Exploiting grammatical dependencies for fine-grained opinion mining

2010 ◽  
Author(s):  
Ritesh Srivastava ◽  
M. P. S. Bhatia ◽  
Hemant Kr Srivastava ◽  
C. P. Sahu
Keyword(s):  
Opinion Mining ◽  
Fine Grained ◽  
Grammatical Dependencies

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.

Multidimensional mining of public opinion in emergency events

2020 ◽  
Vol 38 (3) ◽  
pp. 545-560
Author(s):  
Qingqing Zhou ◽  
Ming Jing
Keyword(s):  
Public Opinion ◽  
Opinion Mining ◽  
Content Type ◽  
The Public ◽  
Fine Grained ◽  
Multiple Dimensions ◽  
Emergency Event ◽  
Comprehensive Information ◽  
Opinion Expression ◽  
Emergency Events

Purpose The suddenness, urgency and social publicity of emergency events lead to great impacts on public life. The deep analysis of emergency events can provide detailed and comprehensive information for the public to get trends of events timely. With the development of social media, users prefer to express opinions on emergency events online. Thus, massive public opinion information of emergencies has been generated. Hence, this paper aims to conduct multidimensional mining on emergency events based on user-generated contents, so as to obtain finer-grained results. Design/methodology/approach This paper conducted public opinion analysis via fine-grained mining. Specifically, public opinion about an emergency event was collected as experimental data. Secondly, opinion mining was conducted to get users’ opinion polarities. Meanwhile, users’ information was analysed to identify impacts of users’ characteristics on public opinion. Findings The experimental results indicate that public opinion is mainly negative in emergencies. Meanwhile, users in developed regions are more active in expressing opinions. In addition, male users, especially male users with high influence, are more rational in public opinion expression. Originality/value To the best of the authors’ knowledge, this is the first research to identify public opinion in emergency events from multiple dimensions, which can get in-detail differences of users’ online expression.

Fine-grained opinion mining by integrating multiple review sources

2010 ◽  
Vol 61 (11) ◽  
pp. 2288-2299 ◽  
Author(s):  
Qingliang Miao ◽  
Qiudan Li ◽  
Daniel Zeng
Keyword(s):  
Opinion Mining ◽  
Fine Grained

scholarly journals Spotting The Elusive Prospect Customer: Exploratory Study Of A Web-Powered Customer Relationship Management Framework

2015 ◽  
Vol 31 (5) ◽  
pp. 1935 ◽  
Author(s):  
Myriam Ertz ◽  
Raoul Graf
Keyword(s):  
Customer Relationship Management ◽  
Exploratory Study ◽  
Web Mining ◽  
Opinion Mining ◽  
Relationship Management ◽  
Online Marketing ◽  
Customer Relationship ◽  
Homogeneous Groups ◽  
Fine Grained ◽  
Managerial Implications

Research on how Web-Mining (WM) optimizes marketing, is sparse. Especially absent, is research on WM usefulness for Customer Relationship Management (CRM). The purpose of this research, is to propose a Web Mining-enabled knowledge acquisition framework for analytical CRM. An exploratory study consisting of eleven in-depth interviews with marketing scholars and practitioners revealed that, WM methods and techniques - currently available to practitioners - are well-suited for identifying the profile of web prospects according to their browsing behaviour and to classify them into homogeneous groups. Besides, the nascent technologies regarding opinion mining, sentiment analysis or natural language parsing, and which underlie WM, seem sufficient to acquire knowledge pertaining to attitudinal and other more psychometrically-based characteristics about web prospects. Such tools enable to better understand the so-often termed elusive prospects, by crafting fine-grained online marketing strategies to acquire those would-be customers. The authors discuss the managerial implications that derive from these findings.

scholarly journals Transferable Interactive Memory Network for Domain Adaptation in Fine-Grained Opinion Extraction

2019 ◽  
Vol 33 ◽  
pp. 7192-7199 ◽  
Author(s):  
Wenya Wang ◽  
Sinno Jialin Pan
Keyword(s):  
Opinion Mining ◽  
Domain Adaptation ◽  
Global Memory ◽  
Target Space ◽  
Target Domain ◽  
Fine Grained ◽  
Opinion Extraction ◽  
Benchmark Datasets ◽  
Memory Network ◽  
Syntactic Relations

In fine-grained opinion mining, aspect and opinion terms extraction has become a fundamental task that provides key information for user-generated texts. Despite its importance, a lack of annotated resources in many domains impede the ability to train a precise model. Very few attempts have applied unsupervised domain adaptation methods to transfer fine-grained knowledge (in the word level) from some labeled source domain(s) to any unlabeled target domain. Existing methods depend on the construction of “pivot” knowledge, e.g., common opinion terms or syntactic relations between aspect and opinion words. In this work, we propose an interactive memory network that consists of local and global memory units. The model could exploit both local and global memory interactions to capture intra-correlations among aspect words or opinion words themselves, as well as the interconnections between aspect and opinion words. The source space and the target space are aligned through these domaininvariant interactions by incorporating an auxiliary task and domain adversarial networks. The proposed model does not require any external resources and demonstrates promising results on 3 benchmark datasets.

Opinion Mining and Sentiment Analysis

2017 ◽  
Author(s):  
Eric Breck ◽  
Claire Cardie
Keyword(s):  
Sentiment Analysis ◽  
Question Answering ◽  
Opinion Mining ◽  
Sports Fans ◽  
Fine Grained ◽  
Opinion Analysis ◽  
Opinion Summarization ◽  
Automatic Methods ◽  
Analysis System ◽  
Compositional Approaches

Opinions are ubiquitous in text, and readers of online text—from consumers to sports fans to news addicts to governments—can benefit from automatic methods that synthesize useful opinion-oriented information from the sea of data. In this chapter on opinion mining and sentiment analysis, we introduce an idealized, end-to-end opinion analysis system and describe its components. We present methods for classifying documents and text passages according to their sentiment as well as methods that perform more fine-grained extraction of opinion expressions, their holders and their targets. We also address supplementary tasks of opinion lexicon construction, opinion summarization, opinion-oriented question answering, multi-lingual sentiment analysis and compositional approaches to phrase-level sentiment analysis.

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