You work as a social media moderator for your firm. Your key responsibility is to tag uploaded content (images) during Pride Month based on its sentiment (positive, negative, or random) and categorize them for internal reference and SEO optimization.
Task
Your task is to build an engine that combines the concepts of OCR and NLP that accepts a .jpg file as input, extracts the text, if any, and classifies sentiment as positive or negative. If the text sentiment is neutral or an image file does not have any text, then it is classified as random.
Data
You must use an external dataset to train your model. The attached dataset link contains the sample data of each category [Positive | Negative | Random] and test data.
Data files
| File name | Description |
| Test.zip | Contains image files to be classified |
| Sample.zip | Contains sample image files belonging to each category |
| Test.csv | Predictions file containing indices of test data and a blank target column |
| sample_submission.csv | Submission format to be followed for uploading predictions |
Data description
| Column name | Description |
| Filename | File name of test data image |
| Category | Target column [values: 'Positive'/'Negative'/'Random'] |
Submission format
Please refer to sample_submission.csv for more details
Evaluation criteria
score=100∗recall_score(actual_values,predicted_values)
