Abstract
Nowadays word embeddings, also known as word vectors, play an important role for many natural language processing (NLP) tasks. In general, these word embeddings are learned from unsupervised learning models (e.g. Word2Vec, GloVe) with a large unannotated corpus and they are independent with the task of their application. In this paper we aim to enrich word embeddings by adding more information from a specific task that is the aspect based sentiment analysis. We propose a model using a convolutional neural network that takes a labeled data set, the learned word embeddings from an unsupervised learning model (e.g. Word2Vec) as input and fine-tunes word embeddings to capture aspect category and sentiment information. We conduct experiments on restaurant review data (http://spidr-ursa.rutgers.edu/datasets/). Experimental results show that fine-tuned word embeddings outperform unsupervisedly learned word embeddings.
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Acknowledgement
This paper is supported by The Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01-2014.22.
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Pham, DH., Nguyen, TTT., Le, AC. (2017). Fine-Tuning Word Embeddings for Aspect-Based Sentiment Analysis. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_56
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DOI: https://doi.org/10.1007/978-3-319-64206-2_56
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