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The effect of noise in automatic text classification

Published: 25 February 2011 Publication History

Abstract

Noisy unstructured text is common in informal settings such as on-line chat, SMS, email, newsgroups and blogs, automatically transcribed text from speech, and automatically recognized text from printed or handwritten material. This paper focuses on the issues faced by automatic text classifiers in analyzing noisy documents coming from various sources. The goal of this paper is to bring out and study the effect of noise on automatic text classification. We present detailed experimental results with simulated noise on the Tech-TC300 and 20-newsgroups benchmark datasets.

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  • (2019)Prediction and Trading of Dow Jones from Twitter: A Boosting Text Mining Method with Relevant Tweets IdentificationPrimate Life Histories, Sex Roles, and Adaptability10.1007/978-3-030-15640-4_2(26-42)Online publication date: 15-Mar-2019

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  1. The effect of noise in automatic text classification

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    ICWET '11: Proceedings of the International Conference & Workshop on Emerging Trends in Technology
    February 2011
    1385 pages
    ISBN:9781450304498
    DOI:10.1145/1980022
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 25 February 2011

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    Author Tags

    1. SVM
    2. automatic text classification
    3. classifier accuracy
    4. feature noise
    5. label noise
    6. naïve Bayesian classifier
    7. noisy unstructured text
    8. text mining

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    • (2019)Prediction and Trading of Dow Jones from Twitter: A Boosting Text Mining Method with Relevant Tweets IdentificationPrimate Life Histories, Sex Roles, and Adaptability10.1007/978-3-030-15640-4_2(26-42)Online publication date: 15-Mar-2019

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