Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Improving pronoun resolution using statistics-based semantic compatibility information
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Exploiting domain structure for named entity recognition
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Extracting Product Comparisons from Discussion Boards
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Mining comparative sentences and relations
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Mining opinions in comparative sentences
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
An empirical approach to the interpretation of superlatives
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Feature selection & dominant feature selection for product reviews using meta-heuristic algorithms
Proceedings of the Third Annual ACM Bangalore Conference
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Resolving object and attribute coreference in opinion mining
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Automatic extraction of destinations, origins and route parts from human generated route directions
GIScience'10 Proceedings of the 6th international conference on Geographic information science
LivePulse: tapping social media for sentiments in real-time
Proceedings of the 20th international conference companion on World wide web
LCI: a social channel analysis platform for live customer intelligence
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Latent aspect rating analysis without aspect keyword supervision
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Sentiment analysis on twitter data for portuguese language
PROPOR'12 Proceedings of the 10th international conference on Computational Processing of the Portuguese Language
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Identifying helpful reviews based on customer's mentions about experiences
Expert Systems with Applications: An International Journal
Aspect and sentiment extraction based on information-theoretic co-clustering
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Identifying helpful online reviews: A product designer's perspective
Computer-Aided Design
Techniques and applications for sentiment analysis
Communications of the ACM
Deriving market intelligence from microblogs
Decision Support Systems
Hi-index | 0.02 |
Opinion mining became an important topic of study in recent years due to its wide range of applications. There are also many companies offering opinion mining services. One problem that has not been studied so far is the assignment of entities that have been talked about in each sentence. Let us use forum discussions about products as an example to make the problem concrete. In a typical discussion post, the author may give opinions on multiple products and also compare them. The issue is how to detect what products have been talked about in each sentence. If the sentence contains the product names, they need to be identified. We call this problem entity discovery. If the product names are not explicitly mentioned in the sentence but are implied due to the use of pronouns and language conventions, we need to infer the products. We call this problem entity assignment. These problems are important because without knowing what products each sentence talks about the opinion mined from the sentence is of little use. In this paper, we study these problems and propose two effective methods to solve the problems. Entity discovery is based on pattern discovery and entity assignment is based on mining of comparative sentences. Experimental results using a large number of forum posts demonstrate the effectiveness of the technique. Our system has also been successfully tested in a commercial setting.