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dc.contributor.authorWandabwa, Hen_NZ
dc.contributor.authorNaeem, Men_NZ
dc.contributor.authorMirza, Fen_NZ
dc.date.accessioned2019-01-23T01:58:19Z
dc.date.available2019-01-23T01:58:19Z
dc.date.copyright2017-04-03en_NZ
dc.identifier.citationIn WWW '17 Companion Proceedings of the 26th International Conference on World Wide Web Companion, pp. 1423-1424.
dc.identifier.isbn978-1-4503-4914-7en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/12179
dc.description.abstractTwitter as an information dissemination tool has proved to be instrumental in generating user curated content in short spans of time. Tweeting usually occurs when reacting to events, speeches, about a service or product. This in some cases comes with its fair share of blame on varied aspects in reference to say an event. Our work in progress details how we plan to collect the informal texts, clean them and extract features for blame detection. We are interested in augmenting Recurrent Neural Networks (RNN) with self-developed association rules in getting the most out of the data for training and evaluation. We aim to test the performance of our approach using human-induced terror-related tweets corpus. It is possible tailoring the model to fit natural disaster scenarios.
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urihttps://dl.acm.org/citation.cfm?doid=3041021.3051157
dc.rights© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.
dc.subjectAspect extraction; Recurrent Neural Networks; Deep Learning; NLP
dc.titleAspect of Blame in Tweets: a Deep Recurrent Neural Network Approachen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1145/3041021.3051157
pubs.elements-id278904
aut.relation.conference26th International Conference on World Wide Web Companionen_NZ


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