/Proj_03--Twitter-Dataset

Primary LanguageJupyter Notebook

Twitter Sentiment Analysis

Overview

The "Twitter Sentiment Analysis" project aims to analyze and determine the sentiment (positive, negative) expressed in tweets using the Sentiment140 dataset. This project involves data exploration, cleaning, analysis, and building a sentiment prediction model.

Project Steps

1. Data Exploration

  • Understanding the Dataset: Reviewed the Sentiment140 dataset to understand the structure and columns.
  • Key Columns:
    • target: Sentiment label (0 = negative, 4 = positive)
    • date: The date when the tweet was posted
    • text: The tweet content

2. Data Cleaning

  • Handling Missing and Duplicate Values: Ensured data quality by addressing any missing or duplicate entries.
  • Dropping Irrelevant Columns: Removed unnecessary columns such as flag and user.
  • Anomaly Detection: Addressed anomalies in text, date, and sentiment data.

3. Exploratory Data Analysis (EDA)

  • Summary Statistics: Generated summary statistics to understand the distribution of data.
  • Data Visualization: Created visualizations to analyze tweet patterns, sentiment distribution, and temporal trends.

4. Sentiment Distribution Analysis

  • Visualizing Sentiment Classes: Analyzed the balance of sentiment classes using plots such as violin plots and pie charts.

5. Temporal Analysis

  • Trend Analysis: Explored how sentiment varies over time by analyzing monthly, weekly, and daily trends.

6. Text Preprocessing

  • Data Cleaning: Removed stop words, special characters, and URLs.
  • Tokenization and Lemmatization: Tokenized and lemmatized the tweets for better text analysis.

7. Word Frequency Analysis

  • Frequency Analysis: Analyzed word frequency by sentiment class.
  • Visualization: Represented word frequencies using bar charts and word clouds.

8. Sentiment Prediction Model

  • Model Building: Developed and evaluated models to predict tweet sentiment.

9. Insights and Recommendations

  • Key Insights: Summarized insights gained from the analysis.
  • Actionable Recommendations: Provided recommendations based on sentiment trends observed in the dataset.

10. Presentation

  • Documentation: Documented the entire process and results.

  • The project provided practical experience with data exploration, cleaning, and analysis techniques.

  • It demonstrated the importance of text preprocessing in sentiment analysis.

  • The sentiment prediction model can be used to automate the classification of new tweets.

Learning Benefits

  • Ease of Learning: Simplified complex data tasks into manageable steps, making the project accessible for learning and practice.
  • Practical Practice: Enhanced understanding of text preprocessing, sentiment analysis, and data visualization techniques in a real-world context.

Tools Used

  • Python (for data exploration, cleaning, analysis, and modeling)
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn, NLTK, Scikit-learn
  • Jupyter Notebook (for executing and documenting the project)