NSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis
نویسندگان
چکیده
This paper describes our deep learningbased approach to multilingual aspectbased sentiment analysis as part of SemEval 2016 Task 5. We use a convolutional neural network (CNN) for both aspect extraction and aspect-based sentiment analysis. We cast aspect extraction as a multi-label classification problem, outputting probabilities over aspects parameterized by a threshold. To determine the sentiment towards an aspect, we concatenate an aspect vector with every word embedding and apply a convolution over it. Our constrained system (unconstrained for English) achieves competitive results across all languages and domains, placing first or second in 5 and 7 out of 11 language-domain pairs for aspect category detection (slot 1) and sentiment polarity (slot 3) respectively, thereby demonstrating the viability of a deep learning-based approach for multilingual aspect-based sentiment analysis.
منابع مشابه
INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis
This paper describes our deep learningbased approach to multilingual aspectbased sentiment analysis as part of SemEval 2016 Task 5. We use a convolutional neural network (CNN) for both aspect extraction and aspect-based sentiment analysis. We cast aspect extraction as a multi-label classification problem, outputting probabilities over aspects parameterized by a threshold. To determine the senti...
متن کاملNLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features
This paper describes our system submitted to Aspect Based Sentiment Analysis Task 5 of SemEval-2016. Our system consists of two components: binary classifiers trained using single layer feedforward network for aspect category classification (Slot 1), and sequential labeling classifiers for opinion target extraction (Slot 2). Besides extracting a variety of lexicon features, syntactic features, ...
متن کاملXRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis
This paper presents our contribution to the SemEval 2016 task 5: Aspect-Based Sentiment Analysis. We have addressed Subtask 1 for the restaurant domain, in English and French, which implies opinion target expression detection, aspect category and polarity classification. We describe the different components of the system, based on composite models combining sophisticated linguistic features wit...
متن کاملGTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis
This paper describes in detail the approach carried out by the GTI research group for SemEval 2016 Task 5: Aspect-Based Sentiment Analysis, for the different subtasks proposed, as well as languages and dataset contexts. In particular, we developed a system for category detection based on SVM. Then for the opinion target detection task we developed a system based on CRFs. Both are built for rest...
متن کاملCOMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory
This paper reports our submission to the Aspect-Based Sentiment Analysis task of SemEval 2016. It covers the prediction of sentiment for a given set of aspects (e.g., subtask 1, slot 2) for the English language using discourse analysis. To that end, a discourse parser implementing the Rhetorical Structure Theory is employed and the resulting information is used to determine the context of each ...
متن کامل