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Natural language Processing (NLP) is a field of artificial intelligence and computational linguistics concerned with interaction of humans with machines. Social Networking sites (SNS) is a widely used communication tool today. Sometimes it gives rise to the problem of flaming. A flaming incident is triggered by comments and action made by user in SNS that ends up damaging reputation or causing negative impact on the target party. The paper proposes an architecture to overcome this problem. The training dataset used contains a total of 17282 tweets already annotated as positive, negative and neutral. The test dataset that has been used contains a total of 4382 tweets already annotated as positive, negative and neutral. Result of machine learning techniques used in the proposed architecture are reported and discussed in this paper.

An Approach to Overcome Flaming Problem of Social Networking Sites Using Various Classifiers