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Nowadays, understanding customer’s review has become very crucial for any business to improve their market strategies, customer experience and profitability. Sentiment analysis is one of the techniques that are used majorly in such scenarios. Sentiment analysis is the process in which we identify the sentiments of data in the form of text, audio or visuals and classify them into different classes as positive, negative or neutral. This can be performed based on polarity separation of emotions. This study proposes a solution to perform sentiment analysis in the form of website and proposes a solution by combining sentiment analysis of images captured from webcam and text accepted by user. Various machine learning and deep learning models are used here like DeepFace, NLTK, OpenCV, Haar Cascade Classifier and more. Also, the paper has demonstrated a system design of how to implement it in website format using HTML, CSS and JavaScript and connect to Python using Flask.

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Multi-modal Based Sentiment Analysis Web Application Using Flask and Deep Learning Models

  • 会议论文

  • 发表日期：

    2025年

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  • Gulshan Hariyani,
  • Aniket Singh,
  • Prathamesh Patil,
  • Virag Kothari,
  • Preeti Kale

摘要

Nowadays, understanding customer’s review has become very crucial for any business to improve their market strategies, customer experience and profitability. Sentiment analysis is one of the techniques that are used majorly in such scenarios. Sentiment analysis is the process in which we identify the sentiments of data in the form of text, audio or visuals and classify them into different classes as positive, negative or neutral. This can be performed based on polarity separation of emotions. This study proposes a solution to perform sentiment analysis in the form of website and proposes a solution by combining sentiment analysis of images captured from webcam and text accepted by user. Various machine learning and deep learning models are used here like DeepFace, NLTK, OpenCV, Haar Cascade Classifier and more. Also, the paper has demonstrated a system design of how to implement it in website format using HTML, CSS and JavaScript and connect to Python using Flask.