Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints. A parallel computational environment provided by Apache Hadoop can distribute and process the data over different destination systems. In this paper, the Hadoop cluster with four nodes integrated with RHadoop, Flume, and Hive is created to analyze the tweets gathered from the Twitter stream. Twitter stream data is collected relevant to an event/topic like IPL-2015, cricket, Royal Challengers Bangalore, Kohli, Modi, from May 24 to 30, 2016 using Flume. Hive is used as a data warehouse to store the streamed tweets. Twitter analytics like maximum number of tweets by users, the average number of followers, and maximum number of friends are obtained using Hive. The network graph is constructed with the user's unique screen name and mentions using 'R'. A timeline graph of individual users is generated using 'R'. Also, the proposed solution analyses the emotions of cricket fans by classifying their Twitter messages into appropriate emotional categories using the optimized support vector neural network (OSVNN) classification model. To attain better classification accuracy, the performance of SVNN is enhanced using a chimp optimization algorithm (ChOA). Extracting the users' emotions toward an event is beneficial for prediction, but when coupled with visualizations, it becomes more powerful. Bar-chart and wordcloud are generated to visualize the emotional analysis results.

Analyzing the heterogeneous data generated by social networking sites is a research challenge. Twitter is a massive social networking site. In this paper, for processing the heterogeneous data, a methodology is devised, which helps in categorizing the data obtained from Twitter into different directories and understanding the text data explicitly. The methodology is implemented using Python programming language. Python’s tweepy package is used to download the Twitter stream data which includes images, videos and text data. Python’s Aylien API is used for analyzing the Twitter text data. Using this API, sentiment analysis report is generated. Using Python’s matplotlib package, a pie chart is generated to visualize the sentiment analysis results. Further an algorithm is proposed for sentiment analysis, which not only categorizes the tweets into positive, negative and neutral (as Aylien API does), but also categorizes the tweets into strongly and weakly, positive and negative based on the polarity and subjectivity. Django platform and Python’s TextBlob package are used for implementing this algorithm. For this experiment, data is collected from Twitter using the hash tags related to different events/topics like IPL2018, World Cup2018, Modi, and Delete Facebook etc. during the period Monday Jan 22, 2018 to Monday May 28, 2018. Moreover, the data is collected and processed using Python TextBlob. Also conducted the Sentiment analysis on text data using TextBlob and visual reports are generated using Google chart. The results obtained from both the above-mentioned approaches are compared and it is observed that the proposed algorithm gives better sentiment analysis of the tweets.

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Wireless Sensor Network is a Research issue in which construction of Virtual Backbone schemes over multi-hop wireless sensor networks. This paper investigates the problem of connectivity for random placement of nodes by minimizing active sensor nodes. Several backbone construction techniques have been reported in recent years regarding the routing problem of the ad hoc wireless sensor networks. As the network size is growing in the physical world, redundancy in nodes also increased because of this the redundancy it affects the WSN performance. Hence the classification of all these backbone construction techniques is done, which are based on the connected dominating set (CDS) and other techniques. A generalized scheme to be more robust, having lesser number of Backbone nodes with higher mobility is proposed.