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Weakly supervised techniques for domain-independent sentiment classification

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posted on 2023-06-07, 21:43 authored by Jonathon Read, John Carroll
An important sub-task of sentiment analysis is polarity classification, in which text is classified as being positive or negative. Supervised machine learning techniques can perform this task very effectively. However, they require a large corpus of training data, and a number of studies have demonstrated that the good performance of supervised models is dependent on a good match between the training and testing data with respect to the domain, topic and time-period. Weakly-supervised techniques use a large collection of unlabelled text to determine sentiment, and so their performance may be less dependent on the domain, topic and time-period represented by the testing data. This paper presents experiments that investigate the effectiveness of word similarity techniques when performing weakly-supervised sentiment classification. It also considers the extent to which the performance of each method is independent from the domain, topic and time-period of the testing data. The results indicate that the word similarity techniques are suitable for applications that require sentiment classification across several domains.

History

Publication status

  • Published

Publisher

Association for Computing Machinary, Inc

Volume

p45-52

Page range

45-52

Pages

8.0

Presentation Type

  • paper

Event name

First International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement

Event location

Hong Kong, China

Event type

conference

ISBN

978-1-60558-805-6

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

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