Segmentation and Classification of Opinions with Recurrent Neural Networks

Imran Sheikh 1 Irina Illina 1 Dominique Fohr 1
1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Automatic opinion/sentiment analysis is essential for analysing large amounts of text as well as audio/video data communicated by users. This analysis provides highly valuable information to companies, government and other entities, who want to understand the likes, dislikes and feedback of the users and people in general. Opinion/Sentiment analysis can follow a classification approach or perform a detailed aspect level analysis. In this paper, we address a problem in between these two, that of segmentation and classification of opinions in text. We propose a recurrent neural network model with bi-directional LSTM-RNN, to perform joint segmentation and classification of opinions. We introduce a novel method to train neural networks for segmentation tasks. With experiments on a dataset built from the standard RT movie review dataset, we demonstrate the effectiveness of our proposed model. Proposed model gives promising results on opinion segmentation, and can be extended to general sequence segmentation tasks.
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Communication dans un congrès
IEEE Information Systems and Economic Intelligence, May 2017, Al Hoceima, Morocco. 2017, proceedings of IEEE SIIE
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Dernière modification le : jeudi 11 janvier 2018 - 06:27:31
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  • HAL Id : hal-01491182, version 1

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Imran Sheikh, Irina Illina, Dominique Fohr. Segmentation and Classification of Opinions with Recurrent Neural Networks. IEEE Information Systems and Economic Intelligence, May 2017, Al Hoceima, Morocco. 2017, proceedings of IEEE SIIE. 〈hal-01491182〉

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