https://github.com/hiteshmeta85/ml-mini-project-web

Binary Classification of Disaster related tweets from Social Media using BERT Model
https://github.com/hiteshmeta85/ml-mini-project-web

axios dark-mode machine-learning nextjs responsive tailwind typescript

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Binary Classification of Disaster related tweets from Social Media using BERT Model

• Host: GitHub
• URL: https://github.com/hiteshmeta85/ml-mini-project-web
• Owner: hiteshmeta85
• Created: 2022-10-06T16:16:03.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-10-20T20:30:58.000Z (over 2 years ago)
• Last Synced: 2024-10-27T21:41:46.823Z (3 months ago)
• Topics: axios, dark-mode, machine-learning, nextjs, responsive, tailwind, typescript
• Language: TypeScript
• Homepage:
• Size: 4.21 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Machine Learning Mini Project

### Undergrad CSE: Sem V

### Classification of Disaster related Tweets from Social Media

At the time of a disaster, many people ask for help through social media. They make tweets on Twitter asking for

immediate rescue. Extraction of raw data from social media like Twitter based on a few parameters like disaster type,

location, and disaster-related hashtags. The tweets extracted have noise - unwanted data which needs to be filtered out.

Our models would classify disaster-related tweets. These classified tweets can then be used to identify those people who

need help and using this information higher authorities can take quick actions.

---

Nodejs Version

> Node -v

> 16.15.1

### Tech Used

```

1. Next.js

2. Tailwind

3. Typescript

4. Axios

```

### Getting Started

---

First, install the required packages:

```

npm install

```

Then, configure the .env file:

```

Check the .env.example file

```

Then, run the development server:

```

npm run dev

```

---

### Sample Images

1. Select Disaster Type

   ![](public/sample-images/index.png)

   

2. Enter Custom Hashtags

   ![](public/sample-images/custom-hashtags.png)

   

3. Raw Data scraped from Twitter using Twint

   ![](public/sample-images/raw-data.png)

   

4. Binary Classified Data using BERT Model

   ![](public/sample-images/binary-classified-data.png)

   

5. BERT model flow for Binary Classification of Text

   ![](public/sample-images/model-flow.png)