🐦 Task 2 | Twitter US Airline Sentiment Analysis 💬

Welcome to the Twitter US Airline Sentiment Analysis Project! This project focuses on analyzing customer opinions shared on Twitter regarding major US airlines. ✈️ By studying text data, sentiments, and trends, we aim to understand customer satisfaction levels, identify common issues, and uncover insights that can guide airline service improvements.

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🌟 Project Overview:

Social media has become a powerful platform for customers to express their opinions about brands and services. In the airline industry, analyzing such data can provide crucial insights for:

• ✨ Understanding passenger sentiment (positive, neutral, negative)
• ✨ Identifying frequent complaints and areas of improvement
• ✨ Tracking sentiment trends across airlines
• ✨ Using Natural Language Processing (NLP) for text analysis
• ✨ Supporting business strategies with customer-driven insights In this project, we use the Twitter US Airline Sentiment Dataset 🗂️ to perform sentiment classification and extract valuable findings through preprocessing, EDA, NLP techniques, and visualizations.

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🎯 Objectives

• 🔹 Understand dataset and business context
• 🔹 Clean and preprocess textual data
• 🔹 Perform Exploratory Data Analysis (EDA)
• 🔹 Apply Natural Language Processing (NLP) techniques
• 🔹 Build interactive and meaningful visualizations
• 🔹 Generate insights on customer satisfaction levels
• 🔹 Export results into a structured report and presentation

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🛠️ Tools & Technologies Used

• Programming Language: Python 🐍
• Data Handling: Pandas, NumPy
• Text Processing: NLTK, re, WordCloud
• Vectorization: TF-IDF, CountVectorizer
• Modeling: Logistic Regression, Naive Bayes, or ML models
• Visualization: Matplotlib, Seaborn 📊, WordCloud 🌥️
• Reporting: Python-PPTX for automated PowerPoint generation 🖼️
• Environment: Jupyter Notebook / VS Code

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📂 Dataset:

📌 Dataset Source: Twitter US Airline Sentiment Dataset

📌 Columns Included:

• 🐦 tweet_id – Unique tweet ID
• 📅 tweet_created – Date & time of tweet
• ✈️ airline – Airline mentioned
• 💬 text – Tweet content
• 😃 airline_sentiment – Sentiment label (positive, neutral, negative)
• 🔍 airline_sentiment_confidence – Confidence score of classification
• 👤 user – Twitter user handle

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🔍 Steps Involved:

1️⃣ Data Collection

• Imported dataset using Pandas
• Verified structure, dimensions, and column names

2️⃣ Data Cleaning & Preprocessing 🧹

• Removed duplicates & null values
• Cleaned tweet text (stopwords, punctuation, links, hashtags, mentions)
• Tokenization & Lemmatization
• Created new features like word count, character length, hashtags count

3️⃣ Exploratory Data Analysis (EDA) 🔬

• Sentiment distribution (positive, neutral, negative)
• Airline-wise sentiment comparison
• Frequent words/phrases in negative reviews
• Time-based sentiment trends

4️⃣ Data Visualization 📊

• Bar Charts – Airline-wise sentiment counts
• Pie Charts – Sentiment proportions
• WordClouds – Frequent words in positive & negative tweets
• Heatmaps – Correlation between features
• Line Plots – Sentiment trends over time

5️⃣ Insights & Reporting 📝

Generated findings, such as:

• 🔝 Negative tweets dominated the dataset (~60%)
• ✈️ United Airlines had the highest number of negative sentiments
• 💬 Common complaints included delays, cancellations, and poor customer service
• 🌟 Virgin America received comparatively more positive feedback
• 📆 Peak complaint times observed during travel-heavy months

6️⃣ Automated PowerPoint Report 🖥️

• Exported all charts and insights into a professional PowerPoint
• Executive summary with key highlights
• Structured outputs saved in outputs/ folder

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📊 Sample Visualizations:-

• Sentiment Distribution Pie Chart
• Airline-wise Sentiment Bar Chart
• WordCloud of Negative Tweets
• Time-based Sentiment Trends

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💡 Key Insights:

• ✔️ Majority of tweets carried negative sentiment (~60%)
• ✔️ United Airlines had the most negative mentions
• ✔️ Virgin America stood out with more positive tweets
• ✔️ Customer dissatisfaction mainly revolved around delays, lost baggage, and poor staff behavior

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📑 Deliverables:

• 📌 Cleaned Dataset → outputs/Twitter_cleaned.csv
• 📌 PowerPoint Report → outputs/Twitter_Sentiment_Report.pptx
• 📌 Python Notebook / Script → Task 2.py

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🚀 Conclusion:

This project demonstrates how NLP and sentiment analysis can transform unstructured social media text into actionable business insights. By combining text preprocessing, classification models, and impactful visualizations, the analysis highlights how customer opinions can shape airline decision-making, brand image, and service improvements.

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🔗 Let's Connect:-

💼 LinkedIn: https://www.linkedin.com/in/abdullah-umar-730a622a8/

💼 Portfolio: https://my-dashboard-canvas.lovable.app/

📧 Email: umerabdullah048@gmail.com

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Task Statement:-

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Code Preview:-

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Data Visualization PLots Preview:-

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🙌 Acknowledgment

A big thanks to Twitter US Airline Sentiment DataSet Providers and my company for assigning me this exciting project. This project has enhanced my data analytics, visualization, and reporting skills significantly!

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✨ This project is the Last part of my Data Analyst journey at BrainWave Matrix Solutions. Stay tuned for more exciting projects! 🚀

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