Abstract:
Images are used on social media to convey views, opinions, feelings, and emotions.
We research the issue of knowing natural feelings from large-scale pictures of social
media in this work. We need to understand public sentiment for Social marketing, to
develop product quality, to improve customer service, social media monitoring and
research purpose. In this work we have classified happy, sad and angry emotions of
people by using their face image on social media. For the classification of pictures, we
have used Convolutional Neural Networks (CNN). First, we modified an appropriate
CNN architecture for the assessment of picture sensitivity. The findings indicate that
in picture sentiment analysis, the suggested CNN can attain stronger efficiency than
rival algorithms. By using this model we can classify image into three categories of
sentiment named happy, sad and angry and we got an accuracy of 75.28%. This work
can be further enhanced to be used in many other related areas of image classification.
