We introduce the challenging task of detecting changes from an online conversation. Our goal is to detect significant changes in, for example, sentiment or topic in a stream of messages that are part of an ongoing conversation. Our approach relies on first applying linguistic preprocessing or collecting simple statistics on the messages in the conversation in order to build a time series. Change point detection algorithms are then applied to identify the location of significant changes in the distribution of the underlying time series. We present a collection of sports events on which we can evaluate the performance of our change detection method. Our experiments, using several change point detection algorithms and several types of time series, show that it is possible to detect salient changes in an on-line conversation with relatively high accuracy.
@InProceedings{GOUTTE18.335, author = {Cyril Goutte and Yunli Wang and FangMing Liao and Zachary Zanussi and Samuel Larkin and Yuri Grinberg}, title = "{EuroGames16: Evaluating Change Detection in Online Conversation}", 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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