Fine-grained opinion mining : An application of online review analysis in the express industry

2017 ◽  
Author(s):  
Zhibin Zhang ◽  
Hong Li ◽  
Wendong Yu
Keyword(s):  
Opinion Mining ◽  
Online Review ◽  
Fine Grained ◽  
Review Analysis ◽  
Express Industry

scholarly journals An Affordance-Based Online Review Analysis Framework

2019 ◽  
Vol 1 (1) ◽  
pp. 2457-2466
Author(s):  
Tianjun Hou ◽  
Bernard Yannou ◽  
Yann Leroy ◽  
Emilie Poirson
Keyword(s):  
Opinion Mining ◽  
Online Reviews ◽  
User Preferences ◽  
Data Driven ◽  
Online Review ◽  
Product Reviews ◽  
Analysis Framework ◽  
Review Analysis ◽  
Classical Models ◽  
Feedback Data

AbstractOne of the main tasks of today's data-driven design is to learn customers' concerns from the feedback data posted on the internet, to drive smarter and more profitable decisions during product development. Feature-based opinion mining was first performed by the computer and design scientists to analyse online product reviews. In order to provide more sophisticated customer feedback analyses and to understand in a deeper way customer concerns about products, the authors propose an affordance-based online review analysis framework. This framework allows understanding how and in what condition customers use their products, how user preferences change over years and how customers use the product innovatively. An empirical case study using the proposed approach is conducted with the online reviews of Kindle e-readers downloaded from amazon.com. A set of innovation leads and redesign paths are provided for the design of next-generation e-reader. This study suggests that bridging data analytics with classical models and methods in design engineering can bring success for data-driven design.

scholarly journals Analysis Methods in Neural Language Processing: A Survey

2019 ◽  
Vol 7 ◽  
pp. 49-72 ◽  
Author(s):  
Yonatan Belinkov ◽  
James Glass
Keyword(s):  
Language Processing ◽  
Network Models ◽  
Research Trends ◽  
Neural Network Models ◽  
Fine Grained ◽  
Survey Paper ◽  
Review Analysis ◽  
Analysis Methods ◽  
New Models ◽  
Future Work

The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work.

Semi-Supervised Aspect-Based Sentiment Analysis for Case-Related Microblog Reviews Using Case Knowledge Graph Embedding

2021 ◽  
pp. 2050012
Author(s):  
Peilian Zhao ◽  
Cunli Mao ◽  
Zhengtao Yu
Keyword(s):  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Opinion Mining ◽  
Data Augmentation ◽  
Training Data ◽  
Knowledge Graph ◽  
Fine Grained ◽  
Learning Framework ◽  
Proposed Model ◽  
Real World Applications

Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal field after data augmentation (DA). Specifically, we pre-trained the BERT embedding and in-domain KG embedding for unlabeled data and labeled data with case elements after DA, and then we put two embeddings into the E2E framework to classify the polarity of target-entity. Finally, we built a case-related dataset based on a popular benchmark for ABSA to prove the efficiency of Semi-ETEKGs, and experiments on case-related dataset from microblog comments show that our proposed model outperforms the other compared methods significantly.

FLORA: a hierarchical fuzzy system for online accommodation review analysis

2019 ◽  
Vol 21 (3) ◽  
pp. 347-367
Author(s):  
Thara Angskun ◽  
Jitimon Angskun
Keyword(s):  
Sentiment Analysis ◽  
Semantic Analysis ◽  
Online Reviews ◽  
Online Review ◽  
Probability Weighting ◽  
Content Type ◽  
Analysis Process ◽  
Review Analysis ◽  
Accuracy Comparison ◽  
Hierarchical Fuzzy System

Purpose This paper aims to introduce a hierarchical fuzzy system for an online review analysis named FLORA. FLORA enables tourists to decide their destination without reading numerous reviews from experienced tourists. It summarizes reviews and visualizes them through a hierarchical structure. The visualization does not only present overall quality of an accommodation, but it also presents the condition of the bed, hospitality of the front desk receptionist and much more in a snap. Design/methodology/approach FLORA is a complete system which acquires online reviews, analyzes sentiments, computes feature scores and summarizes results in a hierarchical view. FLORA is designed to use an overall score, rated by real tourists as a baseline for accuracy comparison. The accuracy of FLORA has achieved by a novel sentiment analysis process (as part of a knowledge acquisition engine) based on semantic analysis and a novel rating technique, called hierarchical fuzzy calculation, in the knowledge inference engine. Findings The performance comparison of FLORA against related work has been assessed in two aspects. The first aspect focuses on review analysis with binary format representation. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, is achieved with the highest values in precision, recall and F-measure. The second aspect looks at review analysis with a five-point rating scale rating by comparing with one of the most advanced research methods, called fuzzy domain ontology. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, returns the closest results to the tourist-defined rating. Research limitations/implications This research advances knowledge of online review analysis by contributing a novel sentiment analysis process and a novel rating technique. The FLORA system has two limitations. First, the reviews are based on individual expression, which is an arbitrary distinction and not always grammatically correct. Consequently, some opinions may not be extracted because the context free grammar rules are insufficient. Second, natural languages evolve and diversify all the time. Many emerging words or phrases, including idioms, proverbs and slang, are often used in online reviews. Thus, those words or phrases need to be manually updated in the knowledge base. Practical implications This research contributes to the tourism business and assists travelers by introducing comprehensive and easy to understand information about each accommodation to travelers. Although the FLORA system was originally designed and tested with accommodation reviews, it can also be used with reviews of any products or services by updating data in the knowledge base. Thus, businesses, which have online reviews for their products or services, can benefit from the FLORA system. Originality/value This research proposes a FLORA system which analyzes sentiments from online reviews, computes feature scores and summarizes results in a hierarchical view. Moreover, this work is able to use the overall score, rated by real tourists, as a baseline for accuracy comparison. The main theoretical implication is a novel sentiment analysis process based on semantic analysis and a novel rating technique called hierarchical fuzzy calculation.

scholarly journals Mining Review Unit Model for Online Review Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 196826-196834
Author(s):  
Qingxi Peng ◽  
Lan You ◽  
Qisheng Lu ◽  
Xiangyu Li
Keyword(s):  
Online Review ◽  
Review Analysis ◽  
Unit Model
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