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Deep Learning Method to Identify the Demographic Attribute to Enhance Effectiveness of Sentiment Analysis

  • Akula V. S. Siva Rama Rao
  • P. Ranjana
Chapter
  • 36 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 103)

Abstract

Sentiment analysis and machine-learning techniques play an important role in analyzing social media networks datasets. The customers, who have different levels of demographic attributes pouring views, reviews and feedback on various products and services in social media networks everyday life, this enormous data emerged as major source to extract knowledge to take appropriate decision by companies and business organizations. Most of the sentiment analysis processes ignoring various demographic attributes of customers such as sex, age, occupation, income, location, etc. Different levels of demographic attributes of a customer have their own custom purchase preferences. Depending on the sex, customers will have different preferences, habits and taste of purchasing items. The proposed method focused on sex demographic attribute analysis of the customer to yield effective low-level analysis results. The major challenge in the proposed method is identifying the sex (Male/Female) of the customer by using South Indian names. The proposed system implemented using multi-layer perceptron deep learning method and achieved best train and test accuracy results than decision tree, random forest, k-neighbors, support vector machine (SVM), Naive Bayes. The low-level demographic attribute feature extraction analysis enhanced the effectiveness of the sentiment analysis.

Keywords

Social media networks Demographic attribute Feature extraction Deep learning Machine learning NLP Sentiment analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Akula V. S. Siva Rama Rao
    • 1
    • 2
  • P. Ranjana
    • 3
  1. 1.Research Scholar, Department of Computer Science and EngineeringHindustan Institute of Technology and ScienceChennaiIndia
  2. 2.Associate Professor, Dept. of CSESITETadepalligudemIndia
  3. 3.Professor, Department of Computer Science and EngineeringHindustan Institute of Technology and ScienceChennaiIndia

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