Implementing Neural Networks for Text and Web Data Analysis

Objective:

In this assignment, you will implement a neural network model using Keras (with TensorFlow backend) to solve a text classification problem or web-related application (such as sentiment analysis or web scraping). You will gain hands-on experience with Deep Learning concepts and how they can be applied to text and web data, aligning with the objectives of Module 5.

Learning Outcomes:

• Understand the basics of Artificial Neural Networks (ANNs) and deep learning.
• Apply deep learning models to text analytics and web data.
• Evaluate model performance in a text classification task (e.g., sentiment analysis).
• Understand the integration of Deep Learning techniques for solving real-world problems.

Instructions:

Access Google Colab:

• Open Google Colab and access the notebook directly from the .
• Open the notebook for Chapter 8: Artificial Neural Networks and follow the code provided in the Colab interface.
• Execute the code as provided and make necessary adjustments for the task you are working on.

Choose a Text or Web Data Problem:

• You can choose one of the following options:
• Sentiment Analysis of Movie Reviews: Use a dataset like the IMDb reviews dataset to classify text as positive or negative sentiment.
• Web Scraping and Classification: Collect web data (e.g., news articles or tweets) using BeautifulSoup or Tweepy and classify the content based on categories (e.g., topic classification).

Follow the Steps in the Notebook:

• Step 1: Preprocess the text/web data (e.g., tokenization, stopword removal, and vectorization).
• Step 2: Create and train a neural network using Keras.
• Step 3: Evaluate the model using performance metrics like accuracy, precision, recall, and F1-score.

Ensure that you execute all the code provided in the notebook and take notes on any observations, challenges, or adjustments you make during the process.

Write a Report:

After running the code and analyzing the results, write a 1-2 page report with the following structure:

Introduction (100 words):

• Briefly describe the dataset and classification task. Mention whether you chose text analysis or web data for the project.

Process (300 words):

• Explain the steps you followed in the notebook. Discuss data preprocessing, model architecture, training, and evaluation metrics used for performance assessment.

Findings (200 words):

• Discuss the performance of the neural network model. Did it work well on the task? What did the evaluation metrics indicate about the models accuracy and reliability?

Conclusion (100-150 words):

• Reflect on what you learned from applying deep learning models to text/web data. What insights did you gain about the use of Deep Learning in real-world applications like sentiment analysis or web scraping?

Deliverables:

1. Report (2-3 pages, 1000-1200 words)- ensure you have a cover page, no specific format is required, you are free to use APA or any professional format.