Twitter - Sentiment Analysis with Emojis // NLP

This sentiment analysis project focuses on the use of emojis to classify the emotional tone of tweets. The workflow follows these main steps:

Importing Libraries and Loading Data:

• Libraries such as pandas, numpy, and matplotlib are imported.
• A dataset of tweets, including tweet text and emojis, is loaded.

Data Preprocessing:

• Cleaning and normalizing the tweet texts.
• Extracting emojis and analyzing their frequency in the texts.

Exploratory Data Analysis:

• Visualizing the distribution of emojis in tweets.
• Grouping tweets by accounts, showing the number of tweets per account.
• Analyzing correlations between popular accounts and emoji usage.

Sentiment Modeling:

• Using Natural Language Processing (NLP) techniques to classify tweets into sentiment categories.
• Applying algorithms to predict sentiments based on the presence of emojis.

Results Visualization:

• Graphs and tables showing the relationship between used emojis and detected sentiment.
• Representation of emoji usage frequency by the most popular accounts.