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Social Media has become one of the major industries in the world. It has been noted that almost three fourth of the world's population use social media. This has instigated many researches towards social media. One such useful application is the sentimental analysis of real time social media data for security purposes. The insights that are generated can be used by law enforcement agencies and for intelligence purposes. There are many types of analyses that have been done for security purposes. Here, the authors propose a comprehensive software application which will meticulously scrape data from Twitter and analyse them using the lexicon based analysis to look for possible threats. They propose a methodology to obtain a quantitative result called criticality to assess the level of threat for a public event. The results can be used to understand people's opinions and comments with regard to specific events. The proposed system combines this lexicon based sentimental analysis along with deep data collection and segregates the emotions into different levels to analyse the threat for an event.

Summary Sentiment analysis and opinion mining has become a major tool for collecting information from customer reviews on user sentiments and emotions, especially for online video streaming services and social networks. The increasing use of smartphones has popularized subscription to various streaming services that provide streaming media and video‐on‐demand. These applications offer a gateway to analyze user reviews by introducing sentiment analysis in the mobile environment. Online user reviews can hold a lot of useful information and help predict user interests. Analysis of user reviews can provide substantive information for business processing. Sentiment classification of these reviews is a commonly used analysis technique. Usually, these reviews are given in a text format, with every word in each considered a feature, so selection should focus on optimal features from all available features present in the reviews. This study employs machine learning algorithms to extract the best features from the training review data set. Then, the selected features are fed into the convolutional neural network and other fully connected layers for further processing. The proposed approach is evaluated with the standard evaluation metrics, such as precision, accuracy, recall, and f‐measure, using three distinct benchmark data sets: polarity, Rotten Tomatoes, and IMDb. This work has also employed a pretrained sentiment analysis model over an Android application framework to classify reviews on a Smartphone without the need for any cloud or server‐side API.

In wireless sensor network (WSN), the sensors are deployed and placed uniformly to transmit the sensed data to a centralized station periodically. So, the major threat of the WSN network layer is sinkhole attack and it is still being a challenging issue on the sensor networks, where the malicious node attracts the packets from the other normal sensor nodes and drops the packets. Thus, this paper proposes an Intrusion Detection System (IDS) mechanism to detect the intruder in the network which uses Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for its routing operation. In the proposed algorithm, the detection metrics, such as number of packets transmitted and received, are used to compute the intrusion ratio (IR) by the IDS agent. The computed numeric or nonnumeric value represents the normal or malicious activity. As and when the sinkhole attack is captured, the IDS agent alerts the network to stop the data transmission. Thus, it can be a resilient to the vulnerable attack of sinkhole. Above all, the simulation result is shown for the proposed algorithm which is proven to be efficient compared with the existing work, namely, MS-LEACH, in terms of minimum computational complexity and low energy consumption. Moreover, the algorithm was numerically analyzed using TETCOS NETSIM.