NDLI: Opinion mining from noisy text data

Content Provider	Springer Nature Link
Author	Dey, Lipika Haque, Sk. Mirajul
Copyright Year	2009
Abstract	The proliferation of Internet has not only led to the generation of huge volumes of unstructured information in the form of web documents, but a large amount of text is also generated in the form of emails, blogs, and feedbacks, etc. The data generated from online communication acts as potential gold mines for discovering knowledge, particularly for market researchers. Text analytics has matured and is being successfully employed to mine important information from unstructured text documents. The chief bottleneck for designing text mining systems for handling blogs arise from the fact that online communication text data are often noisy. These texts are informally written. They suffer from spelling mistakes, grammatical errors, improper punctuation and irrational capitalization. This paper focuses on opinion extraction from noisy text data. It is aimed at extracting and consolidating opinions of customers from blogs and feedbacks, at multiple levels of granularity. We have proposed a framework in which these texts are first cleaned using domain knowledge and then subjected to mining. Ours is a semi-automated approach, in which the system aids in the process of knowledge assimilation for knowledge-base building and also performs the analytics. Domain experts ratify the knowledge base and also provide training samples for the system to automatically gather more instances for ratification. The system identifies opinion expressions as phrases containing opinion words, opinionated features and also opinion modifiers. These expressions are categorized as positive or negative with membership values varying from zero to one. Opinion expressions are identified and categorized using localized linguistic techniques. Opinions can be aggregated at any desired level of specificity i.e. feature level or product level, user level or site level, etc. We have developed a system based on this approach, which provides the user with a platform to analyze opinion expressions crawled from a set of pre-defined blogs.
Starting Page	205
Ending Page	226
Page Count	22
File Format	PDF
ISSN	14332833
Journal	International Journal of Document Analysis and Recognition (IJDAR)
Volume Number	12
Issue Number	3
e-ISSN	14332825
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-08-21
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Noisy text Context-dependent cleaning Opinion mining WordNet Text analytics for market knowledge discovery Pattern Recognition Image Processing and Computer Vision
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Special issue on noisy text analytics

Special issue on noisy text analytics

Special issue on noisy text analytics

Supervised semantic relation mining from linguistically noisy text documents

Report from the AND 2009 working group on noisy text datasets

Finding structure in noisy text: topic classification and unsupervised clustering

Domain-specific entity extraction from noisy, unstructured data using ontology-guided search

Data mining for large scale 3D seismic data analysis

Evaluation of Localized Semantics: Data, Methodology, and Experiments

Opinion mining from noisy text data

Similar Documents

Special issue on noisy text analytics

Special issue on noisy text analytics

Special issue on noisy text analytics

Supervised semantic relation mining from linguistically noisy text documents

Report from the AND 2009 working group on noisy text datasets

Finding structure in noisy text: topic classification and unsupervised clustering

Domain-specific entity extraction from noisy, unstructured data using ontology-guided search

Data mining for large scale 3D seismic data analysis

Evaluation of Localized Semantics: Data, Methodology, and Experiments

Opinion mining from noisy text data