The topics associated to the KMi publications listed in this page were automatically generated using the CSO Classifier, a solution developed by the SKM3 team in KMi. This technology has also been adopted by Springer Nature and is used routinely by them to generate automatically the metadata for all Computer Science conference proceedings they publish.
He, Y., Lin, C., Gao, W. and Wong, K.F. (2017). Tracking Sentiment and Topic Dynamics from Social Media. In: Wong, Kam-Fai; Gao, Wei; Xu, Ruifeng and Li, Wenjie eds. Social Media Content Analysis Natural Language Processing and Beyond. Series on Language Processing, Pattern Recognition, and Intelligent Systems:, 5. World Scientific, pp. 457–465. https://oro.open.ac.uk/52178/.
Uren, V., Wright, D., Scott, J., He, Y. and Saif, H. (2016). Social media and sentiment in bioenergy consultation. International Journal of Energy Sector Management, 10(1), pp. 87–98. https://oro.open.ac.uk/45439/.
Burel, G., Mulholland, P., He, Y. and Alani, H. (2015). Predicting Answering Behaviour in Online Question Answering Communities. In: 26th ACM Conference on Hypertext and Social Media, 1-4 Sep 2015, Cyprus. https://oro.open.ac.uk/44262/.
Burel, G. and He, Y. (2014). Quantising contribution effort in online communities. In: 23rd International Conference on World Wide Web, 7-11 Apr 2014, Seoul, Korea. https://oro.open.ac.uk/44017/.
Burel, G., He, Y., Mulholland, P. and Alani, H. (2015). Modelling Question Selection Behaviour in Online Communities. In: 2015 International Conference on the World Wide Web, 18-22 May 2015, Florence, Italy. https://oro.open.ac.uk/44014/.
Huynh, T., He, Y. and Rüger, S. (2015). Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis. In: 37th European Conference on IR Research (ECIR 2015), 29 Mar - 2 Apr 2015, Vienna. https://oro.open.ac.uk/43265/.
Saif, H., He, Y., Fernández, M. and Alani, H. (2016). Contextual semantics for sentiment analysis of Twitter. Information Processing and Management, 52(1), pp. 5–19. https://oro.open.ac.uk/42471/.
Cano Basave, A.E., He, Y., Liu, K. and Zhao, J. (2013). A weakly supervised Bayesian model for violence detection in social media. In: 6th International Joint Conference on Natural Language Processing, 14-18 Oct 2013, Nagoya, Japan. https://oro.open.ac.uk/41416/.
Varga, A., Cano Basave, A.E., Rowe, M., Ciravegna, F. and He, Y. (2014). Linked knowledge sources for topic classification of microposts: a semantic graph-based approach. Journal of Web Semantics: Science, Services and Agents on the World Wide Web, 26 pp. 36–57. https://oro.open.ac.uk/41414/.
Cano Basave, A.E., He, Y. and Xu, R. (2014). Automatic labelling of topic models learned from Twitter by summarisation. In: 52nd Annual Meeting of the Association for Computational Linguistics, 22-27 Jun 2014, Baltimore, MD, USA. https://oro.open.ac.uk/41413/.
Saif, H., He, Y., Fernández, M. and Alani, H. (2014). Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter. In: Workshop 5: SemanticSentimentAnalysis2014: Semantic Web and Sentiment Analysis, 25-19 May 2014, Crete, Greece. https://oro.open.ac.uk/41401/.
Saif, H., He, Y., Fernández, M. and Alani, H. (2014). Semantic patterns for sentiment analysis of Twitter. In: 13th International Semantic Web Conference (ISWC 2014), 19-23 Oct 2014, Riva del Garda, Trentino, Italy. https://oro.open.ac.uk/41399/.
Cano, A.E., He, Y. and Alani, H. (2014). Stretching the life of Twitter classifiers with time-stamped semantic graphs. In: 13th International Semantic Web Conference (ISWC 2014), 19-23 Oct 2014, Riva del Garda, Trentino, Italy. https://oro.open.ac.uk/41402/.
Cano, A., He, Y. and Alani, H. (2014). The topics they are a-changing - characterising topics with time-stamped semantic graphs. In: 13th International Semantic Web Conference (ISWC 2014), 19-23 Oct 2014, Riva del Garda, Trentino, Italy. https://oro.open.ac.uk/41404/.
Saif, H., Fernández, M., He, Y. and Alani, H. (2014). On stopwords, filtering and data sparsity for sentiment analysis of Twitter. In: LREC 2014, Ninth International Conference on Language Resources and Evaluation, 26-31 May 2014, Reykjavik, Iceland. https://oro.open.ac.uk/40666/.
Saif, H., Fernández, M., He, Y. and Alani, H. (2014). SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter. In: 11th ESWC 2014, 25-29 May 2014, Crete, Greece. https://oro.open.ac.uk/40661/.
Saif, H., Fernández, M., He, Y. and Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold. In: 1st Interantional Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI (ESSEM 2013), 3 Dec 2013, Turin, Italy. https://oro.open.ac.uk/40660/.
He, Y., Saif, H., Wei, Z. and Wong, K.F. (2012). Quantising opinions for political tweets analysis. In: LREC 2012, Eighth International Conference on Language Resources and Evaluation, 21-27 May 2012, Istanbul, Turkey. https://oro.open.ac.uk/40659/.
Saif, H., He, Y. and Alani, H. (2011). Semantic smoothing for Twitter sentiment analysis. In: 10th International Semantic Web Conference (ISWC 2011), 23-27 Oct 2011, Bonn, Germany. https://oro.open.ac.uk/38502/.
Saif, H., He, Y. and Alani, H. (2012). Alleviating data sparsity for Twitter sentiment analysis. In: 2nd Workshop on Making Sense of Microposts (#MSM2012): Big things come in small packages at the 21st International Conference on theWorld Wide Web (WWW'12), 16 Apr 2012, Lyon, France. https://oro.open.ac.uk/38501/.
