Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis

Huynh, Trung, He, Yulan and Rüger, Stefan (2015). Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis. IN: Advances in information retrieval. Hanbury, Allan; Kazai, Gabriella; Rauber, Andreas and Fuhr, Norbert (eds) Lecture notes in computer science . AUT: Springer.


In recent years, learning word vector representations has attracted much interest in Natural Language Processing. Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics. In this paper we go beyond the vector representations at the word level and propose a novel framework that learns higher-level feature representations of n-grams, phrases and sentences using a deep neural network built from stacked Convolutional Restricted Boltzmann Machines (CRBMs). These representations have been shown to map syntactically and semantically related n-grams to closeby locations in the hidden feature space. We have experimented to additionally incorporate these higher-level features into supervised classifier training for two sentiment analysis tasks: subjectivity classification and sentiment classification. Our results have demonstrated the success of our proposed framework with 4% improvement in accuracy observed for subjectivity classification and improved the results achieved for sentiment classification over models trained without our higher level features.

Publication DOI: https://doi.org/10.1007/978-3-319-16354-3_49
Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Event Title: 37th European Conference on Information Retrieval Research
Event Type: Other
Event Dates: 2015-03-29 - 2015-04-02
Uncontrolled Keywords: convolutional restricted Boltzmann machines,sentiment analysis,stacked restricted Boltzmann Machine,word embedding,Computer Science(all),Theoretical Computer Science
Full Text Link: http://link.spr ... -319-16354-3_49
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2015
Authors: Huynh, Trung
He, Yulan ( 0000-0003-3948-5845)
Rüger, Stefan



Version: Accepted Version

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