https://github.com/arv-anshul/yt-comment-sentiment
An end-to-end project to predict the sentiment of YouTube video comments using Machine Learning.
https://github.com/arv-anshul/yt-comment-sentiment
dashboard dvc end-to-end machine-learning mlflow python3 sklearn vue youtube
Last synced: about 1 month ago
JSON representation
An end-to-end project to predict the sentiment of YouTube video comments using Machine Learning.
- Host: GitHub
- URL: https://github.com/arv-anshul/yt-comment-sentiment
- Owner: arv-anshul
- Created: 2024-11-22T13:13:03.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-12-26T17:58:24.000Z (7 months ago)
- Last Synced: 2025-02-10T16:52:04.357Z (5 months ago)
- Topics: dashboard, dvc, end-to-end, machine-learning, mlflow, python3, sklearn, vue, youtube
- Language: Vue
- Homepage: https://arv-anshul.github.io/yt-comment-sentiment
- Size: 3.83 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# YouTube Comment Sentiment
An end-to-end project to predict the sentiment of YouTube video comments using Machine Learning.
## Overview
This project focuses on building a sentiment analysis system for YouTube comments, complete with a FastAPI-based
inference endpoint and insights-providing API endpoints. The development process included robust experimentation,
tracking, and pipeline reproduction (using **MLFlow** and **DVC**).
### Key Features
- **Inference Endpoint:** Built using the FastAPI framework to classify sentiment of comments.
- **Insights Endpoints:** Additional APIs to provide analytics around comment sentiments.
- **Experiment Tracking:** Leveraged MLFlow for tracking experiments.
- **Pipeline Reproduction:** Utilized DVC (Data Version Control) for reproducibility.
- **Text Vectorization:** Used `TfidfVectorizer` for transforming text data into feature vectors.
- **Model Selection:** Experimented with various models and selected `HistGradientBoostingClassifier` as the
best-performing classifier.
## Experimentation
The experimentation phase focused on optimizing hyperparameters for the `TfidfVectorizer` and
`HistGradientBoostingClassifier` model. Below is a screenshot showcasing how different hyperparameter combinations
impacted accuracy:
## Tech Stack
| Tech | Stack |
| -------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Data Handling** |  |
| **Backend Tools** |    |
| **Machine Learning** |   |
| [**Frontend**](./frontend) |      |
| **Dev Tools** |      |
## :warning: Improvements
1. [x] Merge **both classifier model and vectorizer model** which reduce the complexity of loading them using using
`MLFLOW_RUN_ID` in [`app.py`](backend/app.py).
2. [x] After completing previous step, load model using `MLFLOW_MODEL_URI` env instead of `MLFLOW_RUN_ID` env.
3. [ ] ⚠️ Try to use `MLproject` file to run ML Pipeline steps instead of [`dvc.yaml`](dvc.yaml) file. _(Only if
Possible)_
- [ ] Also investigate the use `dvc` here and try to know WHY, WHAT and HOW (part of it).
4. [ ] Know the clear distinction and involvement between the source code of ML Pipeline, Backend.
### Sentiment Model
- We can handle the imbalance(ness) of the dataset training _which might improve the model metrics_.
- We can also find more diverse data for this because **I have seen (while EDA) that it contains many political
comments**.
- [clapAI/MultiLingualSentiment](https://huggingface.co/datasets/clapAI/MultiLingualSentiment) from HuggingFace.
- We can use different text vectorization steps such as **Vectorization + PCA**.
- We can fine-tune a [BERT model](https://hf.co/arv-anshul/comment-sentiment-bert) and use it instead.
---
> \[!IMPORTANT\]
>
> Feel free to explore and contribute!