An Intelligent Data Mining System for Tweet Opinion Analysis using Combined Cluster based Classification Approach
Keywords:
Cluster-based classification, Machine learning, Miner architecture, Supervised learning, Support vector machine, Intelligent Systems.Abstract
We're all driven by the need to be heard and to be able to convey our thoughts and ideas clearly. We want to know about and discuss specific issues; in other words, the right to be enlightened and the need to make the proper decisions and choices. It's no secret that microblogging services like Twitter and Facebook are bursting to the seams with information. There is no longer a distinction between "read only" and "read-write" content on the web anymore. As a result of the information contained in micro-blogging posts, comments, and ratings, opinion mining data is extracted from these data points. Sentiment analysis is to identify the ideas, feelings, and attitudes expressed in the source material. This article presents a comparison with similar existing approaches. 85.92 percent accuracy and 82.35% success rate were achieved by the minor architecture. There are F measures of 84.99 percent and the proposed architecture works only with tweets in the English language. In the future, the research will focus on designing an architecture capable of handling many languages. Language to language the accuracy may be different which is uncertain, but when the work is carried out and compared with the state of art of existing approaches our proposed approach seems to be better. When the two methods indicated above are combined, they produce superior results in complicated opinion mining compared to state-of-the-art procedures.
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