Foundations of statistical natural language processing
Foundations of statistical natural language processing
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Measuring semantic similarity in the taxonomy of WordNet
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
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
HHMM-based Chinese lexical analyzer ICTCLAS
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Hownet And the Computation of Meaning
Hownet And the Computation of Meaning
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Measuring semantic similarity between words using web search engines
Proceedings of the 16th international conference on World Wide Web
A Graph Modeling of Semantic Similarity between Words
ICSC '07 Proceedings of the International Conference on Semantic Computing
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Gather customer concerns from online product reviews - A text summarization approach
Expert Systems with Applications: An International Journal
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Opinion extraction and summarization on the web
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Opinion digger: an unsupervised opinion miner from unstructured product reviews
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Clustering product features for opinion mining
Proceedings of the fourth ACM international conference on Web search and data mining
Sentiment classification of Internet restaurant reviews written in Cantonese
Expert Systems with Applications: An International Journal
Experiments with SVM to classify opinions in different domains
Expert Systems with Applications: An International Journal
Electronic word of mouth analysis for service experience
Expert Systems with Applications: An International Journal
More than words: Social networks' text mining for consumer brand sentiments
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Finding the weakness of the products from the customers' feedback can help manufacturers improve their product quality and competitive strength. In recent years, more and more people express their opinions about products online, and both the feedback of manufacturers' products or their competitors' products could be easily collected. However, it's impossible for manufacturers to read every review to analyze the weakness of their products. Therefore, finding product weakness from online reviews becomes a meaningful work. In this paper, we introduce such an expert system, Weakness Finder, which can help manufacturers find their product weakness from Chinese reviews by using aspects based sentiment analysis. An aspect is an attribute or component of a product, such as price, degerm, moisturizing are the aspects of the body wash products. Weakness Finder extracts the features and groups explicit features by using morpheme based method and Hownet based similarity measure, and identify and group the implicit features with collocation selection method for each aspect. Then utilize sentence based sentiment analysis method to determine the polarity of each aspect in sentences. The weakness of product could be found because the weakness is probably the most unsatisfied aspect in customers' reviews, or the aspect which is more unsatisfied when compared with their competitor's product reviews. Weakness Finder has been used to help a body wash manufacturer find their product weakness, and our experimental results demonstrate the good performance of the Weakness Finder.