Abstract
In recent times, generation of big data takes place in an exponential way from diverse textual data sources like review sites, media, blogs, etc. Sentiment analysis (SA) finds it useful to classify the opinions of the big data to different kinds ofsentiments. Therefore, SA on big data helps a business to take beneficial commercial understandings from text based content. Though several SA approaches have been presented, yet, there is a need to improve the performance of SA to interpret the customer’s feedback and increase the product quality.This paper introduces a novel social spider optimization based feature selection based wavelet kernel extreme learning machine (SSO-WKELM) model. The proposed model initially undergoes pre-processing to remove the unwanted word removal. Then, Term Frequency-Inverse Document Frequency (TF-IDF) is utilized as a feature extraction technique to extract the set of feature vectors. Besides, a social spider optimization (SSO) algorithm is utilized for feature selection process and thereby achieves improved classification performance. Subsequently, WKELM is employed as a classifier to classify the incidence of positive or negative user reviews. For experimental validation, a Product review dataset derived from Amazon along with synthetic data is used. The experimental results stated the superior classification performance of the SSO-WKELM model.
Highlights
Nowadays, electronic-commerce is extensively developed as a simulation of using Internet [1]
Big data is referred as massive amount of data which is derived from web pages, social media, remote sensing data, and clinical archives, etc,in a structured, semi-structured, or unstructured manner [3]
This paper introduces a novel optimal social spider optimization based feature selection (FS) with wavelet kernel extreme learning machine (SSO-WKELM) method
Summary
Electronic-commerce is extensively developed as a simulation of using Internet [1]. R.Aravind Babu b Fig. 1.Data complexity in big data Prediction of sentiments in decisions making is a major concern used for preventing the wrong solution in human-based actions of businesses. SA is depicted as a principle of examining the emotions expressed in a text [7] In this competing world, learning the user demand and market-related manufacturing is one of the promising objectives in businesses. Sharef et al [9] outlined the advanced models in SA, such as sentiment polarity forecasting, SA features, sentiment classification models, and utilities of SA.Graham et al [10] examined the classification of “big data logistics.” It has provided a novel view for accurate words and predict “positive” and “negative” emotions. In data theory, selecting the term wi from W and election of document dj from D has been assumed
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