Dependency Tree-Based Rules for Concept-Level Aspect-Based Sentiment Analysis

2014 ◽  
pp. 41-47 ◽  
Author(s):  
Soujanya Poria ◽  
Nir Ofek ◽  
Alexander Gelbukh ◽  
Amir Hussain ◽  
Lior Rokach
Keyword(s):  
Sentiment Analysis ◽  
Dependency Tree ◽  
Level Aspect

scholarly journals DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers

2021 ◽  
Author(s):  
Abdul Wahab ◽  
Rafet Sifa
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Syntactic Structure ◽  
Language Modeling ◽  
Benchmark Dataset ◽  
Fine Tuning ◽  
New Model ◽  
Dependency Tree ◽  
Better Than

<div> <div> <div> <p> </p><div> <div> <div> <p>In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT and has an additional third objective called Parent Prediction (PP) apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both BERT- Base and DIBERT. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis. </p> </div> </div> </div> </div> </div> </div>

scholarly journals DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers

2021 ◽  
Author(s):  
Abdul Wahab ◽  
Rafet Sifa
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Syntactic Structure ◽  
Language Modeling ◽  
Benchmark Dataset ◽  
Fine Tuning ◽  
New Model ◽  
Dependency Tree ◽  
Better Than

<div> <div> <div> <p> </p><div> <div> <div> <p>In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT and has an additional third objective called Parent Prediction (PP) apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both BERT- Base and DIBERT. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis. </p> </div> </div> </div> </div> </div> </div>

Review-level aspect-based sentiment analysis using an ontology

2018 ◽  
Author(s):  
Sophie de Kok ◽  
Linda Punt ◽  
Rosita van den Puttelaar ◽  
Karoliina Ranta ◽  
Kim Schouten ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Level Aspect

scholarly journals Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree

2019 ◽  
Author(s):  
Kai Sun ◽  
Richong Zhang ◽  
Samuel Mensah ◽  
Yongyi Mao ◽  
Xudong Liu
Keyword(s):  
Sentiment Analysis ◽  
Dependency Tree

scholarly journals Exploring BERT for Aspect Extraction in Portuguese Language

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Émerson Lopes ◽  
Ulisses Correa ◽  
Larissa Freitas
Keyword(s):  
Computer Science ◽  
Sentiment Analysis ◽  
Sentiment Classification ◽  
Science Field ◽  
Aspect Extraction ◽  
Sentiment Orientation ◽  
Accommodation Sector ◽  
Two Stages ◽  
Document Level ◽  
Level Aspect

Sentiment Analysis is the computer science field that comprises techniques that aim to automatically extract opinions from texts. Usually, these techniques assign a Sentiment Orientation to the whole document (Document Level Sentiment Analysis). But a document can express sentiment about several aspects of an entity. Methods that extract those aspects, paired with the sentiment about them, operate in the Aspect Level. Aspect-Based Sentiment Analysis approaches can be split into two stages: Aspect Extraction and Aspect Sentiment Classification. The literature presents works mainly focused on reviews about hotels, smartphones, or restaurants. In this work, we present an approach for Aspect Extraction based on Multilingual (Google's) and Portuguese (BERTimbau) BERT pre-trained models. Our experiments show that Aspect Extraction based on BERT pre-trained for Portuguese achieved Balanced Accuracy of up to 93% on a corpus of reviews about the accommodation sector.

scholarly journals A Fine-grained Sentiment Analysis Method Based on Dependency Tree and Graph Attention Network

2020 ◽  
Vol 1651 ◽  
pp. 012173
Author(s):  
Yejin Tan ◽  
Wangshu Guo ◽  
Jiawei He ◽  
Jian Liu ◽  
Ming Xian
Keyword(s):  
Sentiment Analysis ◽  
Analysis Method ◽  
Attention Network ◽  
Fine Grained ◽  
Dependency Tree

scholarly journals DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers

2021 ◽  
Author(s):  
Abdul Wahab ◽  
Rafet Sifa
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Syntactic Structure ◽  
Language Modeling ◽  
Benchmark Dataset ◽  
Fine Tuning ◽  
New Model ◽  
Dependency Tree ◽  
Better Than

<div> <div> <div> <p> </p><div> <div> <div> <p>In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT and has an additional third objective called Parent Prediction (PP) apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both BERT- Base and DIBERT. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis. </p> </div> </div> </div> </div> </div> </div>

Sign in / Sign up

Export Citation Format

Share Document