This paper presents our experimental work on mining of opinions from a large number of blog posts and its relevance for socio-political research. The experimental work involves collecting blog data on three interesting topics, transforming the collected blog data into vector space representation, and then performing opinion mining using both a machine learning text classifier and an unsupervised semantic orientation approach. We implemented Naïve Bayes and SO-PMI-IR algorithms for opinion mining. We obtained interesting results, which have been evaluated for correctness and also cross-validated with the outcomes of multiple techniques employed. The paper concludes with a short discussion of the results and relevance of the experimental work. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2012.
CITATION STYLE
Singh, V. K., Mukherjee, M., Mehta, G. K., Garg, S., & Tiwari, N. (2012). Opinion mining from weblogs and its relevance for socio-political research. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 85, pp. 134–145). https://doi.org/10.1007/978-3-642-27308-7_14
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