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2017 | OriginalPaper | Buchkapitel

Sentiment Analysis with Improved Adaboost and Transfer Learning Based on Gaussian Process

verfasst von : Yuling Liu, Qi Li, Guojiang Xin

Erschienen in: Cloud Computing and Security

Verlag: Springer International Publishing

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Abstract

Sentiment analysis is an increasingly important area in NLP to extract opinions and sentiment expressed by humans. Traditional methods are often difficult to tackle the problems of different sample distribution and domain dependence, which seriously limits the development of sentiment classification. In this paper, a novel sentiment analysis method is proposed by combining improved Adaboost and transfer learning based on Gaussian Processes to solve these two problems. A Paragraph Vector Model is employed to obtain the continuous distributed vector representations. Then, Adaboost method is used to choose the most important training features in source training data and auxiliary data. Finally, an asymmetric transfer learning classifier is introduced in Gaussian Processes. It is shown that, compared with the existing algorithms, our method is more effective for the different sample distribution and domain dependence.

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Vorheriges Kapitel A Method Towards Community Detection Based on Estimation of Distribution Algorithm

Nächstes Kapitel HL-HAR: Hierarchical Learning Based Human Activity Recognition in Wearable Computing

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Titel: Sentiment Analysis with Improved Adaboost and Transfer Learning Based on Gaussian Process
verfasst von: Yuling Liu
Qi Li
Guojiang Xin
Verlag: Springer International Publishing
Buch: Cloud Computing and Security
Print ISBN: 978-3-319-68541-0

Electronic ISBN: 978-3-319-68542-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-68542-7_58

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