Periods, capitalized words, etc.
Computational Linguistics
OPINE: extracting product features and opinions from reviews
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
The utility of linguistic rules in opinion mining
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Successfully detecting and correcting false friends using channel profiles
Proceedings of the second workshop on Analytics for noisy unstructured text data
A comparative study of statistical features of language in blogs-vs-splogs
Proceedings of the second workshop on Analytics for noisy unstructured text data
Opinion mining from noisy text data
Proceedings of the second workshop on Analytics for noisy unstructured text data
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Statistical machine translation of texts with misspelled words
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Linking online news and social media
Proceedings of the fourth ACM international conference on Web search and data mining
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Text mining aims at deriving high quality information from text in an automated way. Text mining applications rely on Natural Language Processing (NLP) tools like tagger, parser etc. to locate and retrieve relevant information in an application specific manner. Most of these NLP tools however have been designed to work on clean and grammatically correct text. Presently, many organizations are interested to derive information from informally written text that is generated as a result of human communication through emails, or blog posts, web-based reviews etc. These texts are highly noisy and often found to contain mixture of languages. In this study we present some analysis on how noise introduced due to incorrect English affects the performance of some of the NLP tools and thereafter the text mining applications. The text mining application that we focus on is opinion mining. Opinion mining is the most significant text mining application that has to deal with noisy text generated in an unregulated fashion by users.