Ubiquitous online communication is producing massive amounts of data on an un-precedential scale. Many have come up in analyzing the data produced from Twitter. Sentiment analysis is being carried out to catch the pulse of the people. As the data is mostly in the unstructured format. Getting the summary of what has been expressed positively and what has been expressed negatively plays a major role. Summaries take a wide strand in this plait as the relevant content can't be comprehended all at once. Working on the premise that online social media conversations might represent a new source of information to monitor the status of the policies launched by the government, this investigate stands as a first paw which may ultimately be the source for producing abstractive summaries from Twitter. This paper mainly focuses on developing a hybrid method which takes the combination of extractive summaries using statistical approaches as well as abstractive summaries using Rich Semantic Graph based approaches. The set of classified positive and the negative tweets will be passed as input to the hybrid method and it generates concise as well as readable summaries from both set of tweets. These summaries can be easily viewable on the PDA's and enhances the decision making capability of the policymaker.
The outlier detection technique is widely used in the data analysis for the clustering of data. Many techniques have been applied in the outlier detection to increase the efficiency of the data analysis. The Local Projection based Outlier Detection (LPOD) method effectively identifies neighbouring values of data, but this has the drawback of random selection of the cluster centre that affects the overall clustering performance of the system. In this study, the Adaptive Clustering by Fast Search and Find of Density Peak (ACFSFDP) is proposed to select the clustering centre and density peak. This ACFSFDP method is implemented with the min-max algorithm to find the number of categories that measured the local density and distance information. The density and distance are used to select the cluster centre, but density is not calculated on the existing distance based clustering techniques. The ACFSFDP method calculates cluster centre based on the density and distance during the clustering process, whereas the existing techniques randomly select the data centre. The results indicated that the ACFSFDP method is provided effective outlier detection compared with existing Clustering by Fast Search and Find of Density Peak (CFSFDP) methods. The ACFSFDP is tested on two datasets Pen-digits and waveform datasets. The experiment results proved that Area Under Curve (AUC) of the ACFSFDP is 99.08% on the Pen-Digit dataset, while the existing distance classifier method k-Nearest Neighbour has achieved 68.7% of AUC.
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