This is a title slide titled "Twitter Sentiment Analysis Summary." It displays the main text "IM6423 Data Visualization Assignment" with the subtitle "Twitter Sentiment Analysis Project Summary."

IM6423 Data Visualization Assignment

Twitter Sentiment Analysis Project Summary

Source: IM6423 Data Visualization Assignment [Icons: Twitter bird, charts]

This agenda slide outlines the structure for a Twitter Sentiment Analysis presentation. It includes study overview and methodology, exploratory/multivariate analysis with plots, advanced charts and text visualizations, and key findings with recommendations/conclusions.

Presentation Agenda

1. Study Overview and Methodology

Introduction to Twitter Sentiment Analysis project and research methodology.

2. Exploratory and Multivariate Analysis

Exploratory data analysis with histograms, boxplots, scatter plots.

3. Advanced Charts and Text Visualization

Structural neutrality, parallel coordinates, funnel charts, word clouds.

4. Key Findings, Recommendations, Conclusions

Summary of insights, actionable recommendations, and final conclusions. Source: IM6423 Data Visualization Assignment on Twitter Sentiment Analysis

This slide overviews a sentiment analysis study on 10K Twitter tweets about [topic], classifying sentiments as positive, negative, or neutral. Goals include visualizing patterns and insights via charts, using Python, Matplotlib, and NLTK.

Study Overview

• Sentiment analysis of Twitter data (positive/negative/neutral)
• Dataset: 10K tweets on [topic]
• Goals: Visualize patterns and insights via charts
• Tools: Python, Matplotlib, NLTK

Source: IM6423 Data Visualization Assignment

The Methodology workflow slide outlines four phases for tweet sentiment analysis: Data Collection via Twitter API, Preprocessing with Python (NLTK, pandas), Sentiment Scoring using VADER, and Visualization Pipeline with Matplotlib and Seaborn. It describes gathering tweets, cleaning text and removing stopwords, computing compound sentiment scores, and generating histograms, word clouds, and similar visuals.

Methodology

{ "headers": [ "Phase", "Description", "Tools" ], "rows": [ [ "Data Collection", "Gather tweets via API", "Twitter API" ], [ "Preprocessing", "Clean text, remove stopwords", "Python (NLTK, pandas)" ], [ "Sentiment Scoring", "Compute compound sentiment scores", "VADER" ], [ "Visualization Pipeline", "Generate histograms, word clouds, etc.", "Matplotlib, Seaborn" ] ] }

This Exploratory Analysis slide uses histograms to show sentiment distribution patterns and boxplots to compare tweet lengths by sentiment. Key statistics indicate 45% positive and 30% negative tweets.

Exploratory Analysis

!Image

• Histograms show sentiment distribution patterns
• Boxplots compare tweet length by sentiment
• Key stats: 45% positive, 30% negative tweets

Source: Wikipedia

Neutral tweets consistently achieve higher retweet counts than biased ones, preserving structural virality. Positive and negative tweets show engagement biases, but neutrality maintains network integrity.

Structural Neutrality Insights

Source: IM6423 Twitter Sentiment Analysis

The Multivariate Analysis slide reveals a moderate positive Likes-Sentiment correlation of 0.65 and a Retweets-Sentiment correlation of 0.58, consistent with the likes trend. It examines 5 key dimensions via a parallel coordinates plot.

Multivariate Analysis

• 0.65: Likes-Sentiment Correlation

Moderate positive relationship

• 0.58: Retweets-Sentiment Correlation

Consistent with likes trend

• 5: Key Dimensions Analyzed

Via parallel coordinates plot

The "Advanced Charts" slide displays a visualization of user engagement metrics. It illustrates engagement drop-off by sentiment, conversion from views to shares, and key funnel stages.

Advanced Charts

!Image

• Engagement drop-off by sentiment
• Conversion from views to shares
• Highlights key funnel stages

Source: Photo by Yusuf Onuk on Unsplash

The "Text Visualization" slide features word clouds for positive and negative sentiments, highlighting top words like "great" (positive) and "bad" (negative). Word sizing provides frequency insights, with placeholders for the actual sentiment word clouds.

Text Visualization

!Image

• Word clouds for positive/negative sentiments
• Top words: 'great' (positive), 'bad' (negative)
• Frequency insights via word sizing
• Placeholders for sentiment word clouds

Source: Wikipedia

The Key Findings slide highlights that positive tweets exhibit viral trends, while neutral tweets demonstrate structural stability. It also reveals that text patterns predict sentiment at 82% accuracy and visuals uncover sentiment biases.

Key Findings

• Positive tweets exhibit viral trends.
• Neutral tweets show structural stability.
• Text patterns predict sentiment at 82%.
• Visuals uncover sentiment biases.

The "Recommendations" slide outlines key strategies for better AI practices. It advises using balanced datasets, adopting hybrid models for accuracy, implementing real-time dashboards, and performing ethical neutrality checks.

Recommendations

• Use balanced datasets.
• Adopt hybrid models for accuracy.
• Implement real-time dashboards.
• Perform ethical neutrality checks.

The conclusions slide highlights that effective visualization uncovers sentiment dynamics and proposes future scalable analysis tools. It invites Q&A, expresses thanks with a graduation emoji, and features trophy and handshake icons.

Conclusions

• Effective visualization uncovers sentiment dynamics

• Future: Scalable analysis tools
• Q&A

Thank you! 🎓

[Icons: trophy, handshake]

Source: IM6423 Data Visualization Assignment