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Review Web Pages Collector Tool for Thematic Corpus Creation

9 pagesPublished: November 28, 2016

Abstract

We present a method of automaticaly extracting and gathering specific data text from web pages, creating a thematic corpus of reviews for opinion mining and sentiment analysis. The internet is an immense source of machine-readable texts \cite{mcenery1996} suitable for linguistic corpus studies\cite{Fletcher04}\cite{Kilgarriff2003}. Though, specific tools of web information extraction research domain as well as from the NLP do not include an open source system able to provide a thematic corpus according to an end-user request\cite{Sharoff2006}.\\ The need of use natural texts as databank for opinion mining and sentiment analysis is increased since the expansion of the digital interaction between users and blogs, forums and social networks.\\ The RevScrap system is designed to provide an intuitive, easy-to-use interface able to extract specific information from accurate web pages returned by search engine's request and create a corpus composed by comments, reviews, opinions, as expressed by users' experience and feedback. The corpus is well structured in xml documents, reflected Singler's design criteria\cite{sinclair01}..

Keyphrases: corpus design, opinion mining, thematic corpus

In: Antonio Moreno Ortiz and Chantal Pérez-Hernández (editors). CILC2016. 8th International Conference on Corpus Linguistics, vol 1, pages 274-282.

BibTeX entry
@inproceedings{CILC2016:Review_Web_Pages_Collector,
  author    = {Lisa Medrouk and Anna Pappa and Jugurtha Hallou},
  title     = {Review Web Pages Collector Tool for Thematic Corpus Creation},
  booktitle = {CILC2016. 8th International Conference on Corpus Linguistics},
  editor    = {Antonio Moreno Ortiz and Chantal Pérez-Hernández},
  series    = {EPiC Series in Language and Linguistics},
  volume    = {1},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5283},
  url       = {/publications/paper/4hFq},
  doi       = {10.29007/qcjn},
  pages     = {274-282},
  year      = {2016}}
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