Aspect based fine-grained sentiment analysis for online reviews

2019 ◽  
Vol 488 ◽  
pp. 190-204 ◽  
Author(s):  
Feilong Tang ◽  
Luoyi Fu ◽  
Bin Yao ◽  
Wenchao Xu
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Fine Grained

scholarly journals Users’ Sentiment Analysis of Shopping Websites Based on Online Reviews

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xiaohong Wang ◽  
Shuang Dong
Keyword(s):  
Sentiment Analysis ◽  
Text Analysis ◽  
Online Shopping ◽  
Rapid Development ◽  
Online Reviews ◽  
Analysis Method ◽  
Purchasing Decisions ◽  
Emotional Words ◽  
Fine Grained ◽  
The Relationship

AbstractWith the rapid development of online shopping, how to explore the value of online reviews, so as to give full play to their role in potential users’ purchasing decisions. Based on text mining and quantitative analysis, this paper studies the sentiment analysis of online reviews on B2C shopping website. The main attributes of commodity or service are extracted based on the order of word frequency in the online reviews. Text analysis method is used to judge the relationship between attributes of commodity or service and its emotional words. The fine-grained sentimental polarity and intensity of attributes are identified to analyze users’ concerns and preferences. The research shows that users pay more attention to the configuration and after-sales service of mobile, and have a positive sentimental orientation to most of attributes, especially unlocking function, hand feeling attribute and logistics service; and have a neutral sentimental orientation towards the attributes of battery and memory, and a negative sentimental orientation towards the membrane of mobile phone. The results can provide a reference for consumers to make purchasing decisions, for enterprises to improve product quality, and for shopping platform to optimize service.

A Supervised Fine-Grained Sentiment Analysis System for Online Reviews

2015 ◽  
Vol 21 (4) ◽  
pp. 589-605 ◽  
Author(s):  
Hanxiao Shi ◽  
Wenping Zhan ◽  
Xiaojun Li
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Fine Grained ◽  
Analysis System

scholarly journals Cross-Domain End-To-End Aspect-Based Sentiment Analysis with Domain-Dependent Embeddings

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yingjie Tian ◽  
Linrui Yang ◽  
Yunchuan Sun ◽  
Dalian. Liu
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Learning Strategy ◽  
Domain Adaptation ◽  
Online Reviews ◽  
Domain Model ◽  
Fine Grained ◽  
Cross Domain ◽  
End To End ◽  
Public Datasets

With the development of sentiment analysis, studies have been gradually classified based on different researched candidates. Among them, aspect-based sentiment analysis plays an important role in subtle opinion mining for online reviews. It used to be treated as a group of pipeline tasks but has been proved to be analysed well in an end-to-end model recently. Due to less labelled resources, the need for cross-domain aspect-based sentiment analysis has started to get attention. However, challenges exist when seeking domain-invariant features and keeping domain-dependent features to achieve domain adaptation within a fine-grained task. This paper utilizes the domain-dependent embeddings and designs the model CD-E2EABSA to achieve cross-domain aspect-based sentiment analysis in an end-to-end fashion. The proposed model utilizes the domain-dependent embeddings with a multitask learning strategy to capture both domain-invariant and domain-dependent knowledge. Various experiments are conducted and show the effectiveness of all components on two public datasets. Also, it is also proved that as a cross-domain model, CD-E2EABSA can perform better than most of the in-domain ABSA methods.

scholarly journals Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

2020 ◽  
Vol 47 (2) ◽  
pp. 105-121
Author(s):  
Wie Wei ◽  
Yi-Ping Liu ◽  
Lei-Ru Wei
Keyword(s):  
Sentiment Analysis ◽  
Academic Research ◽  
Online Reviews ◽  
Domain Ontology ◽  
Business Strategies ◽  
Product Reviews ◽  
Analysis Framework ◽  
Fine Grained ◽  
Product Features ◽  
Sentiment Dictionary

Mining product reviews and sentiment analysis are of great significance, whether for academic research purposes or optimizing business strategies. We propose a feature-level sentiment analysis framework based on rules parsing and fine-grained domain ontology for Chinese reviews. Fine-grained ontology is used to describe synonymous expressions of product features, which are reflected in word changes in online reviews. First, a semiautomatic construction method is developed by using Word2Vec for fine-grained ontology. Then, feature-level sentiment analysis that combines rules parsing and the fine-grained domain ontology is conducted to extract explicit and implicit features from product reviews. Finally, the domain sentiment dictionary and context sentiment dictionary are established to identify sentiment polarities for the extracted feature-sentiment combinations. An experiment is conducted on the basis of product reviews crawled from Chinese e-commerce websites. The results demonstrate the effectiveness of our approach.

Aspect-based sentiment analysis for online reviews with hybrid attention networks

2021 ◽  
Author(s):  
Yuming Lin ◽  
Yu Fu ◽  
You Li ◽  
Guoyong Cai ◽  
Aoying Zhou
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Attention Networks
Sign in / Sign up

Export Citation Format

Share Document