TikTok is a social media platform for creating, sharing, and discovering short-form videos. TikTok allows users to create short videos that can be up to 60 seconds and more, featuring music, sound effects, filters, and other creative tools.
Users can also interact with other users' videos by commenting, liking, and sharing them. Despite its entertaining content, this application is not liked by everyone. If you skim through its reviews, you’ll find a mixture of hate and support for TikTok all over the world.
Sentiment analysis of reviews is a type of natural language processing (NLP) that involves analyzing the opinions, emotions, and attitudes expressed in a piece of text, typically a review or a feedback.
The goal of sentiment analysis is to identify and extract subjective information from the text, such as whether the text expresses a positive, negative, or neutral sentiment or opinion. Let's analyze TikTok Reviews to find out what people feel about the content of this app.
This project will go through the task of TikTok reviews sentiment analysis. The dataset I am gonna use for sentiment analysis was downloaded from kaggle. It was scrapped from TikTok reviews on Google play store and it has been processed to a format that fit for sentiment analysis, though more data manipulations would be done.
Data source: click here to access the Data Source
About Dataset
The content of the various columns is listed below. Please find the description for each column.
- Column Name: Column Description
- userName: Name of a User
- userImage: Profile Image that a user has
- content: This represents the comments made by a user
- score: Scores/Rating between 1 to 5
- thumbsUpCount: Number of Thumbs up received by a person
- reviewCreatedVersion: Version no on which the review iscreated
- at: Created At
- replyContent: Reply to the comment by the Company
- repliedAt: Date and time of the above reply
- reviewId: unique identifier
Access the full report and codes here
Summary
The sentiment analysis involves analyzing the language used in user reviews and determining whether it expresses a positive or negative sentiment towards the TikTok app. This can be done using natural language processing techniques and machine learning algorithms.
After analyzing a large sample of TikTok users' reviews and ratings generated from TikTok google play, the sentiment analysis provided insights into the overall perception of the TikTok app among its users.The sentiment analysis reveals that the majority of users have a positive sentiment towards the app, with many praising its ease of use and entertainment value with 73.6% five stars ratings.
Conclusion
Conducting sentiment analysis on TikTok users' reviews and ratings using Python can provide valuable insights into user perceptions of the app. By analyzing the language used in these reviews, it is possible to determine the overall sentiment towards the app and identify areas where improvements could be made to enhance user satisfaction.