Sentiment Analysis on the Instagram Application

Table of Contents

• Overview
• Data Source
• Preparing and Installation
  • Preparing Dataset
  • Installation
• References

Overview

The aim of this project is to analyze sentiment regarding the use of the Instagram application to understand people's views and responses. This sentiment analysis process uses machine learning techniques which are carried out using a set of datasets. This project is designed to classify Instagram-related public comments into two categories, namely positive and negative sentiment.

Data Source

This project uses a dataset sourced from the kaggle.com site, where the dataset contains user data, ratings and comments on Instagram application from Google Playstore. Initial data used in this project amounting to around 3,080,209 data

https://www.kaggle.com/datasets/bwandowando/3-million-instagram-google-store-reviews

Getting Started

We will prepare this project, starting from getting the dataset, installation, and running the code.

Preparing Dataset

If you want to get the entire dataset, you can download it from the link below.

https://drive.google.com/file/d/1TA4kfdNGZVA3xDl_cEshSXoYZZQwXGd5/view

If you want to download dataset manually, you can download and get the link from the data source section. After that you can follow this steps :

• Clone this repository.

    https://github.com/Agastiya/instagram-app-sentiment-analysis.git
  

• Create a directory called dataset.

    mkdir Dataset
  

• Extract dataset from download and put the INSTAGRAM_REVIEWS.csv into the directory.

Installation

pip install -r requirement.txt

After installing all the libraries we need, you can run the block code step by step.

References

• https://www.kaggle.com/code/ankumagawa/sentimental-analysis-using-naive-bayes-classifier