Aspect Based Sentiment Analysis (ABSA) aims at collecting detailed opinion information according to products and their features, via the recognition of targets of the opinions in text. Though some annotated data have been produced in challenges as SemEval, resources are still scarce, especially for languages other than English. We are interested in enhancing today’s mostly statistical text classification with the use of linguistics tools, in order to better define and analyze what has been written. The work presented in this paper focuses on two French datasets of movies and books online reviews. In reviews, text length is much higher compared to a tweet, giving us the opportunity to work on a challenging and linguistically interesting dataset. Moreover, movies and books are products that make classifying opinions into aspects quite complex. This article provides an analysis of the particularities of the two domains during the process of collecting and annotating data, a precise annotation scheme for each domain, examples and statistics issued from the annotation phase, and some perspectives on our future work.
@InProceedings{PECORE18.449, author = {Stefania Pecore and Jeanne Villaneau}, title = "{Complex and Precise Movie and Book Annotations in French Language for Aspect Based Sentiment Analysis}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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