How To Build Text Sentiment Analysis App With a Simple API?

Today, I am building a Text Sentiment Analysis application that will let the user know how their text sounds. Is it positive, negative, or neutral? So without any further ado, let’s jump in.

Stack

We need to choose a stack first to build this application. I have decided to go with the Jamstack.

I am going to use Next.js for the client-side and TailwindCSS for the styling.

If you don’t know about Next.js, it is a JavaScript framework built on top of React and provides features like server-side rendering, static site generation, etc. Tailwind is a CSS framework that provides utility classes to speed up the development process.

Choosing The API

Let’s find an API that we can use to get the sentiment analysis. Go to RapidAPI Hub and create an account if you haven’t already and then search for “text” in the search section.

You will see different search results related to text APIs. For this piece, I am using Text Analysis API.

It is a free API, so you do not need to subscribe to it. However, you will need your RapidAPI key. Go ahead and save the x-rapidapi-key so you can use it later.

Building The UI

You can create a Next.js boilerplate with TailwindCSS integrated by running the following command in your terminal. So let’s do that.

sh
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npx create-next-app -e with-tailwindcss text-sentiment-analysis-app

This command is going to take a minute to set everything up. After generating the boilerplate, you will see a folder with the name text-sentiment-analysis-app has been created. Open this folder in your preferred code editor. I will use VSCode for this project.

Project Files

When you open the project in your code editor, you will see the following directories and files in the root directory:

• pages directory: Inside it, you will have files index.js, _app.js, and another directory called api. You only need to know about the index.js file that is the main entry point in your project.
• public directory: This directory contains icons. You place your static files here to load later in the application.
• node_modules: It’s another directory that contains all the node modules you are using in your application.
• package.json: This file contains the metadata of your project.
• package-lock.json: This file is responsible for tracking the exact version of every installed package.
• postcss.config.js: This file contains PostCSS configurations.
• tailwind.config.js: It contains TailwindCSS configurations.
• readme.md: It’s a markdown file for documentation. readme.md: It’s a markdown file for documentation.

Before we move on to writing the code, open this file, and copy all of its content, then paste it inside the tailwind.config.js file in your project. These are some TailwindCSS configurations I have done specifically for this project. I have added some colors that you do not have by default with TailwindCSS and set some screen sizes.

Now let’s start writing the code. I am going to do it in steps so you can follow along.

→ STEP #1

Open the pages/index.js file and remove all the existing code. After this, copy-paste the following code there:

js
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export default function Home() {
	return (
		<div className="flex flex-col items-center relative min-h-screen">
			<h2 className="font-raleway font-bold text-6xl text-primary pt-20 pb-6 md:text-3xl">
				Text <span className="text-secondary">Sentiment</span> Analysis
				App
			</h2>
			<h3 className="text-danger text-2xl font-raleway font-bold uppercase tracking-wide mb-12 md:text-base md:px-4 md:text-center">
				Check how your text sounds
			</h3>
		</div>
	);
}

It will create two headings for you with the text “Text Sentiment Analysis App” and “Check how your text sounds”. You can change it to anything you prefer.

→ STEP #2

Now let’s create some text areas where users will be able to write the content and a button that will request the API to analyze our content.

For this, copy the following code and paste it in pages/index.js:

js
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export default function Home() {
	return (
		<div className="flex flex-col items-center relative min-h-screen">
			<h2 className="font-raleway font-bold text-6xl text-primary pt-20 pb-6 md:text-3xl">
				Text <span className="text-secondary">Sentiment</span> Analysis
				App
			</h2>
			<h3 className="text-danger text-2xl font-raleway font-bold uppercase tracking-wide mb-12 md:text-base md:px-4 md:text-center">
				Check how your text sounds
			</h3>
			<div className="flex justify-between w-5/6 h-96 mt-8 md:flex-col md:items-center md:justify-start">
				<textarea
					type="text"
					className="border border-primary outline-none w-2/5 px-4 py-2 rounded-sm font-raleway md:w-full"
					placeholder="Write/paste any content..."
				/>
				<div className="flex items-center">
					<button className="h-1/6 outline-none border border-secondary font-bold font-raleway mx-12 px-12 rounded-sm bg-secondary text-primary transition duration-300 hover:bg-bc hover:text-black md:h-16 md:my-12">
						Analyse
					</button>
				</div>
				<textarea
					type="text"
					className="border border-primary outline-none w-2/5  px-4 py-2 rounded-sm font-raleway md:w-full"
					placeholder="Analysis..."
					value=""
				/>
			</div>
		</div>
	);
}

This code is going to create an input field and button. I have also styled them a little bit using TailwindCSS.

→ STEP #3

Let’s create some states to store the content and analysis that we will receive from the API. For this, copy-paste the following code in pages/index.js.

js
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import {useState} from 'react';
export default function Home() {
	const [content, setContent] = useState('');
	const [analysis, setAnalysis] = useState('');
	return (
		<div className="flex flex-col items-center relative min-h-screen">
			<h2 className="font-raleway font-bold text-6xl text-primary pt-20 pb-6 md:text-3xl">
				Text <span className="text-secondary">Sentiment</span> Analysis
				App
			</h2>
			<h3 className="text-danger text-2xl font-raleway font-bold uppercase tracking-wide mb-12 md:text-base md:px-4 md:text-center">
				Check how your text sounds
			</h3>
			<div className="flex justify-between w-5/6 h-96 mt-8 md:flex-col md:items-center md:justify-start">
				<textarea
					type="text"
					className="border border-primary outline-none w-2/5 px-4 py-2 rounded-sm font-raleway md:w-full"
					placeholder="Write/paste any content..."
					onChange={e => setContent(e.target.value)}
				/>
				<div className="flex items-center">
					<button className="h-1/6 outline-none border border-secondary font-bold font-raleway mx-12 px-12 rounded-sm bg-secondary text-primary transition duration-300 hover:bg-bc hover:text-black md:h-16 md:my-12">
						Analyse
					</button>
				</div>
				<textarea
					type="text"
					className="border border-primary outline-none w-2/5  px-4 py-2 rounded-sm font-raleway md:w-full"
					placeholder="Analysis..."
				/>
			</div>
		</div>
	);
}

You can see that I have added an onChange event handler to set the state value as soon as the user writes something in the text area field.

→ STEP #4

Let’s integrate the API now. For this, first, create a .env.local file and paste the following in it:

sh
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NEXT_PUBLIC_RAPIDAPI_KEY=YOUR_RAPID_API_KEY

You need to replace YOUR-RAPIDAPI-KEY here with the API key that I told you to save earlier. It is the value of x-rapidapi-key that you saved earlier.

Now download and add axios to your project. For this, run the following command in the terminal:

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npm install axios

Now import axios at the top of the pages/index.js.

js
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import axios from 'axios';

→ STEP #5

We are using the summarize-text endpoint of the Text Analysis API to analyze the text.

Create a file called superhero.js in the pages/api directory and copy-paste the following code there:

RapidAPI Hub provides you with code snippets in different languages for integrating the API. I am going to use the (JavaScript) Axios one.

Fetching data using (JavaScript) Axios

Now create a file with the name analyse.js inside the pages/api directory. It is going to create a REST API endpoint for you. The endpoint point will look like this:

sh
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http://localhost:3000/api/analyse

Now copy the following code in this file:

js
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import axios from 'axios';
export default async function handler(req, res) {
	if (req.method === 'POST') {
		const options = {
			method: 'POST',
			url: 'https://text-analysis12.p.rapidapi.com/sentiment-analysis/api/v1.1',
			headers: {
				'content-type': 'application/json',
				'x-rapidapi-host': 'text-analysis12.p.rapidapi.com',
				'x-rapidapi-key': process.env.NEXT_PUBLIC_RAPIDAPI_KEY
			},
			data: {language: 'english', text: req.body.content}
		};
		axios
			.request(options)
			.then(function (response) {
				res.status(200).json(response.data);
			})
			.catch(function (error) {
				console.error(error);
			});
	} else {
		res.status(400);
	}
}

This code is making an API call to the Text Analysis API on the server and returns the results to the user. It is going to execute when the user makes an API call to the analyse endpoint I have mentioned above.

Once you are done, copy the following code in the pages/index.js file:

js
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import {useState} from 'react';
import axios from 'axios';
export default function Home() {
	const [content, setContent] = useState('');
	const [analysis, setAnalysis] = useState('');
	/**
	 *
	 *
	 * Fetch Analysis of the content
	 */
	const fetchAnalysis = async () => {
		try {
			setAnalysis(`Analysing content...`);
			const res = await axios.post(`/api/analyse`, {
				content
			});
			const {data} = res;
			const msg = `Your text sounds ${
				data.sentiment
			}. It has ${Math.floor(
				data.aggregate_sentiment.pos * 100
			)}% positivity, and ${Math.floor(
				data.aggregate_sentiment.neg * 100
			)}% negativity. It has a neutral level of ${Math.floor(
				data.aggregate_sentiment.neu * 100
			)}%.`;
			setAnalysis(msg);
		} catch (err) {
			setAnalysis(`Couldn't analyse the content.`);
			console.log(err);
		}
	};
	return (
		<div className="flex flex-col items-center relative min-h-screen">
			<h2 className="font-raleway font-bold text-6xl text-primary pt-20 pb-6 md:text-3xl">
				Text <span className="text-secondary">Sentiment</span> Analysis
				App
			</h2>
			<h3 className="text-danger text-2xl font-raleway font-bold uppercase tracking-wide mb-12 md:text-base md:px-4 md:text-center">
				Check how your text sounds
			</h3>
			<div className="flex justify-between w-5/6 h-96 mt-8 md:flex-col md:items-center md:justify-start">
				<textarea
					type="text"
					className="border border-primary outline-none w-2/5 px-4 py-2 rounded-sm font-raleway md:w-full"
					placeholder="Write/paste any content..."
					onChange={e => setContent(e.target.value)}
				/>
				<div className="flex items-center">
					<button
						className="h-1/6 outline-none border border-secondary font-bold font-raleway mx-12 px-12 rounded-sm bg-secondary text-primary transition duration-300 hover:bg-bc hover:text-black md:h-16 md:my-12"
						onClick={fetchAnalysis}
					>
						Analyse
					</button>
				</div>
				<textarea
					type="text"
					className="border border-primary outline-none w-2/5  px-4 py-2 rounded-sm font-raleway md:w-full"
					placeholder="Analysis..."
					value={analysis}
					readOnly
				/>
			</div>
			<div className="absolute bottom-0 flex justify-center items-end h-52 md:h-44">
				<p className="text-primary pb-12 md:w-60 md:text-center">
					Made by RapidAPI DevRel Team –{' '}
					<a href="https://github.com/RapidAPI/DevRel-Examples-External">
						See Examples Like this
					</a>
				</p>
			</div>
		</div>
	);
}

You can see that I have created a function, fetchAnalysis, to request the API. The API response I receive is between 0 and 1. So I am multiplying the response by 100 to get the percentage, and then I am showing the results to the user.

Wrap Up

That’s it. We have successfully built a Text Sentiment Analysis App using Text Analysis API. You can find the source code of this web app here.

In the end, it will look something like this:

Text Sentimental Analysis App built with Next.js and Text Sentiment Analysis API