Enterprise big data analysis using SVM classifier and lexicon dictionary

S. Radha and C. Nelson Kennedy Babu

International Journal of Enterprise Network Management, 2020, vol. 11, issue 1, 65-75

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.

Keywords: deep web mining; sentiment analysis; big data; unstructured data; map reduce; hidden information; big data analytics; text mining; clusters; enterprise data. (search for similar items in EconPapers)
Date: 2020
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