An improved deep belief neural network based civil unrest event forecasting in twitter.

Nowadays, event forecasting in Twitter can be considered an essential, significant and difficult issue. Maximum conventional methods are focusing on temporal events like sports or elections. These methods do not calculate the spatial features too their correlation analysis. Hence, this paper proposes an Improved Deep Belief Neural Network (iDBNN) for civil unrest event forecasting in twitter data. This proposed method is utilized to forecast the future event with the consideration of the tweets. The proposed method is designed with three phases named as pre-processing phase, feature extraction phase, and civil unrest event forecasting. Initially, the proposed method is used to train the Hong Kong Protest event 2019 tweet data for forecasting events. In the pre-processing phase, removal of special symbol, removal of URL, username removal, tokenization and stop word removal are done. After that, the essential features such as domain weight, event weight, textual similarity, spatial similarity, temporal similarity, and Relative Document-Term Frequency Difference (RDTFD) are extracted and then applied for training the proposed model. To empower the training phase of proposed iDBNN method, the Jellyfish Algorithm is utilized to select optimal weight parameter coefficients of DBNN for training the model parameters. The projected technique is authenticated by statistical capacities and compared with the conventional methods such as Hidden Markov Model (HMM) and Random Forest (RF) respectively. Comparing with other traditional methods, the proposed model shows better performance in terms of prediction and processing time. The iDBNN model shows 91% prediction accuracy that is much higher than the traditional DBNN. [ABSTRACT FROM AUTHOR]