Sentiment Analysis on Kaggle Text Dataset

• This repository showcases a comprehensive sentiment analysis project conducted on a text dataset sourced from Kaggle. The primary goal was to categorize each piece of text into positive, negative, or neutral sentiments. The project covers essential aspects such as data collection, cleaning, preprocessing, feature engineering, model selection, and evaluation. The provided Jupyter notebook outlines the entire methodology, key findings, and insights obtained from the sentiment analysis.

Key Steps and Features:

• Data Collection and Cleaning:

  • Identification of the problem.
  • Data selection based on the identified problem.
  • Cleaning steps: removal of duplicates, handling null values, and text-specific preprocessing like removing punctuation and numbers.

• Data Visualization:

  • Utilization of graphs to gain insights into data behavior and patterns.
  • Visualization of word frequency, sentiment label distribution, and creation of sentiment-specific word clouds.
  • TF-IDF (Term Frequency-Inverse Document Frequency):

• Explanation of TF, IDF, and TF-IDF.

  • Demonstration of TF-IDF's application in text analysis tasks for feature engineering.

• Model Selection: -Overview of traditional machine learning models (Logistic Regression, SVM) and deep learning models (RNNs, Transformers like BERT).

  • Consideration factors for selecting the appropriate model based on the nature of the problem, data size, complexity, computation time, and scalability.

Challenges and Solutions:

• Discussion on challenges such as imbalanced data, sarcasm, contextual understanding, domain specificity, and data noise.

Solutions proposed for addressing each challenge, ensuring robust sentiment analysis results.