This repository contributes for sentiment analysis task used for different datasets. Available are collections of machine learning algorithms for sentiment analysis. The datasets are collected from different sources and introduced in the various papers.
The Social Media Sentiments Analysis Dataset captures a vibrant tapestry of emotions, trends, and interactions across various social media platforms. This dataset provides a snapshot of user-generated content, encompassing text, timestamps, hashtags, countries, likes, and retweets. Each entry unveils unique stories—moments of surprise, excitement, admiration, thrill, contentment, and more—shared by individuals worldwide.
Key Features
| Feature | Description |
|---|---|
| Timestamp | Date and time information |
| Text | User-generated content showcasing sentiments |
| User | Unique identifiers of users contributing |
| Platform | Social media platform where the content originated |
| Hashtags | Identifies trending topics and themes |
| Likes | Quantifies user engagement (likes) |
| Retweets | Reflects content popularity (retweets) |
| Country | Geographical origin of each post |
| Sentiment | Categorized emotions |
How to Use The Social Media Sentiments Analysis Dataset 📊
The Social Media Sentiments Analysis Dataset is a rich source of information that can be leveraged for various analytical purposes. Below are key ways to make the most of this dataset:
Sentiment Analysis: Explore the emotional landscape by conducting sentiment analysis on the "Text" column. Classify user-generated content into categories such as surprise, excitement, admiration, thrill, contentment, and more. Temporal Analysis: Investigate trends over time using the "Timestamp" column. Identify patterns, fluctuations, or recurring themes in social media content. User Behavior Insights: Analyze user engagement through the "Likes" and "Retweets" columns. Discover popular content and user preferences. Hashtag Trends: Identify trending topics and themes by analyzing the "Hashtags" column. Uncover popular or recurring hashtags. Geographical Analysis: Explore content distribution based on the "Country" column. Understand regional variations in sentiment and topic preferences. User Identification: Use the "User" column to track specific users and their contributions. Analyze the impact of influential users on sentiment trends.
Source: https://www.kaggle.com/datasets/kashishparmar02/social-media-sentiments-analysis-dataset <<<<<<< HEAD