renegadeplatypus/sentiment-analysis

Sentiment-Analysis

Sentiment analysis, also known as opinion mining, is a powerful natural language processing (NLP) technique that involves the automated extraction of sentiments, emotions, or opinions from text data. It plays a crucial role in understanding and interpreting the subjective information expressed in written or spoken language. Sentiment analysis has gained immense importance in recent years for several compelling reasons.

First and foremost, sentiment analysis helps businesses and organizations gain valuable insights into public sentiment towards their products, services, and brands. By analyzing customer reviews, social media posts, and other textual data sources, companies can gauge how their offerings are perceived in the market. This information is invaluable for making data-driven decisions, improving products, and enhancing customer satisfaction.

For this project, I have used two sentiment analysis algorithms - "VADER" and "Roberta" and have compared the results. Sentiment analysis was performed on a dataset containing food reviews of various products on Amazon.

Setup

1. Download sentiment analysis notebook

2. Upload the notebook on an IDE(eg. Jupyter, Google Colab, etc.)

3. Download the csv file. Link:- https://drive.google.com/drive/folders/1de1HyVhw1RYX6tX6K9q3zyPERix99i46?usp=sharing

4. Upload the csv file in your IDE

5. Run all the commands(It may take some time since calculating results for a large no. of values, however, the values can be modified as and when required. Default is 5000)