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A real time Multimodal Emotion Recognition web app for text, sound and video inputs
| Date | Stars |
|---|---|
| 2026-07-24 | 1087 |
| 2026-07-25 | 1087 |
| 2026-07-28 | 1087 |
| 2026-07-30 | 1087 |
| 2026-08-06 | 1087 |
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# Real-Time Multimodal Emotion Recognition <img alt="GitHub followers" src="https://img.shields.io/github/followers/maelfabien.svg?style=social"> <img alt="GitHub contributors" src="https://img.shields.io/github/contributors-anon/maelfabien/Multimodal-Emotion-Recognition.svg"> <img alt="GitHub commit activity" src="https://img.shields.io/github/commit-activity/y/maelfabien/Multimodal-Emotion-Recognition.svg"> <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/3.svg"> Don't hesitate to ⭐ the repo if you enjoy our work ! ## In a nutshell We developped a multimodal emotion recognition platform to analyze the emotions of job candidates, in partnership with the French Employment Agency. We analye facial, vocal and textual emotions, using mostly deep learning based approaches. We deployed a web app using Flask :  The tool can be accessed from the WebApp repository, by installing the requirements and launching `main.py`. We have also written a paper on our work : https://www.overleaf.com/read/xvtrrfpvzwhf ## Table of Content : - [I. Context](https://github.com/maelfabien/Multimodal-Emotion-Recognition/blob/master/README.md#i-context) - [II. Data Sources](https://github.com/maelfabien/Multimodal-Emotion-Recognition/blob/master/README.md#ii-data-sources) - [III. Downloads](https://github.com/maelfabien/Multimodal-Emotion-Recognition/blob/master/README.md#iii-download) - [IV. Methodology](https://github.com/maelfabien/Multimodal-Emotion-Recognition/blob/master/README.md#iv-methodology) - [a. Text Analysis](https://github.com/maelfabien/Multimodal-Emotion-Recognition/blob/master/README.md#a-text-processing) - [b. Audio Analysis](https://github.com/maelfabien/Multimodal-Emotion-Recognition/blob/master/README.md#b-audio-processing) - [c. Video Analysis](https://github.com/maelfabien/Multimodal-Emotion-Recognition/blob/master/README.md#c-video-processing) - [d. Ensemble Model](https://github.com/maelfabien/Multimodal-Emotion-Recognition/blob/master/README.md#d-ensemble-model) - [V. How to use it ?](https://github.com/maelfabien/Multimodal-Emotion-Recognition/blob/master/README.md#v-how-to-use-it-) - [VI. Research Paper](https://github.com/maelfabien/Multimodal-Emotion-Recognition/blob/master/README.md#vii-research-paper) - [VII. Deployment](https://github.com/maelfabien/Multimodal-Emotion-Recognition/blob/master/README.md#viii-deployment) In this project, we are exploring state of the art models in multimodal sentiment analysis. We have chosen to explore text, sound and video inputs and develop an ensemble model that gathers the information from all these sources and displays it in a clear and interpretable way. ## 0. Technologies  ## I. Context Affective computing is a field of Machine Learning and Computer Science that studies the recognition and the processing of human affects. Multimodal Emotion Recognition is a relatively new discipline that aims to include text inputs, as well as sound and video. This field has been rising with the development of social network that gave researchers access to a vast amount of data. ## II. Data Sources We have chosen to diversify the data sources we used depending on the type of data considered. All data sets used are free of charge and can be directly downloaded. - For the text input, we are using the **Stream-of-consciousness** dataset that was gathered in a study by Pennebaker and King [1999]. It consists of a total of 2,468 daily writing submissions from 34 psychology students (29 women and 5 men whose ages ranged from 18 to 67 with a mean of 26.4). The writing submissions were in the form of a course unrated assignment. For each assignment, students were expected to write a minimum of 20 minutes per day about a specific topic. The data was collected during a 2-week summer course between 1993 to 1996. Each student completed their daily writing for 10 consecutive
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matched fp:f8ea93bd7a6cc4d2, topic:deep-learning, topic:tensorflow
matched fp:f8ea93bd7a6cc4d2, name:multimodal, desc:multimodal, readme:multimodal