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Authors: Wouter Leeftink and Gerasimos Spanakis

Affiliation: Department of Data Science and Knowlednge Engineering, Maastricht University, Maastricht, 6200MD and Netherlands

Keyword(s): Sentiment Transformation, Deep Learning, Autoencoders.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Natural Language Processing ; Pattern Recognition ; Symbolic Systems

Abstract: An obstacle to the development of many natural language processing products is the vast amount of training examples necessary to get satisfactory results. The generation of these examples is often a tedious and time-consuming task. This paper this paper proposes a method to transform the sentiment of sentences in order to limit the work necessary to generate more training data. This means that one sentence can be transformed to an opposite sentiment sentence and should reduce by half the work required in the generation of text. The proposed pipeline consists of a sentiment classifier with an attention mechanism to highlight the short phrases that determine the sentiment of a sentence. Then, these phrases are changed to phrases of the opposite sentiment using a baseline model and an autoencoder approach. Experiments are run on both the separate parts of the pipeline as well as on the end-to-end model. The sentiment classifier is tested on its accuracy and is found to perform adequatel y. The autoencoder is tested on how well it is able to change the sentiment of an encoded phrase and it was found that such a task is possible. We use human evaluation to judge the performance of the full (end-to-end) pipeline and that reveals that a model using word vectors outperforms the encoder model. Numerical evaluation shows that a success rate of 54.7% is achieved on the sentiment change. (More)

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Paper citation in several formats:
Leeftink, W. and Spanakis, G. (2019). Towards Controlled Transformation of Sentiment in Sentences. In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-350-6; ISSN 2184-433X, SciTePress, pages 809-816. DOI: 10.5220/0007569608090816

@conference{icaart19,
author={Wouter Leeftink. and Gerasimos Spanakis.},
title={Towards Controlled Transformation of Sentiment in Sentences},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2019},
pages={809-816},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007569608090816},
isbn={978-989-758-350-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Towards Controlled Transformation of Sentiment in Sentences
SN - 978-989-758-350-6
IS - 2184-433X
AU - Leeftink, W.
AU - Spanakis, G.
PY - 2019
SP - 809
EP - 816
DO - 10.5220/0007569608090816
PB - SciTePress

- Science and Technology Publications, Lda.
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