Aspect Extraction
32 papers with code • 6 benchmarks • 4 datasets
Aspect extraction is the task of identifying and extracting terms relevant for opinion mining and sentiment analysis, for example terms for product attributes or features.
Benchmarks
These leaderboards are used to track progress in Aspect Extraction
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SemEval-2014 Task-4
Most implemented papers
Replication issues in syntax-based aspect extraction for opinion mining
Reproducing experiments is an important instrument to validate previous work and build upon existing approaches.
Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN
These results, as well as further experiments on domain adaptation for aspect extraction, suggest that differences between speech and written text, which have been discussed extensively in the literature, also extend to the domain of product reviews, where they are relevant for fine-grained opinion mining.
Review highlights: opinion mining on reviews: a hybrid model for rule selection in aspect extraction
We introduce a hybrid technique which combines machine learning and rule based model.
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
howardhsu/BERT-for-RRC-ABSA
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NAACL 2019
Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC.
Aspect Detection using Word and Char Embeddings with (Bi)LSTM and CRF
We proposed a~new accurate aspect extraction method that makes use of both word and character-based embeddings.
Multilingual aspect clustering for sentiment analysis
In this article, we address the novel task of multilingual aspect clustering, which aims at grouping semantically related aspects extracted from reviews written in several languages.
Structure-Level Knowledge Distillation For Multilingual Sequence Labeling
Alibaba-NLP/MultilangStructureKD
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ACL 2020
Multilingual sequence labeling is a task of predicting label sequences using a single unified model for multiple languages.
Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis
HieuPhan33/LCFS-BERT
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ACL 2020
This increases the accuracy of the aspect sentiment classifier.
Simple Unsupervised Similarity-Based Aspect Extraction
In the context of sentiment analysis, there has been growing interest in performing a finer granularity analysis focusing on the specific aspects of the entities being evaluated.
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks
cuhksz-nlp/dgsa
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COLING 2020
End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms.
