Abstract
With explosive growth of the Internet, the amount of information in text form is growing rapidly and the demand for data analysis is also increases. We can perform sentiment analysis on a large set of text messages to discover valuable knowledge and obtain enormous benefits in national security, business, politics, economics, etc. However, text messages from the social networks are rather different from those of traditional text documents. Therefore, it is difficult but essential to develop an effective method of sentiment exploration in social networks. In this paper we first applied a neural network model, namely the self-organizing maps, to cluster similar messages and sentiment keywords, respectively. We then developed an association discovery process to find the associations between a message and some sentiment keywords. The sentiment of a message is then determined according to such associations. We performed experiments on Twitter messages and obtained promising results.
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Yang, HC., Lee, CH., Wu, CY., Huang, YC. (2014). Discovering Sentiment of Social Messages by Mining Message Correlations. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume I. Advances in Intelligent Systems and Computing, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-319-07776-5_23
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DOI: https://doi.org/10.1007/978-3-319-07776-5_23
Publisher Name: Springer, Cham
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