Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks

Schmitt, Martin; Steinheber, Simon and Schreiber, Konrad (October 2018): Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks. 2018 Conference on Empirical Methods in Natural Language Processing; EMNLP 2018, Brussels, Belgium, 31. October - 02. November 2018. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Stroudsburg PA: Association for Computational Linguistics. pp. 1109-1114

Abstract

In this work, we propose a new model for aspect-based sentiment analysis. In contrast to previous approaches, we jointly model the detection of aspects and the classification of their polarity in an end-to-end trainable neural net- work. We conduct experiments with different neural architectures and word representations on the recent GermEval 2017 dataset. We were able to show considerable performance gains by using the joint modeling approach in all set- tings compared to pipeline approaches. The combination of a convolutional neural network and fasttext embeddings outperformed the best submission of the shared task in 2017, establishing a new state of the art.

• Item Type: Conference or Workshop Item (Paper)
• EU Funded Grant Agreement Number: 740516
• EU Projects: Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement
• Form of publication: Publisher's Version
• Research Centers: Center for Information and Language Processing (CIS)
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems; 000 Computer science, information and general works > 004 Data processing computer science; 400 Language > 400 Language; 400 Language > 410 Linguistics
• Place of Publication: Stroudsburg PA
• Annotation: ISBN 978-1-948087-84-1
• Language: English