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Visual Content Like Image Sentiment Analysis In Social Media: Review

Authors: Shaikh Shafi Shadulla *, Tayade Pooja Mahendra;

Visual Content Like Image Sentiment Analysis In Social Media: Review

Abstract

In today’s social media everything is online. People express themselves very openly and convey their message, opinion, emotion, sentiments, and attitudes towards entities such as products, services, organizations, , individuals, issues, events, topics, and their attributes. To express on social media there is not only text message available but also you now can use images, emoticons, videos, likes and dislikes, graphics, stickers etc. As it becoming easier to click or capture and handle photo, image with handheld devices like camera, smartphones, phablets, tablets etc., Social media users are intending to say through visual and textual content rather than only text. Use of images, videos, with combination of text becoming so popular. Visual content speaks on your behalf very strongly. A single image replaces thousands of words. Visual content is combination on images videos, color, tone, texture, lines, shapes and many more. Human brain can sense the sentiment as soon as he visuals content. So the question arises here is that, can a machine do that? Is there any way to say what emotion, sentiment feeling user trying to say through images.AS machine learning, Artificial Intelligence is coming to play its part. Many researchers have tried and many are trying to explore visual content sentiment analysis. It’s very challenging task ahead of researchers. Significant work has carried out in this area although it is in its basic form. So in this paper we are trying to explore different researchers work.

Keywords

Sentiment, Images, Visual Content.

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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