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Abstract
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Now a days social networking sites are becoming the real time information channel over time. With the help of portable devices many users are able to share the real life events on the internet. This feature of the social networking sites made them more popular and valuable. Basically these social networking sites are used for the maintaining social relationship, finding friends and users with the similar interest .The text message shared by user in social networks that message is called Status Update Message. In that SUM the text, meta-information like timestamp, name of the user, geographic coordinates, links of the other resources are present. The SUM's considered in a specific area may provide the proper information.
Social networks is a source of information for event detection such as road traffic congestion and car accidents. Existing system present a real-time monitoring system for traffic event detection from twitter. The system fetches or collect tweets from twitter and then processes tweets using text mining techniques. Lastly performs the classification of tweets. The system aim is to assign the appropriate class label to each tweet, whether it is related to a traffic event or not. System used the support vector machine as a classification model.
The proposed system uses the semi-supervised approach, which gives training using traffic related dataset. We propose a clustering approach for classification of the tweets in traffic related and non- traffic related tweets. We employ a Euclidean distance to calculate the similarity between the tweets.
Keywords: Tweet classification, Traffic event detection, Data mining, text mining, and social sensing.
Keywords: Twitter-Traffic-Status
Language: en
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