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Image Sentiment Analysis Using Convolutional Neural Network

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Intelligent Systems Design and Applications (ISDA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 736))

Abstract

Visual media is one of the most powerful channel for expressing emotions and sentiments. Social media users are gradually using multimedia like images, videos etc. for expressing their opinions, views and experiences. Sentiment analysis of this vast user generated visual content can aid in better and improved extraction of user sentiments. This motivated us to focus on determining ‘image sentiment analyses’. Significant advancement has been made in this area, however, there is lot more to focus on visual sentiment analysis using deep learning techniques. In our study, we aim to design a visual sentiment framework using a convolutional neural network. For experimentation, we employ the use of Flickr images for training purposes and Twitter images for testing purposes. The results depict that the proposed ‘visual sentiment framework using convolutional neural network’ shows improved performance for analyzing the sentiments associated with the images.

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Authors and Affiliations

  1. Delhi Technological University, Delhi, India

    Akshi Kumar & Arunima Jaiswal

Authors
  1. Akshi Kumar
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  2. Arunima Jaiswal
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Corresponding author

Correspondence to Arunima Jaiswal .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Niketa Gandhi

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Kumar, A., Jaiswal, A. (2018). Image Sentiment Analysis Using Convolutional Neural Network. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_45

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  • DOI: https://doi.org/10.1007/978-3-319-76348-4_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76347-7

  • Online ISBN: 978-3-319-76348-4

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