Sentiment Analysis of Twitter Data

This project uses python libraries to perform sentiment analysis on Twitter data. The data used in this project is in the form of a csv file which contains tweets and the information associated with each tweet such as the date of the tweet, number of likes and retweets, etc. The goal of this project is to perform sentiment analysis on these tweets and perform various visualizations on the results obtained from the analysis.

Requirements

The following libraries are required to run this project:

Pandas

Numpy

Matplotlib

Seaborn

TextBlob

re

warnings

Data Cleaning

The data is first loaded into a Pandas dataframe. Then, the tweets are cleaned by removing special characters, emojis, @ mentions, links, etc. The cleaned tweets are then stored in a new column in the dataframe. After cleaning, the dataframe is checked for duplicates and the duplicates are removed.

Sentiment Analysis

The sentiment analysis is performed using the TextBlob library. The subjectivity and polarity of each tweet are calculated and the polarity values are used to determine the sentiment of each tweet. The sentiment is classified as Positive, Neutral, or Negative.

Visualizations

Various visualizations are performed on the sentiment analysis results. These visualizations include:

Scatter plot showing the polarity and subjectivity of each tweet Histogram showing the distribution of sentiments Line plot showing the number of likes by sentiment and by month Bar plot showing the number of tweets by sentiment

Conclusion

This project demonstrates the use of Python libraries to perform sentiment analysis on Twitter data. The results of the sentiment analysis are visualized using various plots to give a better understanding of the sentiment of the tweets.