KNN with TF-IDF based Framework for Text Categorization

Trstenjak, Bruno; Mikac, Sasa; Ðonko, Dženana

doi:10.1016/j.proeng.2014.03.129

Cited by 298 publications

(142 citation statements)

References 14 publications

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“…As regards our own work, we achieved an overall accuracy of 83.5% using 401 documents (of varied lengths) with 18 categories by applying the kNN in a novel way. This percentage is still higher than in comparable works [10,34,35].…”

Section: Experiments Results and Evaluationcontrasting

confidence: 66%

An innovative multi-segment strategy for the classification of legal judgments using the k-nearest neighbour classifier

Pudaruth

Soyjaudah

Gunputh

2017

Complex Intell. Syst.

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The classification of legal documents has been receiving considerate attention over the last few years. This is mainly because of the over-increasing amount of legal information that is being produced on a daily basis in the courts of law. In the Republic of Mauritius alone, a total of 141,164 cases were lodged in the different courts in the year 2015. The Judiciary of Mauritius is becoming more efficient due to a number of measures which were implemented and the number of cases disposed of in each year has also risen significantly; however, this is still not enough to catch up with the increase in the number of new cases that are lodged. In this paper, we used the k-nearest neighbour machine learning classifier in a novel way. Unlike news article, judgments are complex documents which usually span several pages and contains a variety of information about a case. Our approach consists of splitting the documents into equal-sized segments. Each segment is then classified independently of the others. The selection of the predicted category is then done through a plurality voting procedure. Using this novel approach, we have been able to classify law cases with an accuracy of over 83.5%, which is 10.5% higher than when using the whole documents dataset. To the best of our knowledge, this type of process has never been used earlier to categorise legal judgments or other types of documents. In this work, we also

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Section: Experiments Results and Evaluationcontrasting

confidence: 66%

An innovative multi-segment strategy for the classification of legal judgments using the k-nearest neighbour classifier

Pudaruth

Soyjaudah

Gunputh

2017

Complex Intell. Syst.

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“…• Term Frequency [19] The number of times a token occurs in each data sample is called its term frequency. Words having high frequency have better relationship with the sample.…”

Section: Tokenization Data Standardization Emoji Conversionmentioning

confidence: 99%

Information extraction from text messages using data mining techniques

Ahmad¹,

Varma²

2018

Malaya J. Mat.

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We are living in an era of increased pressure and mental disorders. The increased level of stress and pressure results in inclination of the number of people showing suicidal tendencies and thus a larger number of people are committing suicide. Stress can be caused due to family dispute, job dissatisfaction, health issues, etc. In the world of modern computing, people feel free to share their views and feelings over social media with peers and family members via services such as messaging. Due to the reserved nature and busy schedules of people it is extremely difficult to interact with peers and family members in person, therefore social media platforms are considered as the most used platform for personal conversations. The aim of this paper is to estimate the suicidal tendencies of a person by applying data mining techniques to the text messages a person sends to the associated people. By analysing the components of the text messages (key words and emoticons) we can estimate the suicidal tendencies of a person so that necessary steps can be taken in order to save the life of the subject.

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“…The tag which less documents uses represents the documents that have the tag have the special similar characteristic of the classification. In the determination of the weight, based on the idea TFIDF [33] …”

Section: The Approach To the Construction Of Personalized Knowlementioning

confidence: 99%

“…In the vector, each element is the term in the document along with the weight. The weight is calculated by the TFIDF method [33].…”

Section: Deriving the Weight Of Terms In Documentsmentioning

confidence: 99%

An Approach to the Construction of Personalized Knowledge Map Based on Collaborative Tagging

Li¹,

Yuan²,

Xiong³

2015

IJKE

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Abstract-Knowledge is the strategic resource for the organization. Knowledge map is an important tool for knowledge sharing. Providing the personalized knowledge map based on the preference of users can ease the burden of learning the knowledge map and facilitate the finding of the required knowledge. The tagging to documents reflects the user's preference of classification. In the paper, the approach to the personalized knowledge map construction based on the collaborative tagging is proposed. Firstly, the weight of the tag in documents is identified. Secondly, the similarity of users on the preference of classification is defined and then users that have the similar classification preference are identified to expand the current user's preference of personalized classification. Then the text vector of document and text similarity is identified. Afterwards, the knowledge is clustered according to both the personalized classification similarity and text similarity. Finally, the topics of each cluster are identified. In the topic identification, both the weight of the term in the text and the weight of the term in the tags are integrated. The experiment shows that proposed method is feasible and performs well.Index Terms-Knowledge map, knowledge management systems, personalized knowledge map.

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KNN with TF-IDF based Framework for Text Categorization

Cited by 298 publications

References 14 publications

An innovative multi-segment strategy for the classification of legal judgments using the k-nearest neighbour classifier

An innovative multi-segment strategy for the classification of legal judgments using the k-nearest neighbour classifier

Information extraction from text messages using data mining techniques

An Approach to the Construction of Personalized Knowledge Map Based on Collaborative Tagging

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