tweeter_semantic_analysis

URL: https://github.com/priyankatalapalli/tweeter_semantic_analysis

Description

This repository contains a dataset of tweets classified into one of four categories: Regular, Sarcasm, Figurative, and Irony. The goal is to perform sentiment analysis on these tweets to gauge their impact and categorize them accordingly. The analysis also includes architecture-level analysis and emotion mining, supported by various visualizations such as histograms, density plots, bar plots, and pie charts.

Methods

The project focuses on sentiment analysis and emotion mining for tweet data. The methodology includes:

• Sentiment Analysis: This involves analyzing the sentiment of the tweets (positive, negative, neutral) and gauging their impact.
• Emotion Mining: Emotions expressed in tweets are categorized and visualized using various chart types (histograms, density plots, etc.).
• Visualization: The analysis is presented through charts to give insights into the distribution of different tweet types and sentiments.

Results

The repository does not provide explicit results, but the process includes the generation of visualizations such as histograms, density plots, bar plots, and pie charts to represent sentiment analysis and emotion mining.

Models

No specific models are mentioned, but sentiment analysis and emotion mining are core components of the project. The implementation likely uses common techniques for these tasks (e.g., NLP methods for sentiment classification and emotion extraction).

Dataset

The dataset contains tweets classified into the following four categories:

• Regular
• Sarcasm
• Figurative
• Irony

The dataset is used for performing sentiment and emotion analysis to explore the distribution of these categories within the dataset.