Book chapter · Conference paper
Logical Entity Level Sentiment Analysis
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Department of Management Engineering, Technical University of Denmark2
Transport Modelling, Department of Management Engineering, Technical University of Denmark3
Technical University of Denmark4
Algorithms and Logic, Department of Applied Mathematics and Computer Science, Technical University of Denmark5
We present a formal logical approach using a combinatory categorial grammar for entity level sentiment analysis that utilizes machine learning techniques for efficient syntactical tagging and performs a deep structural analysis of the syntactical properties of texts in order to yield precise results.
The method should be seen as an alternative to pure machine learning methods for sentiment analysis, which are argued to have high difficulties in capturing long distance dependencies, and can be dependent on significant amount of domain specific training data. The results show that the method yields high correctness, but further investment is needed in order to improve its robustness.
Language: | English |
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Publisher: | Springer |
Year: | 2017 |
Pages: | 54-71 |
Proceedings: | 22nd International Conference, Formal Grammar |
Series: | Lecture Notes in Computer Science |
Journal subtitle: | 22nd International Conference, Fg 2017, Toulouse, France, July 22-23, 2017, Revised Selected Papers |
ISBN: | 3662563428 , 3662563436 , 9783662563427 and 9783662563434 |
ISSN: | 03029743 and 16113349 |
Types: | Book chapter and Conference paper |
DOI: | 10.1007/978-3-662-56343-4_4 |
ORCIDs: | Petersen, Niklas Christoffer and Villadsen, Jørgen |