A Literature Survey On Sentiment Analysis Using Image Processing
Srijan Sen, Shourov Sikde · IOSR Journal of Computer Engineering · 2025
Sentiment analysis, the automated process of determining emotions and opinions expressed in content, has evolved to encompass visual media, allowing for a deeper understanding of sentiments conveyed in images. This paper explores the application of image processing techniques, coupled with Python programming, to conduct sentiment analysis by extracting emotional cues from visual data. The study begins with an overview of the significance and challenges of sentiment analysis in images, emphasizing the need for advanced tools to analyze the ever-growing volume of visual content on the internet. Leveraging Python's rich ecosystem of libraries, the paper delves into the technical aspects of sentiment analysis using image data. Key components of this research include the utilization of Convolutional Neural Networks (CNNs) for feature extraction, pre-trained models for sentiment recognition, and the development of custom datasets to train and validate sentiment analysis models. Python libraries like TensorFlow and Keras provide a robust framework for building and deploying deep learning models. The paper discusses the ethical considerations related to image-based sentiment analysis, addressing concerns about privacy, bias, and cultural nuances. It also explores the potential applications of this technology, ranging from brand sentiment analysis in marketing to monitoring public sentiment on social media platforms. Furthermore, the study identifies challenges and opportunities in the field, paving the way for future research endeavors. By bridging the domains of computer vision, natural language processing, and machine learning, sentiment analysis by image processing in Python opens up new avenues for understanding the emotional impact of visual content in an increasingly digital and visually driven world.