Social Media Sentiment Analysis

Project Overview

This project performs sentiment analysis on social media posts to gain insights into user sentiments, platform usage, hashtag popularity, and engagement metrics. The analysis includes visualizations and statistical summaries to interpret the data effectively.

Dataset

The project uses the dataset sentimentdataset.csv, which contains social media posts with the following columns:

• Id: Unique identifier for each post.
• Timestamp: Date and time of the post.
• Text: Content of the post.
• Sentiment: Sentiment classification (e.g., Positive, Negative, Neutral).
• User: User who made the post.
• Platform: Social media platform (e.g., Facebook, Twitter, Instagram).
• Hashtags: Hashtags included in the post.
• Country: Country where the user is located.
• Likes: Number of likes the post received.
• Retweets: Number of retweets (if applicable).

Code Explanation

1. Importing Libraries

The code begins by importing necessary libraries:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

• pandas: For data manipulation and analysis.
• numpy: For numerical operations.
• matplotlib.pyplot: For creating static, animated, and interactive visualizations.
• seaborn: For statistical data visualization.

2. Loading the Dataset

The dataset is loaded into a pandas DataFrame:

df = pd.read_csv('sentimentdataset.csv')

This reads the CSV file into a DataFrame named df.

3. Data Cleaning

The dataset is cleaned to remove unnecessary columns and convert data types:

df = df.drop(columns=['Id'])  # Drop the 'Id' column
df['Timestamp'] = pd.to_datetime(df['Timestamp'])  # Convert 'Timestamp' to datetime

4. Exploratory Data Analysis (EDA)

Sentiment Distribution

The distribution of sentiments is visualized using a bar chart:

sentiment_counts = df['Sentiment'].value_counts()
plt.figure(figsize=(10, 6))
sns.barplot(x=sentiment_counts.index, y=sentiment_counts.values)
plt.title('Sentiment Distribution')
plt.xlabel('Sentiment')
plt.ylabel('Count')
plt.show()

• sentiment_counts: Counts the occurrences of each sentiment.
• sns.barplot(): Creates a bar chart to visualize sentiment distribution.

Platform Distribution

The proportion of posts from different platforms is visualized using a pie chart:

platform_counts = df['Platform'].value_counts()
plt.figure(figsize=(10, 6))
plt.pie(platform_counts, labels=platform_counts.index, autopct='%1.1f%%', startangle=140)
plt.title('Platform Distribution')
plt.show()

• platform_counts: Counts the number of posts per platform.
• plt.pie(): Creates a pie chart to show the distribution of posts across platforms.

Country Distribution

The number of posts by country is visualized using a bar chart:

country_counts = df['Country'].value_counts()
plt.figure(figsize=(12, 8))
sns.barplot(x=country_counts.index, y=country_counts.values)
plt.title('Country Distribution')
plt.xlabel('Country')
plt.ylabel('Number of Posts')
plt.xticks(rotation=90)
plt.show()

• country_counts: Counts the number of posts from each country.
• sns.barplot(): Creates a bar chart to visualize the distribution of posts by country.

Hashtags Analysis

The most frequently used hashtags are visualized using a bar chart:

# Extract hashtags and count their occurrences
hashtags = df['Hashtags'].str.split(',', expand=True).stack()
hashtag_counts = hashtags.value_counts()
plt.figure(figsize=(12, 8))
sns.barplot(x=hashtag_counts.index[:10], y=hashtag_counts.values[:10])
plt.title('Top 10 Hashtags')
plt.xlabel('Hashtag')
plt.ylabel('Count')
plt.xticks(rotation=90)
plt.show()

• hashtags: Splits and counts occurrences of hashtags.
• sns.barplot(): Creates a bar chart to show the top 10 hashtags.

5. Platform-Specific Analysis

For each platform, the code performs additional analyses, such as top hashtags and engagement metrics:

for platform in df['Platform'].unique():
    platform_data = df[df['Platform'] == platform]
    
    # Example: Top hashtags for each platform
    hashtags = platform_data['Hashtags'].str.split(',', expand=True).stack()
    hashtag_counts = hashtags.value_counts()
    plt.figure(figsize=(12, 8))
    sns.barplot(x=hashtag_counts.index[:10], y=hashtag_counts.values[:10])
    plt.title(f'Top 10 Hashtags for {platform}')
    plt.xlabel('Hashtag')
    plt.ylabel('Count')
    plt.xticks(rotation=90)
    plt.show()

This loop analyzes and visualizes data specific to each platform.

Conclusion

The analysis provides insights into social media sentiment trends, platform usage, and hashtag popularity. The visualizations help understand user engagement and sentiment distribution effectively.

Contribution

Feel free to contribute by submitting pull requests or opening issues. Your feedback and suggestions are welcome!

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

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