Sentiment Analysis and Hashtag Exploration on Twitter Data using Python

This Python script performs sentiment analysis and hashtag exploration on a Twitter dataset using Pandas, Regular Expressions, TextBlob, and Matplotlib. The script extracts tweets from a CSV file, performs various text-processing tasks, calculates sentiment scores, and visualizes the results.

Dependencies

Python 3.x

Pandas

Matplotlib

TextBlob

Description

The script performs the following tasks:

Data Loading: Loads Twitter data from a CSV file into a Pandas DataFrame.

Text Processing: Removes unnecessary columns (e.g., user_name, location). Extracts hashtags and mentions from tweets using regular expressions. Counts the number of hashtags and mentions. Analyzes sentiment using TextBlob and assigns sentiment levels (positive, negative, neutral) to each tweet.

Hashtag Exploration: Counts the occurrences of each hashtag. Identifies the top five most used hashtags. Visualizes the frequency of the top hashtags using a bar chart.

Sentiment Analysis: Visualizes the distribution of sentiment levels using a pie chart.

Results

The script generates visualizations illustrating the sentiment distribution and the most frequently used hashtags in the Twitter dataset.

Installation

To use this code, you can clone this repository using Git: git clone https://github.com/Megznaik/Analyse-twitter-data-Python.git

Contributing

Contributions are welcome! If you find any issues or improvements, you can open an issue or create a pull request.