Class normalization in centroid-based text categorization

Lertnattee, Verayuth; Theeramunkong, Thanaruk

doi:10.1016/j.ins.2005.05.010

Cited by 28 publications

(23 citation statements)

References 35 publications

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“…The proposed Gravitation Model (GM) concentrates on the adjustment of classification hyper plane to reduce the bias inherent in CBC, and it is entirely different from the previous works [3,4,5,6] which obtain the centroids with good initial term weights in construction phase or modify the position of centroids during training phase.…”

Section: The Proposed Gravitation Modelmentioning

confidence: 99%

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A Weighted Method to Improve the Centroid-based Classifier

Liu¹,

Wang²,

Tu³

et al. 2017

dtetr

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Abstract. Centroid-Based Classifier (CBC) is one of the most widely used text classification method due to its theoretical simplicity and computational efficiency. However, the accuracy of CBC is not satisfactory when it deals with the skewed distributed data. In this paper, we propose a new classification model named as Gravitation Model (GM) to solve the model misfit of CBC. In the proposed model, we give each category a mass factor to indicate its distribution in vector space and this factor can be learned from training data. We provide the performance comparisons with CBC and its improved methods based on the results of experiments conducted on twelve real datasets, which show that the proposed gravitation model consistently outperforms CBC. Furthermore, it reaches the same performance as the best centroid-based classifier and is more stable than the best one.

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Section: The Proposed Gravitation Modelmentioning

confidence: 99%

“…Centroid-Based Classifier (CBC) [1,2,3,4,5,6] is one of the most popular TC methods. The basic idea of CBC is that an unlabeled sample should be assigned to a particular class if the similarity of this sample to the centroid of the class is the largest.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Method to Improve the Centroid-based Classifier

Liu¹,

Wang²,

Tu³

et al. 2017

dtetr

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“…They have different polarity or strength of polarity when used in a different domain. Smoothing techniques have being proposed in (Tan 2007a, b;Lertnattee and Theeramunkong 2006;Guan 2009) that minimizes the effect of noise in the dataset. (Chizi et al 2009) defined a weighting scheme giving higher weights to explicit opinion words.…”

Section: Centroid Basedmentioning

confidence: 99%

Sentiment analysis and the complex natural language

Khan

Durrani

Ali

et al. 2016

Complex Adapt Syst Model

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There is huge amount of content produced online by amateur authors, covering a large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ sentiments towards a target entity. Machine learning (ML) techniques are frequently used as the natural language data is in abundance and has definite patterns. ML techniques adapt to domain specific solution at high accuracy depending upon the feature set used. The lexicon-based techniques, using external dictionary, are independent of data to prevent overfitting but they miss context too in specialized domains. Corpus-based statistical techniques require large data to stabilize. Complex network based techniques are highly resourceful, preserving order, proximity, context and relationships. Recent applications developed incorporate the platform specific structural information i.e. meta-data. New sub-domains are introduced as influence analysis, bias analysis, and data leakage analysis. The nature of data is also evolving where transcribed customer-agent phone conversation are also used for sentiment analysis. This paper reviews sentiment analysis techniques and highlight the need to address natural language processing (NLP) specific open challenges. Without resolving the complex NLP challenges, ML techniques cannot make considerable advancements. The open issues and challenges in the area are discussed, stressing on the need of standard datasets and evaluation methodology. It also emphasized on the need of better language models that could capture context and proximity.

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“…Lertnattee [27] explored the effect of term distributions and clusters within a negative class to improve the performance of a centroid-based binary classifier. Lertnattee [28] investigated the effectiveness of several commonly used normalization functions and proposed a new type of class normalization, called term-length normalization, in centroid-based text categorization. Eui-Hong [29] presented a simple linear-time centroid-based document classification algorithm that uses a similarity function to account for the term similarity between the test document and the documents in the class, as well as for the dependencies between the terms in these documents.…”

Section: Related Workmentioning

confidence: 99%

A Framework of Centroid-Based Methods for Text Categorization

Wang

Chen

Wang

2014

IEICE Trans. Inf. & Syst.

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SUMMARYText Categorization (TC) is a task of classifying a set of documents into one or more predefined categories. Centroid-based method, a very popular TC method, aims to make classifiers simple and efficient by constructing one prototype vector for each class. It classifies a document into the class that owns the prototype vector nearest to the document. Many studies have been done on constructing prototype vectors. However, the basic philosophies of these methods are quite different from each other. It makes the comparison and selection of centroid-based TC methods very difficult. It also makes the further development of centroid-based TC methods more challenging. In this paper, based on the observation of its general procedure, the centroid-based text classification is treated as a kind of ranking task, and a unified framework for centroid-based TC methods is proposed. The goal of this unified framework is to classify a text via ranking all possible classes by document-class similarities. Prototype vectors are constructed based on various loss functions for ranking classes. Under this framework, three popular centroid-based methods: Rocchio, Hypothesis Margin Centroid and DragPushing are unified and their details are discussed. A novel centroid-based TC method called SLRCM that uses a smoothing ranking loss function is further proposed. Experiments conducted on several standard databases show that the proposed SLRCM method outperforms the compared centroid-based methods and reaches the same performance as the state-of-the-art TC methods. key words: text categorization, centroid-based methods, smoothing listwise ranking centroid method, a unified framework

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Class normalization in centroid-based text categorization

Cited by 28 publications

References 35 publications

A Weighted Method to Improve the Centroid-based Classifier

A Weighted Method to Improve the Centroid-based Classifier

Sentiment analysis and the complex natural language

A Framework of Centroid-Based Methods for Text Categorization

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