Visualize Twitter sentiment over time using a smoothed line plot

Introduction

Twitter is a widely used social media platform where people share their opinions and emotions about different topics. These opinions can be positive, negative, or neutral. Sentiment analysis helps in understanding these emotions by analyzing the text of tweets.

In this project, we study how sentiments expressed in tweets change over time using data visualization.

Objective

The main objectives of this project are:

• To analyze the sentiment of tweets

• To convert textual data into sentiment scores

• To understand how sentiment changes over time

• To visualize the trend using a smoothed line plot

Problem Explanation

The given dataset contains tweet IDs and tweet text, but it does not include time or sentiment information.

To solve this problem:

• Sentiment is derived from the tweet text by identifying positive and negative words

• Each tweet is assigned a sentiment score based on its content

• Since there is no time column, tweet order (ID) is used as a representation of time

• A smoothed line plot is used to show the overall trend of sentiment

This approach helps in identifying patterns and changes in sentiment across tweets.

Visualization

Conclusion

The visualization shows how sentiment varies across tweets. Positive values indicate positive sentiment, while negative values indicate negative sentiment.

The smoothed line plot makes it easier to observe overall trends by reducing fluctuations in the data. Even without a time column, using tweet order provides meaningful insights into sentiment patterns.

This analysis demonstrates how data visualization can help in understanding public opinion from textual data.