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| ## Bangla News Headlines Categorization Using Gated Recurrent Unit (GRU): Project Overview | ||
| - Created a tool that can categorizes the Bengali news headlines into six category (**`National, Politics, International, Sports, Amusement, IT`**) using deep recurrent neural network. | ||
| - A dataset of **`0.1 Million`** news headlines is created. In built chrome web scrapper used for scraping the news headlines from different Bengali online news portals such as **`Dainik Jugantor, Dainik Ittefaq, Dainik Kaler Kontho`** and so on. | ||
| - A dataset of **`0.13 Million`** news headlines is created. In built chrome web scrapper used for scraping the news headlines from different Bengali online news portals such as **`Dainik Jugantor, Dainik Ittefaq, Dainik Kaler Kontho`** and so on. | ||
| - **`Word embeeding`** feature represtations technique is used for extracting the semantic meaning of the words. | ||
| - A deep learning model has been built by using a **`bidirectional gated recurrent network`**. | ||
| - Finally, the model performance evaluated using various evaluation measures such as **`confusion matrix, accuracy , precision, recall and f1-score`**. | ||
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| ## Resources Used | ||
| - **Developement Envioronment :** Google Colab | ||
| - **Python Version :** 3.7 | ||
| - **Framework and Packages :** Tensorflow, Scikit-Learn, Pandas, Numpy, Matplotlib, Seaborn | ||
| - **Scrapper :** [Chrome Web Scrapper](https://chrome.google.com/webstore/detail/web-scraper-free-web-scra/jnhgnonknehpejjnehehllkliplmbmhn?hl=en) | ||
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| ## Project Outline | ||
| - Data Collection and Cleaning | ||
| - Data Summary | ||
| - Data Preparation for Model Building | ||
| - Model Development | ||
| - Model Evaluation | ||
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| ## Data Collection and Cleaning | ||
| Data is collected by creating a scraping graph using chrome web scarpper. Around `0.13 Million` Bengali news headlines of six categries are scrapped from different online news portals. The news portals are **`Dainik Jugantor, Dainik Ittefaq, Dainik Kaler Kontho`** and so on. The headlines distribution of each categories represents in the following figure. This dataset is an imbalanced dataset. | ||
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| As the headlines are small in length it is not mandatory to remove the stopwords from the headlines. After cleaning the sample data would look like this. | ||
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| ## Data Summary | ||
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| Data summary includes the information about number of documents, words and unique words have in each category class. Also, include the length distribution of the headlines in the dataset. | ||
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