Big Data Sentimental Analysis Using Document to Vector and Optimized Support Vector Machine

Abstract

With the rapid evolution of the internet, using social media networks such as Twitter, Facebook, and Tumblr, is becoming so common that they have made a great impact on every aspect of human life. Twitter is one of the most popular micro-blogging social media that allow people to share their emotions in short text about variety of topics such as company’s products, people, politics, and services. Analyzing sentiment could be possible as emotions and reviews on different topics are shared every second, which makes social media to become a useful source of information in different fields such as business, politics, applications, and services. Twitter Application Programming Interface (Twitter-API), which is an interface between developers and Twitter, allows them to search for tweets based on the desired keyword using some secret keys and tokens. In this work, Twitter-API used to download the most recent tweets about four keywords, namely, (Trump, Bitcoin, IoT, and Toyota) with a different number of tweets. “Vader” that is a lexicon rule-based method used to categorize downloaded tweets into “Positive” and “Negative” based on their polarity, then the tweets were protected in Mongo database for the next processes. After pre-processing, the hold-out technique was used to split each dataset to 80% as “training-set” and rest 20% “testing-set.” After that, a deep learning-based Document to Vector model was used for feature extraction. To perform the classification task, Radial Bias Function kernel-based support vector machine (SVM) has been used. The accuracy of (RBF-SVM) mainly depends on the value of hyperplane “Soft Margin” penalty “C” and γ “gamma” parameters. The main goal of this work is to select best values for those parameters in order to improve the accuracy of RBF-SVM classifier. The objective of this study is to show the impacts of using four meta-heuristic optimizer algorithms, namely, particle swarm optimizer (PSO), modified PSO (MPSO), grey wolf optimizer (GWO), and hybrid of PSO-GWO in improving SVM classification accuracy by selecting the best values for those parameters. To the best of our knowledge, hybrid PSO-GWO has never been used in SVM optimization. The results show that these optimizers have a significant impact on increasing SVM accuracy. The best accuracy of the model with traditional SVM was 87.885%. After optimization, the highest accuracy obtained with GWO is 91.053% while PSO, hybrid PSO-GWO, and MPSO best accuracies are 90.736%, 90.657%, and 90.557%, respectively.

Research on Sports Performance Prediction Based on BP Neural Network

“…Category 3 means the MEPSO (multiexemplar particle swarm optimization) proposed in [56]. Category 4 means the OSVM (optimized support vector machine) which is proposed by Mahmood and Qasim [57]. Category 5 means the LRFAR (linear regression factor analysis regression) scheme [58].…”

Network Media Public Opinion and Social Governance Supported by the Internet-of-Things Big Data

“…Dang et al [16] evaluated the network hot spots by cleaning the text of the NPO from the network, combining it with the domain dictionary and similarity calculation [16]. Mahmood and Qasim [17] used the Support Vector Machine (SVM) and Naive Bayes Model (NBM) to study NPO [17]. Huang [18] obtained Microblog and blog data through a web crawler and studied the classification of NPO texts by using the SVM model [18].…”