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2018 | OriginalPaper | Buchkapitel

Fine-Tuning of Word Embeddings for Semantic Sentiment Analysis

verfasst von : Mattia Atzeni, Diego Reforgiato Recupero

Erschienen in: Semantic Web Challenges

Verlag: Springer International Publishing

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Abstract

In this paper, we present a state-of-the-art deep-learning approach for sentiment polarity classification. Our approach is based on a 2-layer bidirectional Long Short-Term Memory network, equipped with a neural attention mechanism to detect the most informative words in a natural language text. We test different pre-trained word embeddings, initially keeping these features frozen during the first epochs of the training process. Next, we allow the neural network to perform a fine-tuning of the word embeddings for the sentiment polarity classification task. This allows projecting the pre-trained embeddings in a new space which takes into account information about the polarity of each word, thereby being more suitable for semantic sentiment analysis. Experimental results are promising and show that the fine-tuning of the embeddings with a neural attention mechanism allows boosting the performance of the classifier.

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Vorheriges Kapitel Domain-Aware Sentiment Classification with GRUs and CNNs

Nächstes Kapitel The KABSA System at ESWC-2018 Challenge on Semantic Sentiment Analysis

http://www.maurodragoni.com/research/opinionmining/events/challenge-2018/.

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Titel: Fine-Tuning of Word Embeddings for Semantic Sentiment Analysis
verfasst von: Mattia Atzeni
Diego Reforgiato Recupero
Verlag: Springer International Publishing
Buch: Semantic Web Challenges
Print ISBN: 978-3-030-00071-4

Electronic ISBN: 978-3-030-00072-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00072-1_12

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