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We show that the consistency problem is NP-complete.

We reduce the polarity consistency problem to the satisfiability problem and utilize two fast SAT solvers to detect inconsistencies in a sentiment dictionary.

Yesterday I blogged about Web Optimizer, a minifier that Mads Kristensen wrote for ASP. A few people mentioned that Shannon Deminick also had a great minifier for ASP. Shannon has a number of great libraries on his Git Hub https://github.com/Shazwazza including not just "Smidge" but also Examine, an indexing system, Client Dependency for managing all your client side assets, and Articulate, a blog engine built on Umbraco. Scott Hanselman is a former professor, former Chief Architect in finance, now speaker, consultant, father, diabetic, and Microsoft employee.

Often when there's more than one way to do things, but one of the ways is made by a Microsoft employee like Mads - even if it's in his spare time - it can feel like inside baseball or an unfair advantage. He is a failed stand-up comic, a cornrower, and a book author.

Smidge has been around as a runtime bundler since the beginning of ASP.

NET Core even back when DNX was a thing, if you remember that. NET Core has evolved, and it's under active development.

Besides obvious instances, where the same word appears with different polarities in different dictionaries, the dictionaries exhibit complex cases of polarity inconsistency, which cannot be detected by mere manual inspection.

We introduce the concept of polarity consistency of words/senses in sentiment dictionaries in this paper.

Watermarking is advocated to enforce ownership rights over shared relational data and for providing a means for tackling data tampering.

When ownership rights are enforced using watermarking, the underlying data undergoes certain modifications; as a result of which, the data quality gets compromised.

Finally, candidates with higher confidence are extracted as opinion targets or opinion words.

Compared to previous methods based on the nearest-neighbor rules, our model captures opinion relations more precisely, especially for long-span relations.

We perform experiments on five sentiment dictionaries and Word Net to show inter- and intra-dictionaries inconsistencies.

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