A Quantified Approach for Analyzing the User Rating Behaviour in Social Media

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1 A Quantified Approach for Analyzing the User Rating Behaviour in Social Media P. Surya 1, Dr. B. Umadevi 2 1 Research Scholar, 2 Assistant Professor & Head, P.G & Research Department of Computer Science, Raja Doraisingam Govt. Arts College, Sivagangai, TamilNadu, India Abstract: The technological revolution made a significant change in the society. Today the human society uses the mobile phones not only for the communication but also for sharing their views and other joyful moments. In this venture, the social Medias like face book and WhatsApp plays vital role. Around 2.5 billion people are pressing the like buttons around the world. Through internet the people are spending minimum three hours per day in chatting, poking, and tweeting on the social media. The increased volume in data set is a big problem for the social media and more over the data set of this category are unstructured. It has become as big data in the social. Handling big data is an issue by its characteristics such as volume, velocity, variety, veracity and value. This paper analyses the user rating on three attributes such as likes, photos, status. It makes an analytical view of the users interest towards various things in the social media. Keywords: Facebook, HDFS, SVM, MapReduce. 54 I. INTRODUCTION The Face book social network gained very much popularity among then people around the world. Everyday millions of users share their information in the form of text, images or videos. Facebook engineers or analysts manipulate this large data set using Hadoop. Data set is of one peta byte disk space while 25 CPU cores. Hadoop is used as an open source for distributed framework for distributed storage. It is found by Apache foundation & usually processes large data sets. Hadoop includes distributed file system. The Large files extend in small sets and referred as cluster. The Packets are transferred to the cluster nodes in parallel form. A. Hadoop for Face Book: Hadoop is an open-source software framework used for distributed storage and processing of dataset of big data using the Map Reduce programming model. It consists of computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework [1]. The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a Map Reduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel [2]. This approach takes advantage of data locality where nodes manipulate the data they have access to. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking. B. Hadoop an Overview: The Hadoop framework consists of the following modules. Hadoop Common, it includes libraries and utilities required by the other modules. Hadoop Distributed File System (HDFS) contains distributed filesystem that stores data on commodity machines, providing very high aggregate bandwidth across the cluster. Hadoop YARN is a platform controls computing resources in clusters for managing and also for schedule the applications. The Hadoop Map is a Map Reduce programming model for large-scale data processing. The Hadoop framework has been written in the Java programming language, with some native code in C and command line utilities written as shell scripts [3]. The Map Reduce Java code is common, any programming language can be used with "Hadoop Streaming" to implement the "map" and "reduce" parts of the user's program other projects in the Hadoop ecosystem expose richer user interfaces. C. Hadoop Distributed File System (HDFS): The HDFS is a distributed, scalable, and portable file system written in Java for the Hadoop framework [4]. A Hadoop cluster has nominally a single name node plus a cluster of data nodes, although redundancy options are available for the name node due to its criticality. Each data node serves up blocks of data over the network using a block protocol specific to HDFS. The file system uses TCP/IP sockets for communication [5]. Clients use remote procedure calls (RPC) to communicate with each other. HDFS stores large across multiple machines. It achieves reliability by replicating the data across multiple hosts, and hence theoretically does not require redundant array of independent disks (RAID) storage on hosts [6]. HDFS is not fully POSIX-compliant, because the requirements for a POSIX file-system differ from the target goals of a Hadoop application. HDFS was designed for mostly immutable files and may not be suitable for systems requiring concurrent write-

2 operations. HDFS can be mounted directly with a File system in User space (FUSE) virtual file system on Linux and some other UNIX systems [6]. II. BACKGROUND AND RELATED WORKS Social Media is an umbrella term that describes websites and online tools that people use to connect and share content, experiences, opinions and media [7]. It enables conversations and interactions with people online. Examples of Social Media platforms are Facebook, Twitter and YouTube. While social media is great for staying in touch with friends and family, it also provides businesses of all shapes and sizes with a fantastic opportunity to communicate directly with new and existing customers - and at minimal cost. Both Facebook and Twitter allow small businesses to share descriptions about themselves, photographs, and information about their products and how to buy them, with new and existing customers at the click of a mouse. Recently, Pushpa, GauravGarg has been made a Review on User Behaviour Analysis using KNN and SVM vide Tweetson Big Data[ 8]. Another author Dr. B. Lavanya, B. Divya has been analysed an accident prediction system with huge collection of past records by applying effective predictive data mining techniques such as Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) which have a greater capacity to handle huge and noisy data that are used to predict accidents with more accuracy[9]. Presently, AnushreePriyadarshini and SonaliAgarwal has been analyzed the impact of penalty and kernel parameters on the performance of parallel SVM [1]. Their experimental results also analyzed that the computation time taken by the SVM with multi-node cluster is less as compared to the single node cluster for large dataset. Currently, Zhanquan Sun, Geoffrey Fox has been made an analysis on parallel SVM based on iterative MapReduce model Twister is studied. Their analysis results show that the parallel SVM based on iterative MapReduce is efficient in data intensive problems [11]. III. METHODOLOGY The effectiveness of well-known sentiment classification algorithms on our novel corpus of Facebook features. The different machine learning techniques such as Naïve Bayes, KNN, Neural Networks and Support Vector Machine are used to determine the different types of features posted by the members of the Facebook. This section deals with the proposed methodology. The Support Vector Machine [12] algorithm is used as the back bone of the method. It is adapted into the process of face book data analysis. The goal of this study is to improve the effectiveness of the proposed methodology to predict the user responses. The proposed algorithm in predicting the face book user will be described in the next section. A. Support Vector Machine: The Support Vector Machine (SVM) Support Vector Machine classification method is a very effective way for classification, and its results are better than other classification algorithms, in general such Naïve Bayes and decision trees, etc. The aim of the SVM is to identify a hyper-plane that separates two classes of data. The chosen hyper-plane creates the largest margin between the two classes to make the points belonging to different classes and also make those points away from the hyper-plane as far as possible. In other word, using SVM classification method is equivalent to solving a constrained optimization problem. Support Vector Machine (SVM) is a classification technique based on statistical learning theory. It is based on the idea of a hyper plane classifier [12]. The goal of SVM is to find a linear optimal hyper plane so that the margin of separation between the two classes is maximized. We choose SVM as the classifier because of its often reported best performance and it has been adopted by many previous text classification studies. The method suggested in this paper is to predict or classify the facebook users are belonging to the process of data mining which is given in Fig 1. There are five main stages in this method. The stages are Data collection, preprocessing, Data Transformation, classification and result interpretation. Data collection is gathering information available on facebook data for the three countries like India, Australia and South Africa during the year 216. During pre-processing [13] stage data cleaning, attributes selection, dimensionality reduction, and data partitioning are applied to get better prediction. Subsequently the Extracted data is transformed for classification. Whereas, in classification stage Data Mining [14] algorithms are used for the classification of data. Normally, at this stage different Data Mining algorithms are executed with different variables and compared to select algorithm [15] which produce best results. Finally, in interpretation stage models obtained from previous stage are analyzed to predict user responses on facebook data. 55

Fig 1: Method Proposed for Predicting the User Responses The most universal features of the face book are the Photos, links and status.

3 IV. EXPERIMENT RESULTS The improved communication technology increases the sharing complexities and reduces the potential applications. The way people wants to exhibit their views, is highly promoted by so many number of social networks. Among them Face book takes place a vital role. Fig 1: Method Proposed for Predicting the User Responses The most universal features of the face book are the Photos, links and status. These are the different perceptions people can share their views, thoughts, and joys and also supports for business promotion. The data has been collected with several attributes. The data set (face book) holds for the year 216 for the three countries India, Australia and South Africa which is given in Fig Fig 2 : Sample Dataset

4 The data set is pre processed by the Hadoop Distributed File System (HDFS). From the data set the country and the essential components or features such as photos, likes and status are extracted which is shown in Fig 3. The extractions are tested through the SVM algorithm and it categories the responses made by the face book members. The results are furnished below in Table I and Fig 4. In Table I and Fig 4 one among the features such as photo is considered of maximum number of users posted in the face book. The graph explains that the photos are most liked only in South Africa by the users. In general the members or users may post so many numbers of useful links among themselves. In Table II and Fig 5 the maximum number of users shared or commented found mostly only in India. At the same time the Table III and Fig 6 explains that status feature and likes is highly posted only by the users of Australia. The Table IV and Fig 7 the overall face book members response towards sharing only the photo is occupies the highest percent among the three Facebook features. From this the highest occupation for South Africa, India and Australia respectively. So the most important to understand from this analysis is that the members are interest only towards sharing their joys and happiness rather than text and information s. Fig 3: Selection of Country for Data Extraction Table I. Photos Interactions for (India, Australia and South Africa) COUNTRY NAME COMMENTS LIKES SHARES SOUTH AFRICA Table II. Links Interactions for (India, Australia and South Africa) COUNTRY NAME COMMENTS LIKES SHARES SOUTH AFRICA

5 User-Responses User-Responses International Journal of Electrical Electronics & Computer Science Engineering Table III. Status Interactions for (India, Australia and South Africa) COUNTRY NAME COMMENTS LIKES SHARES SOUTH AFRICA Table IV. Overall Total Interactions for (India, Australia and South Africa) COUNTRY NAME PHOTOS LINKS STATUS SOUTH AFRICA PHOTOS - Comparison COMMENTS LIKES SHARES Posted SOUTH-AFRICA Fig. 4. Photos Interactions for (India, Australia and South Africa) LINKS - Comparison COMMENTS LIKES SHARES Posted SOUTH-AFRICA Fig. 5. Links Interactions for (India, Australia and South Africa) 58

6 User-Responses User-Responses International Journal of Electrical Electronics & Computer Science Engineering STATUS - Comparison COMMENTS LIKES SHARES Posted SOUTH-AFRICA Fig. 6. Status Interactions for (India, Australia and South Africa) Overall Total-Interactions PHOTOS LINKS STATUS Features SOUTH AFRICA 59 V. CONCLUSION Fig. 7. Overall Total Interactions (India, Australia and South Africa) The research imitates with a scope to determine the maximum benefits for the users gathered through the social network. It starts with various parameters to identify the real interactions behind the face book. The Data mining algorithm SVM supports its maximum to classify the users interest towards sharing of Photos, Links and Status. In this it is identified that the photos sharing takes top most position. The next rank goes to status as well as for links. The research would like to conclude that it has the prime feature is to share the photos rather than other information through the network. VI. [1] [2] REFERENCES [3] P. Sachin Bappalige Feed, An introduction to Apache Hadoop for big data, August 214. [4] Evans, Chris, Big data storage: Hadoop storage basics, June 216.

7 [5] Kumar Gautam, Big Data - Part1, February 216. [6] Pessach and Yaniv, Distributed Storage, Distributed Storage: Concepts, Algorithms, and Implementations, Amazon.com, 213. [7] Bharat Naiknaware, Bindesh Kushwaha, Seema Kawathekar, Social Media Sentiment Analysis using Machine Learning Classifiers,, International Journal of Computer Science & Mobile Computing (IJCSMC), Vol. 6, Issue. 6, June 217, pg [8] Dr. B. Lavanya and B. Divya, Big Data Analysis Using SVM and KNN Data Mining Techniques, International Journal of Computer Science and Mobile Computing (IJCSMC), Vol. 6, Issue. 1, January 217, pg [9] Pushpa & Gaurav Garg, Review on User Behaviour Analysis using KNN/SVM vide Tweetson Big Data, International Journal of Innovative Research in Computer & Communication Engineering, Vol. 5, Issue 4, April 217. [1] Zhanquan Sun and Geoffrey Charles Foxn, Study on Parallel SVM Based on MapReduce, CiteSeer. [11] Safa Ben Hamouda and Jalel Akaichi, Social Networks Text Mining for Sentiment Classification: The case of Facebook statuses updates in the Arabic Spring Era, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 2, Issue 5, May 213. [12] Bo Guo, Rui Zhang, Guang Xu, Chuangming Shi and Li Yang, Predicting Students Performance in Educational Data Mining, IEEE Xplore, 24 March 216. [13] B. Umadevi D. Sundar, Dr. P. Alli, An Optimized Approach to Predict the Stock Market Behavior and Investment Decision Making using Benchmark Algorithms for Naïve Investors, Computational Intelligence and Computing Research (ICCIC), 213 IEEE International Conference on ( IEEE Xplore Digital Library), pg1-5. [14] B. Umadevi, D. Sundar, Dr. P. Alli, A Study on Stock Market Analysis for Stock Selection - Naïve Investors Perspective using Data Mining Technique, International Journal of Computer Applications, ( ), Vol 34 No.3, 211. [15] Dr. B. Umadevi and M. Snehapriya, A Review On Various Data Mining Techniques In Social Media, International Journal of Innovative Research in Computer & Communication Engineering, Vol 5, Issue 4, April

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Unit # 19 1 Acknowledgement The following discussion is based on the paper Mining Big Data: Current Status, and Forecast to the Future by Fan and Bifet and online presentation