SentiML ++: an extension of the SentiML sentiment annotation scheme

Abstract

ABSTRACT The amount of opinionated data on the web has exponentially increased especially after the emergence of online social networks. To deal with these huge deluge of data, we need to have robust mechanisms that can help identify all aspects of opinion segment and support the automatic processing of opinion data. Recently, there have been a few developments made in this direction, and different sentiment annotation schemes have been proposed such as the SentiML, OpinionMiningML, and EmotionML. In this work, we propose SentiML++, an extension of SentiML with a focus on annotating opinions, and further answering aspects of the general question “who has what opinion about whom in which context?”. A detailed comparison with SentiML and other existing annotation schemes is also presented. The data collection annotated with SentiML has been annotated with SentiML++ and is available for download for further research purposes. Experiments with data collections annotated with SentiML and SentiML++ proves that SentiML++ is a significant and valuable addition to SentiML.