Hybrid Kmeans with Improved Bagging for Semantic Analysis of Tweets on Social Causes

Analysis of public information from social media could yield fascinating outcomes and bits of knowledge into the universe of general conclusions about any item, administration, or identity. Social network data is one of the most effective and accurate indicators of public sentiment. Analysis of the mood of public on a particular social issue can be easily judged by several methods developed by the technicians. In this paper, analysis of the mood of society towards any particular news from the twitter post in form of tweets. The key objective behind this research is to increase the accuracy and effectiveness of the classification by the process of the NLP that is Natural Language Processing Techniques while focusing on semantics and World Sense Disambiguation. The process of classification includes the combination of the effect of various independent classifiers on one particular classification problem. The data that is available in the form of tweets on twitter can easily frame the insight of the public attitude towards the particular tweet. The proposed work is well planned to design as well as implement the best hybrid method that includes Hybrid Kmeans/Modified Kmeans (MKmeans) that involves clustering and Bagging for sentiment analysis. With this proposed idea one can easily understand the behavior of the public towards the post and further assist in the future policy making taking the results as the basis. At the end results are compared with the existing model with the motive of validating the findings.