Aspect Extraction in Product Reviews via an Improved Unsupervised Method

2015 ◽  
Author(s):  
Wei Jiang ◽  
Hao Pan
Keyword(s):  
Product Reviews ◽  
Aspect Extraction ◽  
Unsupervised Method

scholarly journals A Rule-Based Approach to Aspect Extraction from Product Reviews

2014 ◽  
Author(s):  
Soujanya Poria ◽  
Erik Cambria ◽  
Lun-Wei Ku ◽  
Chen Gui ◽  
Alexander Gelbukh
Keyword(s):  
Product Reviews ◽  
Rule Based ◽  
Aspect Extraction ◽  
Rule Based Approach

scholarly journals ASPECT EXTRACTION AND COMPUTATION OF LEXICON FROM NYKAA PRODUCT REVIEWS

2021 ◽  
Author(s):  
T Sai Sravani K Maneesha Reddy S Nirupama T Sai Sravani K Maneesha Reddy S Nirupama ◽  
Keyword(s):  
Product Reviews ◽  
Aspect Extraction

Knowledge empowered prominent aspect extraction from product reviews

2019 ◽  
Vol 56 (3) ◽  
pp. 408-423 ◽  
Author(s):  
Zhiyi Luo ◽  
Shanshan Huang ◽  
Kenny Q. Zhu
Keyword(s):  
Product Reviews ◽  
Aspect Extraction

scholarly journals Aspect Extraction from Product Reviews Using Category Hierarchy Information

2017 ◽  
Author(s):  
Yinfei Yang ◽  
Cen Chen ◽  
Minghui Qiu ◽  
Forrest Bao
Keyword(s):  
Product Reviews ◽  
Aspect Extraction

Effective Combination of Bert Model and Cross-Sentence Contexts in Aspect Extraction

2021 ◽  
Author(s):  
Anh Khoi Le ◽  
Truong Son Nguyen
Keyword(s):  
Source Code ◽  
Product Reviews ◽  
Research Groups ◽  
Aspect Extraction ◽  
Testing Phase ◽  
Effective Combination

The Aspect Extraction (AE) field investigates in collecting words which are sentiment aspects in sentences and documents. Despite the pandemic, the number of products purchased online is still growing, which means that the number of product reviews and comments is also increasing rapidly, so the role of the task is gradually crucial. Extract aspects in the text is a difficult task, that requires algorithms capable of deep capturing the semantics of the text. In this work, we combine two models of the two research groups, with the first using the BERT algorithm with multiple concatenated layers and the second using the strategies to enrich the dataset by itself in the training or testing phase. The source code is available on github.com, researchers can run it through scripts, modify it for further research also. https://github.com/leanhkhoi/AE_BERT_CROSS_SENTENCES

Aspect Summarization for Product Reviews based on Attention-based Aspect Extraction

2021 ◽  
Vol 48 (12) ◽  
pp. 1318-1328
Author(s):  
Jun-Nyeong Jeong ◽  
Sang-Young Kim ◽  
Seong-Tae Kim ◽  
Jeong-Jae Lee ◽  
Yuchul Jung
Keyword(s):  
Product Reviews ◽  
Aspect Extraction
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