Hadoop framework for efficient sentiment classification using trees

IET NetworksVolume 9, Issue 5 p. 223-228 Special Issue: Selected Papers from the Ubiquitous Clouds and Cognitive Communication Networks (UCCCN 2019)Free Access Hadoop framework for efficient sentiment classification using trees K. Sridharan, Corresponding Author K. Sridharan srijik77@gmail.com Anna University, Chennai, IndiaSearch for more papers by this authorG. Komarasamy, G. Komarasamy Department of Computer Science and Engineering, Jain University, Bangalore, IndiaSearch for more papers by this authorS. Daniel Madan Raja, S. Daniel Madan Raja Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, IndiaSearch for more papers by this author K. Sridharan, Corresponding Author K. Sridharan srijik77@gmail.com Anna University, Chennai, IndiaSearch for more papers by this authorG. Komarasamy, G. Komarasamy Department of Computer Science and Engineering, Jain University, Bangalore, IndiaSearch for more papers by this authorS. Daniel Madan Raja, S. Daniel Madan Raja Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, IndiaSearch for more papers by this author First published: 22 September 2020 https://doi.org/10.1049/iet-net.2019.0208Citations: 5AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Due to the increase in the speed of generation of data, the authors are forced to handle a massive volume of data with the help of conventional machine learning algorithms. Big data is an enormous volume of data which is beyond the capacity of the traditional database software tool to collect, store, manage, and process within a stipulated time limit. Sentiment analysis is analysing the data by classifying the text on the basis of strength and polarity of opinion (positive/negative) words that define the text. While handling big data, Hadoop provides a platform for users to develop their own sentiment analysis with the help of a lexicon dictionary or available application programming interface (API) or external programs. The aim of classifying data is to analyse extensive data and develop an appropriate description or model for every organised class with the feature present in the data. In this work, the feature extraction based on term frequency-inverse document frequency is utilised and the Hadoop framework in attaining a useful classification with the help of random forest techniques. 1 Introduction The most happening research area today in the arena of computer science is big data [1 ], and the essential part of the research area is sentiment analysis. The sentiment is defined as an expression of opinion by an author about any object or any aspect. Analysing, investigating, extracting users' opinions, view, and preferences from the subjective text are known as sentiment analysis. The main focus of sentiment analysis is parsing the document. In simple terms, sentiment analysis can be defined as detecting the polarity of the text. Polarity can be positive, negative, or neutral. It is also referred to as opinion mining as it derives an opinion of the user. Opinions vary from user to user, and sentiment analysis much helps in understanding users' perspective [2 ]. Big data is a massive volume of data that is found in the web in the form of social media data, medical records, or remote sensing data, which can be structured, semi-structured, or non-structured, and these data can be used for sentiment analysis. The main focus of attention on late is big data applications [3 ] due to its enormous amount of data generation and storage, which has been done in the last few years. While considering vast amounts of data, this condition becomes a challenge in knowledge extraction as an adaptation of data mining techniques to the new time and space requirements are not considered. Moreover, while considering real-world applications, a class distribution is presented where samples that belong to one class, which is precisely the main interest, are surpassed by examples of other courses. In these circumstances, which is otherwise called class imbalance problem, the learning process is complicated as this situation is not addressed by standard learning techniques. While promoting a product, customer satisfaction is the critical criterion, and one way of achieving it is by presenting and displaying the products on the basis of the customer's interests. Social media platforms such as Facebook, Twitter, and so on provide a platform for the users to post their views, likings, and emotions about a number of topics, products, people, and services. The orientation of the posted text with regard to positive or negative sentiment can be classified on the basis of the opinions expressed. This helps in identifying the opinion and emotion of the user about a particular product or service. Collection of textual data is involved in sentiment analysis from a number of sources such as reviews, social sites, blogs, and so on, which process a massive volume of data where redundant data is removed by analysing the extracted sentiment and integrating with applications to aid decision making [4 ]. In industrial as well as academic fields, a framework for analysing big data is provided through Hadoop [5 ]. It involves collecting, storing, retrieving, managing, and distributing the vast dataset that is collected with the help of a group of computers and simple programming models. In a Hadoop cluster, every machine has its local storage and can perform local computation. With regard to parallel processing, this can provide a highly efficient, intensive, scalable, and flexible development framework. Reliable service availability is offered through the Hadoop structure to detect and to handle failures at the application layer itself. Feature extraction is nothing but processing of massive data, where the unwanted or redundant information or data is removed, and final data with reduced features (feature vectors) is formed from the original on the basis of transformation of attributes. With a large number of product reviews [6 ], a prospective buyer might face a daunting task of sifting through them, in order to find ones that he might find useful. In this research work, we implemented a system that classified a product review's sentiment into either positive or negative, using parts of speech of the review's textual content. The motivation came from an observation that when expressing positive feelings, a person's emotion and state of mind would usually be different from ones when negative feelings were being shown. Such difference may influence in a person's writing styles, words of choices, degree of formality, and so on. The above challenges are solved through classification technique where the big data is classified based on data format to be processed, the analysis type to be applied, the processing method to be worked, and the data sources for the target structure to acquire, load, process, analyse, and store [7 ]. On the selected application, many classification techniques are used. Before the beginning of actual classification, the information that is required is extracted from a massive volume of data, and classification is performed. Two types of classification methods are named as supervised and unsupervised, which are also called predictive or directed classification. Machine learning is the knowledge that is automatically acquired and integrated by a system. A system that can learn through analytical observation, experience, and other means can result in exhibiting self-improvement, efficiency, and effectiveness. The knowledge that is acquired is interpreted and analysed through machine learning by the knowledge and a corresponding knowledge organisation. One of the machine learning algorithms can be considered as taxonomy-based, which depends on the results of the algorithm or type of available input. Features of big data include substantial data amounts, numerous data types, high requirements for processing speed, and high analysis value return. From the perspective of application scenarios, the demands for massive data analytics mainly concentrate in several major categories, such as classifier, association rules, and clustering. In the context of big data environment, random forest (RF) method is also characterised with following advantages: from the perspective of the massive amount of big data, the RF method can be competent; the relatively simple decision trees generated by RF method facilitates business analysts to interpret its meaning; RF method is suitable for distributed and parallel environment, showing a good scalability; the simple classifier created by decision trees can process data efficiently, which is applicable to the characteristics of rapid data refresh rate in the big data environment. Often estimation of model parameters and hyperparameters are required in machine learning. Some advantages of RF [8 ] are: it can effectively solve the problem of unbalanced classification, it has excellent noise tolerance, and high classification accuracy, the trees of RF method are built using CART algorithm, which is compatible with the treatment of continuous attributes and discrete attributes, and it is suitable for distributed and parallel environment, showing a good scalability. RF be the parametric supervised classification method, which can be considered as a classification and regression tree (CART) model that produces a vast volume of classification trees that can contribute through a voting system of classifying data. A large number of classification trees are provided by RF through a voting system in organising the data. A 'forest' of classification trees is grown by RF, which is a bootstrapped CART. Default values of RF are used as the general belief is that this technique is parameter insensitive, or can be considered as robust to changes from default parameter settings. A Hadoop framework is proposed for sentiment classification. Section 2 details the literature related to this work, Section 3 details on the techniques used. Section 4 elaborates on the results, and Section 5 concludes the work. 2 Literature survey Vast volumes of the dataset, which is complex and has multiple independent sources, are present in big data. Most of the technologies are unable to handle a large dataset, and big data comes in handy in handling such large datasets. When such a broad unstructured data is under processing, it needs some amount of organising to make the data accessible. The required information is provided to the users in a simple manner through classification techniques, which can easily classify the transaction database. Classification algorithms were proposed by Kalambate et al. [9 ], who tried their application on big data. Big data is organised with the help of Naïve Bayes, support vector machines (SVM), Decision Tree, and k-NN algorithms. This paper throws light on the comparison of classification algorithms and the application of each of these algorithms in big data. Rodrigues et al. [10 ] introduced the definition of sentiment analysis as well as Hadoop and described the Hadoop architecture, and then focus on the study of the Hadoop framework for sentiment analysis of social media data. In today's highly developed world, every minute, people around the globe express themselves via various platforms on the Web. And in each minute, a massive amount of unstructured data is generated. This data is in the form of text, which is gathered from forums and social media websites. Such information is termed as big data. User opinions are related to a wide range of topics like politics, the latest gadgets, and products. These opinions can be mined using various technologies and are of utmost importance to make predictions or for one-to-one consumer marketing since they directly convey the viewpoint of the masses. Here Ingle et al. [11 ] proposed to analyse the sentiments of Twitter users through their tweets in order to extract what they think. Hence, we are using Hadoop for sentiment analysis, which will process the massive amount of data on a Hadoop cluster faster. InterIMAGE Cloud Platform was proposed by Ayma et al. [12 ], considered an open-source distributed structure for the analysis of an automatic image. Data Mining Package performed supervised classification procedures on a large volume of data, which is generally referred to as big data, where a distributed infrastructure was used called Hadoop MapReduce. From WEKA's machine learning library, four classification procedures were used, such as Decision Trees, Naïve Bayes, RF, and SVM. The possible tools and features that affect the performance are outlined by the experiment with the SVM classifier based on the datasets with varying sizes for varying cluster configuration. In the current Internet-savvy world, people present their views, opinions, and feelings on specific topics via various social media applications. A huge opportunity is provided through the posts from users for organisations in increasing their market value through the analysis of posts and the use of information in decision making. These can be studied with the help of various machine learning approaches and language-based approaches in order to feed in its sentiments. With so much data moving to the internet, massive data is being produced each second, and the issue is in storing the large dataset and processing it efficiently in a real time and scalable manner in performing sentiment analysis with the help of Hadoop in a timely and efficient manner. Various component tools of Hadoop, such as MapReduce, Mahout, and Hive, are also surveyed in many articles for this work, along with Hadoop. Matwankar and Shinde [13 ] analysed political activeness through sentiments on the basis of comments; contents posted online using classification algorithm, which calculated the effective manner in which online communities work, along with followers and online activities. The political score was calculated by using the comments posted by the user along with the online community which the user is following, what the friends of the users were posting, and so on. On a speculative venture, an algorithm was implemented in calculating political score on the basis of tweets that were composed of Twitter in which python is used as the programming language and SQLlite NoSQL Database management system. Social media has become a common platform for people to communicate with their family and friends and also has become a platform to talk about their preferences. This 'unstructured' conversation has given rise to valuable insight for businesses in promoting their brand business decisions could be made based on this in maintaining their image. The development of sentiment-rich social media on the web has thrown a light in the direction of opinion mining and sentiment analysis among researchers. As social media is widespread among network, sentiment analysis has now become a big data task. An appropriate technique which can perform sentiment analysis on big data sets was proposed by Kurian et al. [1 ], where he performed SA on a large dataset of tweets using Hadoop and measured the performance of the technique through speed and accuracy. The results of the study showed that excellent efficiency was exhibited by this technique while handling sentiment datasets. Unbalanced datasets in the big data arena were analysed by Rio et al. [3 ] using the RF classifier. MapReduce was used to manage oversampling, under sampling, and cost-effective learning, which provided the much-needed support in identifying the underrepresented class. A solid basis is provided by the RF classifier for comparison because of its efficiency, strength, and flexibility. Parameter's selection's effect on classification was examined by Huang and Boutros [14 ] with the RF machine learning process based on two biological datasets with specific p /n ratios such as sequencing summary statistics (low p /n ) and microarray-derived data (high p /n ). Here 'p ' and 'n ' represent a number of variables and samples, respectively. The results of the study show that parameterisation is highly correlated with prediction accuracy and variable importance measures (VIMs). It also proved that the parameters were critical while tuning various datasets, and there was a significant enhancement of parameter optimisation upon default parameters. Wide variability was demonstrated by parameter-optimisation on default parameters on both high and low p /n data. RFs tuning method has been benefited by keeping away from their default parameter settings through this. Han et al. [15 ] proposed the SMRF algorithm – an improved scalable RF algorithm based on Map Reduce model. This new algorithm makes data classification in a computer cluster or cloud computing environment for massive datasets. SMRF processes and optimises the subsets of the data across multiple participating computing nodes by distributing. The experimental results show that the SMRF algorithm has equally accuracy degradation but higher performance while comparing with the traditional RF algorithm. SMRF algorithm is more suitable to classify massive data sets in distributed computing environment than traditional RF algorithm. Naïve Bayes classification algorithm is executed on top of Hadoop to create automatic document categoriser applying MapReduce programming paradigm in Temesgen & Lemma [16 ]. Addis Ababa university institutional repository/Electronic thesis and dissertations text document is accepted for training and evaluation datasets. The proposed design performed an accuracy of 79.06%. The outcome shows that the system can describe large thesis documents into its predefined class with assuring accuracy. Mathivanan et al. [17 ] use the term frequency (TF) feature extraction. The purpose here is working feature selection based on information gain (IG) and particle swarm optimisation (PSO) for feature selection in sentiment classification. Specific systems can modify the features in the original set as they reduce redundant features for text sentiment categorisation and therefore extemporise the accuracy of classification. Moreover, the running time of the learning algorithms is diminished. K-nearest neighbour classifier is used for estimating the recommended system. Empirical consequences have explained that related to the IG based feature selection, the PSO based feature selection system achieves more reliable and more robust performance. 3 Methodology In this section, details of the dataset used, Hadoop framework, feature extraction based on term frequency-inverse document frequency (TF-IDF), and RF classifiers are explained. Fig. 1 shows the flowchart for the proposed methodology. Fig. 1Open in figure viewerPowerPoint Flowchart of the proposed methodology 3.1 Dataset Clothing, shoes, and jewelry dataset is used in this work. Product reviews and metadata from Amazon were chosen for this study, which included 142.8 million reviews over a period from May 1996 to July 2014. The reports such as ratings, texts, votes are included here. The product metadata, such as category information, brand, price, descriptions, and image features, are also included. Links such as viewed and bought graphs are presented. 3.2 Hadoop framework Hadoop [18 ] proves to be a reliable framework, and also it processes the massive set of data in a fault-tolerant manner, which makes it efficient. For sentiment analysis, data accuracy is critical, and the Apache Hadoop framework provides an accurate result of 84% when data produced from social media are abundant. The services are provided by the Cloudera version of Hadoop. With the help of Cloudera manager, services like Flume, Hive, and Oozie were installed on top of Hadoop. Data storage and processing are done by the Hadoop, which is an accessible, open-source, fault-tolerant Virtual Grid operating system design [9 ]. Hadoop is meant basically to store, process massive datasets with the help of commodity hardware, and is not for small data. Hadoop is made up of two vital parts – a storage part, which is a Hadoop Distributed File System (HDFS), and a processing unit, which is known as the MapReduce programming model. For data storage purposes, HDFS is used by Hadoop. There are different nodes in each cluster of Hadoop, and so the architecture of HDFS is divided into the Name Node, Data Node, and HDFS Clients (Edge Node). (A) Name Node: This is otherwise called as the master node, which possesses all the information about the metadata, including all the data nodes and their addresses and some other configurations such as replication of data [19 ]. (B) Data Node: This can be called a slave node in Hadoop, which is used to save data. Here, a task tracker is present, which tracks the ongoing jobs on the data node and the jobs that come from the name node. (C) HDFS Clients: The basis for Hadoop architecture is – HDFS. Here is the equal distribution of data on every node in the Hadoop system. When there is a need to add, modify, or delete a particular data from the Hadoop system, then the data is collected from every node, and the meaningful action is done as per the need. (1) Advantages: HDFS stores a large amount of information due to its distributed data and computations. Sentiment analyser can be implemented quickly because of its simple programming model. Quickly recovers from system failures. As data written in HDFS can be read several times, it increases the extraction of redundant tweets. 3.3 TF-IDF feature extraction Feature based sentiment analysis [20 ] include feature extraction, sentiment prediction, sentiment classification, and optional summarisation modules. Feature extraction identifies those product aspects which are being commented by customers, sentiment prediction identifies the text containing sentiment or opinion by deciding sentiment polarity as positive, negative, or neutral and finally summarisation module aggregates the results obtained from previous two steps. Feature extraction process takes text as input and generates the extracted features in any of the forms like Lexico-Syntactic or Stylistic, Syntactic, and Discourse based. Text miming includes a method of feature vectorisation named as TF-IDF [21 ] which redirects the importance of a term to a document in a corpus. Let 't ' means term, 'd ' means document, and 'D ' means corpus. TF means the number of times a term appeared in a document is specified as . Document frequency is defined as the number of document that includes a term and is denoted as . Considering 'a', 'the', and 'of', it is clear that the TF measures the rank such that it is easy to over-emphasise the terms in which small content of information's are accepted about the document. On the other hand, no particular information is transferred by the document if a term appears across the corpus. Inverse document frequency (IDF) measures the amount of information provided as (1) where means the total number of documents used in the corpus. IDF is 0 when terms are appeared in all documents. The TF-IDF is used to calculate the product of TF and IDF as (2) 3.4 Random forest classifier For supervised learning, RF algorithm is supervised machine learning and an ensemble learning method which is used greatly in science and many other fields. RF can be considered as a form of ensemble learning and has several features which can be flexible. It can find its application to two or multi-class prediction problems, interaction of models among variables, can possess a mixture of categorical and continuous variables which provide VIMs, and there is a predictive performance even for data with more variables (p ) than samples (n ; i.e. p > > n ); this potentially involves noisy and momentously correlated variables [8 ]. Due to their non-parametric nature, RFs are found to be fairly robust and comparatively trustworthy while operated by inexperienced users. Thus, expansion of this technique has taken place to a framework of models [22 ]. 3.4.1 Random forest for classification An important and popular ensemble which is commonly used is RF [23 ] classification. The popularity of RF's performances comes from its efficiency when compared to other classification algorithms [24 ]. As an ensemble of decision tree, RF has to build various decisions trees. To do so, a bootstrap sample set is considered at every step of the original training data and new sampling set is attained by replacing an instance from the original set till the original training data is attained. A random tree is formed by making use of one of the bootstrap sample sets of training data. Every random tree used a conventional top-down induction method by performing many changes positively to the diversity of the ensemble. In each step, small subset of attributes are performed once the best attribute is selected. Consider, 'm ' be the number of selected attributes, 'M ' be the total attribute number in dataset. CART is work out as the best attribute. Pruning method is not applied once the tree is built completely. Computation of the predicted class for a sample is done by aggregating predictions of ensemble o decision trees via majority voting. 3.4.2 Random forest for classification with big data According to the input data, each decision tree will give a result; integration of multiple results will give the final output of a RF. In the RF, the growth process of a single decision tree is as follows: (i) for the original training sets, bagging method is used to select random data with replacement and thus form training sets with difference; (ii) the features are also selected using sampling approach. While dealing with big data experiments, modification of the original RF algorithm has to be done, so that effective process of the data is available. Multiple trees are built for different portions of the data through Mahout Partial implementation (RF-Big Data). This technique is classified into two different stages: the first phase is the creation of the model and the second phase is the estimation of classes associated with the dataset with the help of previous learned model. MapReduce procedure is used in the first phase, where RF is built from the original training set. There are three steps in this process: initial, map, and final. Initially dataset segmentation of dataset is performed as independent data blocks, then the blocks being replicated and transferred between various processing nodes. In the next step, i.e. map step, a subset is built by each map task of the forest, i.e. various random trees of the forest with the data block of its partition and a file containing the built trees are generated. Ultimately in the final step, analysis of the generated output files by mappers is done to extract the trees. Forest is formed from the collection of trees. When forest construction is completed, classification phase begins in estimating the class associated to a data sample set. There are again three steps in this – initial, map, and final. Segmentation of the available data sample set is done in the segmentation step which can either large training or test set into independent data blocks. This is used to duplicate and transfer to other machines which are processed in parallel by each map method. Then the class is estimated by each mapper for available samples by making use of majority vote of the predicted class by trees in RF model that is built in the previous phase. The last step is the final step where predictions are formed by each mapper which altogether form final prediction file. The pseudo-code of the map function of MapReduce job is done through Algorithms 1 and 2 (Figs. 2 and 3 ) to build the model phase. Fig. 2Open in figure viewerPowerPoint Algorithm 1: map phase for the RF-Big Data algorithm for the building of the model phase MAP (key, value) Fig. 3Open in figure viewerPowerPoint Algorithm 2: map phase for the RF-Big Data algorithm to build the model stage CLEANUP () The MapReduce model is collected of two foremost stages: map and reduce. The map phase procedures the input data set, creating some intermediate results. Then, the reduce phase combines these intermediate results to form the final output. Algorithm 1 is used to attain all the occurrences in a mapper's partition in a <key, value> pair format. It considers the 'key' and 'value' which helps to attain tree growth and its predictions. Algorithm 2 built a subset of the forest by using the instances attained in Algorithm 1. Using Bagging technique, the tree is built. 4 Results and discussion Table 1 shows the summary of results for classification accuracy, average recall, average precision, and average F -measure. Figs. 4-7 show the result graph for the same, respectively. Table 1. Summary of results Number of trees used Classification accuracy Average recall Average precision Average F -measure 50 0.856 0.8553 0.85675 0.8557 100 0.8789 0.87815 0.8797 0.87855 150 0.8903 0.8896 0.8912 0.89 200 0.9017 0.90075 0.9039 0.9014 250 0.9069 0.9058 0.9093 0.9065 Fig. 4Open in figure viewerPowerPoint Classification accuracy for RF Fig. 5Open in figure viewerPowerPoint Average recall for RF Fig. 6Open in figure viewerPowerPoint Average precision for RF Fig. 7Open in figure viewerPowerPoint Average F-measure for RF Table 1 and Fig. 4 show that the classification accuracy for RF increases when the number of trees used increased. Results show that the accuracy of number of trees used 250 performs better by 5.77%, by 3.14%, by 1.85%, and by 0.58% than the number of trees used as 50, 100, 150, and 200, respectively (Fig. 4 ). Table 1 and Fig. 5 show that the average recall for RF increases when the number of trees used increased. Results show that the average recall of number of trees used 250 performs better by 5.74%, by 3.11%, by 1.8%, and by 0.56% than the number of trees used as 50, 100, 150, and 200, respectively (Fig. 5 ). Table 1 and Fig. 6 show that the average precision for RF increases when the number of trees used increased. Results show that the average precision of number of trees used 250 performs better by 5.95%, by 3.31%, by 2.01%, and by 0.61% than the number of trees used as 50, 100, 150, and 200, respectively (Fig. 6 ). Table 1 and Fig. 7 show that the average F -measure for RF increases when the number of trees used increased. Results show that the average F -measure of number of trees used 250 performs better by 5.77%, by 3.13%, by 1.84%, and by 0.56% than the number of trees used as 50, 100, 150, and 200, respectively (Fig. 7 ). 5 Conclusion A large volume of data which are complex with multiple autonomous sources is big data. The attitude or opinion towards an event is aimed through sentiment analysis from the topic or product from the user contributed data. Sentiment analysis is implemented through Hadoop which has less complex logical implementation business which is easily extendible and there is better understandability and high performance at a lesser cost. Randomised decision tree helps in constructing RF. Excellent predictors are often produced after each iteration. As the number of trees increase, classification accuracy for RF increases. Results show that the accuracy of number of trees used is 250 performs better by 5.77%, by 3.14%, by 1.85%, and by 0.58% than the number of trees used as 50, 100, 150, and 200, respectively. In future, the existing results can be improved in a better way by introducing optimising techniques. Feature selection techniques can be explored to improve the efficacy of the classifier. 6. References 1Kurian D.D.M.K. Vishnupriya S. Ramesh R. et al.: 'Big data sentiment analysis using hadoop ', Int. J. Innovative Res. Sci. Technol., 2015, 1, (11 ), pp. 92– 96 2Wang Y. Rao Y. Wu L.: 'A review of sentiment semantic analysis technology and progress '. In 2017 13th Int. Conf. on Computational Intelligence and Security (CIS), Hong Kong, China, December 2017, pp. 452– 455 3Del Río S. López V. Benítez J. M. et al.: 'On the use of MapReduce for imbalanced big data using random forest ', Inf. Sci., 2014, 285, pp. 112– 137 4Felciah M.L.P. Anbuselvi R.: 'A study on sentiment analysis of social media reviews '. 2015 Int. Conf. on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, March 2015, pp. 1– 3 5Sharma D.: ' Study of sentiment analysis using hadoop ', in V. Aggarwal V. Bhatnagar D. Mishra (Eds.): Big Data Analytics, Advances in Intelligent Systems and Computing, vol 654, 2018, pp. 363– 376, https://doi.org/10.1007/978-981-10-6620-7_35 6Ogutu R.V.A. Rimiru R. Otieno C.: 'Target sentiment analysis model with naïve Bayes and support vector machine for product review classification ', Int. J. Comput. Sci. Inf. Secur. (IJCSIS), 2019, 17, (7 ), pp. 1– 17 7Suthaharan S.: ' Big data classification: problems and challenges in network intrusion prediction with machine learning ', Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC 27402, USA, 2012 8Liu Y.: 'Random forest algorithm in big data environment ', Comput. Model. New Technol., 2014, 18, (12A ), pp. 147– 151 9Kalambate A.R. Mane M.R. Rane Z. et al.: 'Use of hadoop framework for web based sentiment analysis ', Int. J. for Sci. Res. Dev., 2015, 3, (8 ), ISSN (online): 2321–0613, pp. 855– 857 10Rodrigues A.P. Chiplunkar N.N. Rao A.: 'Sentiment analysis of social Media data using hadoop framework: A survey ', Int. J. Comput. Appl., 2016, 151, (6 ), pp. 7– 10 11Ingle A. Kante A. Samak S. et al.: 'Sentiment analysis of twitter data using hadoop ', Int. J. Eng. Res. General Sci., 2015, 3, (6 ), pp. 144– 147 12Ayma V.A. Ferreira R.S. Happ P. et al.: 'Classification algorithms for big data analysis, a map reduce approach ', Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 2015, 40, (3 ), p. 17 13Matwankar S.H. Shinde S.H.: 'Sentiment analysis for big data using data mining algorithms ', Int. J. Eng. Res. Technol. (IJERT), 2015, 4, (9 ), pp. 962– 965 14Huang B.F. Boutros P.C.: 'The parameter sensitivity of random forests ', BMC bioinformatics, 2016, 17, (1 ), p. 331 15Han J. Liu Y. Sun X.: 'A scalable random forest algorithm based on mapreduce '. 2013 4th IEEE Int. Conf. on Software Engineering and Service Science (ICSESS), Beijing, China, May 2013, pp. 849– 852 16Temesgen M.M. Lemma D.T.: 'A scalable text classification using naive Bayes with hadoop framework '. Int. Conf. on Information and Communication Technology for Development for Africa, Cham, May 2019, pp. 291– 300 17Mathivanan V. Al-Alawi A.S.R. Al-Sulti S.S.: 'An selection system for automotive sentiment classification In hadoop using KNN classifier ', Eur. J. Business Manage. Res., 2019, 4, (6 ), pp. 2– 6 18Alam A. Ahmed J.: 'Hadoop architecture and its issues '. 2014 Int. Conf. on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA., March 2014, Vol. 2, pp. 288– 291 19Patil V.S Soni P.D.: 'Hadoop skeleton & fault tolerance in hadoop clusters ', IJAIEM, 2013, 2, (2 ), pp. 247– 250 20Asghar M.Z. Khan A. Ahmad S. et al.: 'A review of feature extraction in sentiment analysis ', J. Basic Appl. Sci. Res., 2014, 4, (3 ), pp. 181– 186 21Sahitya A.B. Vijayalakshmi D.M.M.: 'Feature extraction from big data' 22Criminisi A. Shotton J. Konukoglu E.: 'Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning ', Found Trends® Comput. Graph Vis., 2011, 7, pp. 81– 227 23Breiman L.: 'Random forests ', Mach. Learn., 2001, 45, (1 ), pp. 5– 32 24Lulli A. Oneto L. Anguita D.: 'Mining big data with random forests ', Cogn. Comput., 2019, 11, (2 ), pp. 294– 316 Citing Literature Volume9, Issue5Special Issue: Selected Papers from the Ubiquitous Clouds and Cognitive Communication Networks (UCCCN 2019)September 2020Pages 223-228 FiguresReferencesRelatedInformation