Event detection in twitter by deep learning classification and multi label clustering virtual backbone formation.

The spread of social networking sites (SNSs) has led to the development of news and its spread. In terms of momentum produced content and topics, the gathered data from online services like twitter, are from a wide range of areas. Finding an abnormality pattern as well as content oriented planning in society, is the reason why event analysis can be of great importance. Event, in social networking sites is an interesting happening in real world which causes discussion on the topic about related issues by the users of SNSs like twitter, and it is released immediately after the discussion or with a delay. Event, changes the amount of textual data, in a way that it presents related topics in a specific time period. This event is identified by time and topic, and is related with entities like people and places. The computations related to event detection in real time is a big problem in this context. A model based on deep learning is presented in this paper. Firstly, according to the labeled data, classes are formed through classification. Later, in a flow manner, unlabeled data are presented to the model. The unlabeled data are divided into the present classes according to the model which they have been trained. If the data are higher than an identified threshold, they are assigned to a new class, and when the data are lower than the threshold, they are categorized as temporary event. Innovation of the proposed method is in two issues. First, the data in this model are semi-supervised; therefore, the labeled data are used in the first phase and the rest of the data are used in the second phase. In HAN classification phase, a module titled bag of sentence was produced for exact classification of sentences, and in the second phase the abstract concept of Virtual backbone was used to enhance precision of multi label clustering. Adding these two sections using the proposed method enhanced the precision of classification and purification of the data in unlabeled data. [ABSTRACT FROM AUTHOR]