This project focuses on analyzing and categorizing comments on Instagram food posts. The dataset consists of two columns: "comments" and "label." The "comments" column contains the text of the comments, while the "label" column indicates whether a comment is positive (1) or negative (0).
data/: This directory contains the dataset used for training and testing the model. Data is uploaded and you can use it for just tutorial purpose.
comments_data.csv: The CSV file containing the comments and corresponding labels. src/: This directory contains the source code for the project.
bash Copy code git clone https://github.com/arad1367/Instagram_NLP_project.git
The dataset (Data_food.csv) is structured with two columns:
Comments: Contains the text of the comments. Label: Indicates the sentiment label (1 for positive, 0 for negative). Model Evaluation The model's performance can be assessed using metrics such as accuracy, precision, recall, and F1 score. The evaluation results and any insights gained from the analysis will be documented in the notebook or script.
Feel free to contribute to this project by opening issues or submitting pull requests. Your input and feedback are highly appreciated!
This project copyright is by GilTech Megoldások KFT. and Pejman Ebrahimi.