Real-Time Unspecified Major Sub-Events Detection in the Twitter Data Stream That Cause the Change in the Sentiment Score of the Targeted Event

Twitter behaves as a social sensor of the world. The tweets provided by the Twitter Firehose reveal the properties of big data (i.e. volume, variety, and velocity). With millions of users on Twitter, the Twitter's virtual communities are now replicating the real-world communities. Consequently, the discussions of real world events are also very often on Twitter. This work has performed the real-time analysis of the tweets related to a targeted event (e.g. election) to identify those potential sub-events that occurred in the real world, discussed over Twitter and cause the significant change in the aggregated sentiment score of the targeted event with time. Such type of analysis can enrich the real-time decision-making ability of the event bearer. The proposed approach utilizes a three-step process: (1) Real-time sentiment analysis of tweets (2) Application of Bayesian Change Points Detection to determine the sentiment change points (3) Major sub-events detection that have influenced the sentiment of targeted event. This work has experimented on Twitter data of Delhi Election 2015.

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Significance of Parallel Computing on the Performance of Digital Image Correlation Algorithms in MATLAB

Designs ◽

10.3390/designs5010015 ◽

2021 ◽

Vol 5 (1) ◽

pp. 15

Author(s):

Andreas Thoma ◽

Abhijith Moni ◽

Sridhar Ravi

Keyword(s):

Digital Image Correlation ◽

Real Time ◽

Digital Image ◽

Correct Choice ◽

Image Correlation ◽

Particle Swarm Algorithm ◽

Processing Unit ◽

Time Analysis ◽

The Real ◽

Real Time Analysis

Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one from the undeformed reference states of the sample and the other from the deformed target state, the relative displacement between the two states is determined. DIC is well-known and often used for post-processing analysis of in-plane displacements and deformation of the specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and expand the scope of this method. Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether the real-time analysis is possible with these methods. The effects of computing with different hardware settings were also analyzed and discussed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm, such that it becomes practically slower than a sub-optimal algorithm. The Newton–Raphson algorithm in combination with a modified particle swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss–Newton algorithm is superior. As expected, the brute force search algorithm is the least efficient method. We also found that the correct choice of parallelization tasks is critical in attaining improvements in computing speed. A poorly chosen parallelization approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode, the correct choice of combinations of integer-pixel and sub-pixel search algorithms is critical for efficient analysis. The real-time analysis using DIC will be difficult on computers with standard computing capabilities, even if parallelization is implemented, so the suggested solution would be to use graphics processing unit (GPU) acceleration.

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The SOMA Terror Organization Portal (STOP): social network and analytic tools for the real-time analysis of terror groups

Social Computing, Behavioral Modeling, and Prediction ◽

10.1007/978-0-387-77672-9_2 ◽

2008 ◽

pp. 9-18 ◽

Cited By ~ 5

Author(s):

Amy Sliva ◽

V.S. Subrahmanian ◽

Vanina Martinez ◽

Gerardo I. Simari

Keyword(s):

Social Network ◽

Real Time ◽

Time Analysis ◽

The Real ◽

Real Time Analysis

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Classification Connection of Twitter Data using K-Means Clustering

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f1004.0486s419 ◽

2019 ◽

Vol 8 (6S4) ◽

pp. 14-22

Keyword(s):

Social Media ◽

Real Time ◽

Clustering Methods ◽

Time Analysis ◽

Data Set ◽

Dynamic Data ◽

Real Time Analysis ◽

Twitter Data ◽

Social Media Platforms ◽

Insight Into

The rise of social media platforms like Twitter and the increasing adoption by people in order to stay connected provide a large source of data to perform analysis based on the various trends, events and even various personalities. Such analysis also provides insight into a person’s likes and inclinations in real time independent of the data size. Several techniques have been created to retrieve such data however the most efficient technique is clustering. This paper provides an overview of the algorithms of the various clustering methods as well as looking at their efficiency in determining trending information. The clustered data may be further classified by topics for real time analysis on a large dynamic data set. In this paper, data classification is performed and analyzed for flaws followed by another classification on the same data set.

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