Abstract
As the outgrowth of the internet as well as the social networks like twitter, Facebook user may get flooded out of raw information. Twitter message is short and may not contain enough contextual information, so traditional clustering method which utilizes the traditional method like “Bag-of-words” accept some restriction. To overwhelm with this trouble, we offered an automatic text classification process. Some social networking sites have imposed limits on the no of character for the users like twitter imposed limit of only 140 characters to the users to post any tweet. To classify these kinds of tweets is a tedious task or even impossible to classify. Short text is hard to sort out due to the lack of semantic information, therefore in this research paper, a novel approach is presented that incorporate the semantic database and utilized it to elicit the necessary features to separate the short text. Experimental results indicate that the proposed approach effectively classify the incoming tweets into predefined categories such as ‘News’, ‘Events’, ‘Personal message’ and ‘Deals’.
Highlights
Text classification is widely played very important roles in many application fields
We suggested a novel approach which is grounded on the formulation of the semantic data- set to classify the short text and experimental results indicate that the suggested methodology is making more accurate results as compared to the previous methodology
We have proposed a novel approach to classify the incoming tweets into predefine class by using the semantic knowledge
Summary
Text classification is widely played very important roles in many application fields. Close to popular social networking websites, twitter, restrict the users to only 140 characters and this led to the users to express their expressions or thoughts in less number of rows To automatically classify these short texts into predefine category is a tedious job because a short text contains less word co-occurrences and less contextual information which is not sufficient to separate. Classification of the short text is very complex and it became more complex in the field of social networking because people often use slang and synonyms to express their views and emotions To surmount this trouble, we suggested a novel approach which is grounded on the formulation of the semantic data- set to classify the short text and experimental results indicate that the suggested methodology is making more accurate results as compared to the previous methodology
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