An automated learning model for sentiment analysis and data classification of Twitter data using balanced CA-SVM

Abstract

The modern society runs over the social media for their most time of every day. The web users spend their most time in social media and they share many details with their friends. Such information obtained from their chat has been used in several applications. The sentiment analysis is the one which has been applied with Twitter data set toward identifying the emotion of any user and based on those different problems can be solved. Primarily, the data as of the Twitter database is preprocessed. In this step, tokenization, stemming, stop word removal, and number removal are done. The proposed automated learning with CA-SVM based sentiment analysis model reads the Twitter data set. After that they have been processed to extract the features which yield set of terms. Using the terms, the tweets are clustered using TGS-K means clustering which measures Euclidean distance according to different features like semantic sentiment score (SSS), gazetteer and symbolic sentiment support (GSSS), and topical sentiment score (TSS). Further, the method classifies the tweets according to support vector machine (CA-SVM) which classifies the tweet according to the support value which is measured based on the above two measures. The attained results are validated utilizing k-fold cross-validation methodology. Then, the classification is performed by utilizing the Balanced CA-SVM (Deep Learning Modified Neural Network). The results are evaluated and compared with the existing works. The Proposed model achieved 92.48 % accuracy and 92.05% sentiment score contrasted with the existing works.

Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter Data

“…The convolutional function provides a solution by reducing the number of parameters for improving network depth, but it still uses a large number of parameters. The normalisation layer normalises the activation as well as gradient propagation with the network, which simplifies the optimal issue of the trained network [25,26]. The batch normalisation layer, which is used in conjunction with the convolution layer and non-linearity, is used to accelerate network training and reduce sensitivity to network initialization [27][28][29].…”

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

“…The procedure aims to define the best group of routes in CHs to the BS through an FF that contains two variables, such as distance and energy [49][50][51][52][53]. Initially, the RE of the next-hop node is described, and the node with maximum energy serves as the RN.…”

LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications

“…The healthcare IoT data sets and performance criteria for the proposed MR-LSGDM strategy are briefly outlined in this section [ 31 ]. The complete approach was developed using the MATLAB 2021a tool on a Core i3-3110M processor running Windows 8 with 2 GB RAM, and it was tested on 8 healthcare IoT data sets ( Table 1 ) [ 32 ].…”