Download the dataset below to solve this Data Science case study on sentiment analysis.
Sentiment Analysis, often referred to as opinion mining, is a field of Natural Language Processing (NLP) that focuses on identifying and categorizing opinions expressed in the text to determine the writer’s attitude towards a particular topic or the overall contextual polarity of the text.
The provided dataset comprises user reviews of the LinkedIn app, a professional networking platform. It includes the following columns:
- Review: This column contains the actual text of the user review. It offers qualitative insights into the user’s experience with the app, highlighting praises, complaints, suggestions, and general comments.
- Rating: Numerical ratings ranging from 1 to 5 accompany the reviews, with 1 being the lowest and 5 being the highest. These ratings provide a quantitative measure of user satisfaction.
The primary objective of this study is to perform a comprehensive Sentiment Analysis of app user reviews for LinkedIn. The analysis aims to understand the general sentiment of the user base, correlating textual feedback with provided ratings. Key goals include:
- Sentiment Distribution Analysis: Assess the overall sentiment distribution among the reviews to gauge general user satisfaction or dissatisfaction.
- Identifying Key Themes:Â Examine common themes or topics within each sentiment category to pinpoint specific areas of strength or concern in the app’s user experience.
