Natural Language Processing for Stocks News Analysis

Offered By
Coursera Project Network
In this Guided Project, you will:

Create a pipeline to remove stop-words, perform tokenization and padding

Understand the theory and intuition behind Recurrent Neural Networks and LSTM

Train the deep learning model and assess its performance

Clock2 hours
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this hands-on project, we will train a Long Short Term Memory (LSTM) deep learning model to perform stocks sentiment analysis. Natural language processing (NLP) works by converting words (text) into numbers, these numbers are then used to train an AI/ML model to make predictions. In this project, we will build a machine learning model to analyze thousands of Twitter tweets to predict people’s sentiment towards a particular company or stock. The algorithm could be used automatically understand the sentiment from public tweets, which could be used as a factor while making buy/sell decision of securities. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Python ProgrammingMachine LearningDeep Learningcoding

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Task #1: Understand the Problem Statement and business case 

  2. Task #2: Import libraries and datasets and Perform Exploratory Data Analysis

  3. Task #3: Perform Data Cleaning (Remove Punctuations)

  4. Task #4: Perform Data Cleaning (Remove Stopwords)

  5. Task #5: Plot WordCloud

  6. Task #6: Visualize Cleaned Datasets

  7. Task #7: Prepare the data by tokenizing and padding

  8. Task #8: Understand the theory and intuition behind LSTM

  9. Task #9: Build and train the model

  10. Task #10: Assess trained model performance

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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