Developing Sentiment Analysis Application with spaCy and Flask

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spaCy, Flask, unit test, static code analysis, Javascript/HTML/CSS

Overview

In this project, we make use of the spaCy, to create an application that would perform sentiment analysis on a provided text. We then deploy this application over the web using Flask framework.

Main Steps

Figure 1 Main Steps

The main steps for completing this project are shown in Figure 1. And the running code can be found on GitHub.

• Step 1 & 2: Create a sentiment analysis application & Package the application

Create a folder SentimentAnalysis (aka Package). Create two files __init__.py and sentiment_analysis.py (aka module) in the folder, define sentiment_analysis_func in sentiment_analysis.py file.

• Step 3 & 4: Run unit tests on the application & Run static code analysis

Create a file test_sentiment_analysis.py.

Check the quality of the code as per the PEP8 guidelines by running static code analysis using PyLint library.

pylint  server.py

• Step 5: Deploy as web application using Flask

Create *.html file in templates folder and *.js file in static folder. The purpose is to create the front-end of the application.

Create server.py file.