Automatic extraction and classification approach of opinions in texts

Abstract

In this paper, we present an approach to automatically extract and classify opinions in texts. We propose a similarity measurement calculating semantically distances between a word and predefined subgroups of seed words. We have evaluated our algorithm on the semantic evaluation company "SemEval 2007" corpus, and we obtained the best value of Precision and F1 62% and 61%. As an improvement of 20 % compared to others participants.

Feature Based Opinion Classification (Fboc) of Customer Reviews

“…They used deep learning approaches for both recognition and classing of aspects and a preprocessing phase that uplift the results for aspect recognition. (Bouchlaghem et al, 2010) proposed a technique for extracting and classifying opinions on the basis of semantic similarities between words. Their technique consists of three stages for classifying the opinion in a text passage, starting with the pre-processing process which consists of splitting of text and tagging its words.…”

Tunisian dialect Wordnet creation and enrichment using web resources and other Wordnets

“…The NLP approaches generally applied lexical resources for the target language. Such resources are useful in several tasks which involve a language meaning understanding like: opinion mining (Kim et al, 2004;Bouchlaghem et al 2010), information retrieval (Valeras et al, 2005;Rosso et al, 2004), query expansion (Parapar et al, 2005), text categorization (Rosso et al, 2004;Ramakrishnan et al, 2003), and many other applications. However, this situation poses significant difficulties in the context of dialectal data because of the huge lack of Dialect-Standard Arabic lexical resources.…”