Enterprise big data analysis using SVM classifier and lexicon dictionary

Abstract

The emergence of the digital era has led to growth in various types of data in a cloud. In fact, there may be three fourth of the total data will be treated as big data. In many organisations, massive volume of both structured and unstructured data sit idle. Various categories of data are complex for pre-processing, analysing, storing and visualising. Cloud computing provides suitable platform for big data analytics for the storage and for predicting customer behaviour to sell products. Unstructured data like emails, notes, messages, documents, notifications and Twitter comments (including from IoT devices) remains untapped and is not stored in a relational database. Valuable information on pricing, customer behaviour and competitors may be inhumed within unstructured data. This makes cloud-based analytics as an effective research field to address several issues and risks need to be reduced. So we propose a method to extract and cluster sentiment information from various types of unstructured text data from social networks by using SVM classifiers combined with lexicons and machine learning for sentiment analysis of customer behaviour feedback. The method has performed efficient data collection, data loading and efficiently performs sentiment analysis on deep and hidden web.

Two Decades of Research on Consumer Behaviour and Analytics

“…In conclusion, introduction of advanced analytics tools to predict consumer buying behaviors is also seen as moderately reported in the existing literature (Huang & Mieghem, 2013;Kitchens et al, 2018;Marinelli et al, 2020;Martínez et al, 2020;Miles, 2014;Radha & Babu, 2020).…”

“…Several scholars identified the importance of maintaining a quality database in their organizations (Ibrahim & Wang, 2019;Kakatkar & Spann, 2019;Li et al, 2020;Maheshwari et al, 2020;Silva et al, 2019). Further, it was identified that the quality datasets helped forecast the firms' future with more accurate predictions (Huang & Mieghem, 2013;Martínez et al, 2020;Radha & Babu, 2020;Reutterer et al, 2020). It was identified that advances in machine learning and consumer culture could affect the revenue, margins, and market share of a firm (Pantano, 2020;Pantano & Dennis, 2019;Pantano et al, 2019;Simchi-Levi & Wu, 2018).…”