This paper introduces the three SSIX corpora for sentiment analysis. These corpora address the need to provide annotated data for supervised learning methods. They focus on stock-market related messages extracted from two financial microblog platforms, i.e., StockTwits and Twitter. In total they include 2,886 messages with opinion targets. These messages are provided with polarity annotation set on a continuous scale by three or four experts in each language. The annotation information identifies the targets with a sentiment score. The annotation process includes manual annotation verified and consolidated by financial experts. The creation of the annotated corpora took into account principled sampling strategies as well as inter-annotator agreement before consolidation in order to maximize data quality.
@InProceedings{GAILLAT18.564, author = {Thomas Gaillat and Manel Zarrouk and André Freitas and Brian Davis}, title = "{The SSIX Corpora: Three Gold Standard Corpora for Sentiment Analysis in English, Spanish and German Financial Microblogs}", 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} }
Summary of the paper