5 publications

References 9 publications

Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce

Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce

A Term Weighting Scheme Using Fuzzy Logic for Enhancing Candidate Screening Task

A fuzzy ERM for extracting and modeling uncertain spatial expressions in text

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