Top Python Projects for Beginners Using AWS NLP ToolsTurn Simple Code into Smart Applications with AWS Comprehend

Pushkar Nandgaonkar
May 7
3 min read

Are you looking to go beyond basic Python programming and build real-world machine learning applications? If so, integrating AWS NLP tools into your Python projects is a fantastic starting point. Whether you want to analyze customer reviews, classify news articles, or understand the tone of social media posts, Amazon Comprehend helps you build smart applications quickly and easily.

In this blog, we’ll explore some beginner-friendly project ideas using AWS Comprehend that are perfect for students who want to elevate their skills without getting overwhelmed.

What is AWS Comprehend?

AWS Comprehend is a Natural Language Processing (NLP) service provided by Amazon Web Services. It uses machine learning to extract insights from text without requiring you to build your own models. It can detect sentiment, identify key phrases, recognize named entities, and determine the language of the input text.

Why is this great for students?

No need to build or train complex models
Integrates easily with Python using the boto3 SDK
Perfect for real-world text analytics projects

Project Idea 1: Analyzing Customer Review Sentiment

The Problem:

In today’s digital world, customer feedback often comes in huge volumes. But how do businesses know whether reviews are positive, negative, or neutral?

The Solution:

Use AWS Comprehend’s Sentiment Analysis to classify review sentiments. With a few lines of code, you can process large datasets and understand customer opinions.

Skills You’ll Gain:

Connecting AWS Comprehend to Python
Preprocessing review text
Interpreting and visualizing sentiment results

Project Idea 2: Language Detection for Multilingual Data

The Problem:

Suppose you’re handling reviews or content from users across the globe. You need to first detect the language before applying further processing.

The Solution:

Use the DetectDominantLanguage API in AWS Comprehend. It returns the most probable language along with a confidence score.

Skills You’ll Gain:

Managing multilingual datasets
Using Python dictionaries and loops for language categorization
Structuring results for dashboards or visualizations

Project Idea 3: Named Entity Recognition in News

Headlines

The Problem:

You have hundreds of news headlines and want to extract useful information like names, organizations, dates, or locations.

The Solution:

Leverage Named Entity Recognition (NER) with AWS Comprehend. It tags entities in the text and groups them into categories like PERSON, LOCATION, DATE, etc.

Skills You’ll Gain:

Extracting and cleaning entities
Sorting entities by type
Building visualizations or summaries using extracted entities

Project Idea 4: Key Phrase Extraction from Academic Articles

The Problem:

When reading academic papers or long essays, it’s easy to lose track of the main ideas. Manually highlighting key phrases is time-consuming.

The Solution:

With Key Phrase Extraction, AWS Comprehend automatically identifies the most important phrases from a given text. You can use this feature to generate summaries, build flashcards, or create keyword-based indexes.

Skills You’ll Gain:

Summarizing academic text using NLP
Filtering out noise from raw data
Enhancing searchability of study materials

Why These Projects Are Beginner-Friendly

These AWS Comprehend projects:

Require minimal setup
Don’t demand prior machine learning experience
Let you work with real-world data
Are scalable—start simple and add more complexity later

They also help you practice key Python skills like working with APIs, handling JSON responses, and automating data workflows.

Need Help Building or Customizing Your Project?

It’s normal to feel stuck or overwhelmed when working with new tools and APIs. If you need personalized support for your academic project, guidance on AWS integration, or coding help, CodersArts is here to help. Our team specializes in one-on-one student support to make sure your project is successful and submission-ready.

🎥 You can also check out the project demo in the following