Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
The Journal of Machine Learning Research
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Needs-based analysis of online customer reviews
Proceedings of the ninth international conference on Electronic commerce
Query-level loss functions for information retrieval
Information Processing and Management: an International Journal
Gather customer concerns from online product reviews - A text summarization approach
Expert Systems with Applications: An International Journal
Entity discovery and assignment for opinion mining applications
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
TSA'09 workshop summary: topic-sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Overview of the 2nd international workshop on search and mining user-generated contents
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Hi-index | 0.00 |
Online product reviews are a reliable source of customers' sentiments. Directly connecting with customers and designers, online reviews can shorten product development life cycles. The problem arising is, although different techniques for processing online reviews are developed, the techniques are rarely seen on accelerating the design work flows. This paper proposes a two stage framework to learn the importance of characteristics from online reviews which could benefit product design. The first stage is a supervised learning routine to identify product characteristics. This procedure calculates the surrounding words' posterior probability. A linear weight learning algorithm is subsequently shown to reach the product characteristics identification. The second stage focus on meeting customers' needs. Distinct from existing classification and rank algorithms, this stage informs an ordinal classification algorithm to balance the weight of product characteristics. This algorithm depicts a pairwise approach to achieve ordinal classification. Furthermore, an integer none linear programming model is advised, which targets at recovering pairwise results to the original class for each object. Four brands of printer reviews from Amazon and real analysis from two experienced product designers are employed in this experimental study. The results validate the feasibility of the two stage framework, and the possibility to explore targeted models from online reviews for product designers.