Around 4.7 billion people use social media daily; out of that, 206 million use Twitter. People spend a lot of time using social media to consume and create content. And when they comment and post reviews, it can make or break a brand.
Everyone takes time to read reviews about a business before purchasing its product or service, which strongly influences their decision. This shows how much individuals value the words of others—even though they don’t know them.
This guide will go over Twitter API sentiment analysis and how to do it using Python. Not knowing how to code is perfectly fine for this. Let’s begin.
What is sentiment analysis?
Sentiment Analysis online API, or opinion mining, is a process that determines the emotional tone of a piece of writing. It ‘computationally’ determines if it is positive, negative, or neutral. This is done on Twitter to find out the attitude or opinion of the speaker.
For example, “I loved this post about marketing” is a positive statement, while “That video was terrible” is a negative.
Where do we use sentiment analysis?
Sentiment analysis helps in many ways, like monitoring a brand and its social media, customer feedback, or customer support analysis.
Sentimental analysis is of great help in these three fields:
- Business
- Politics
- Public Actions
Steps for Twitter Sentiment Analysis in Python
You will be taught to use Tweepy to access the Twitter API in this post. You’ll need your Twitter developer account and sample codes to analyze this. Once you apply for it online, it usually takes about two days for Twitter to review your application.
Step 1: Installation Process
You will have to install these three things.
– Tweepy is Python’s library that can access Twitter API. You can install it using the command:
pip install tweepy
– TextBlob is a Python library that processes textual data. You can install it using the command:
pip install textblob
– NLTK corpora has large, structured texts. You can install it using the command:
python -m textblob.download_corpora
After installing the above, it’s time to import the libraries you will use for the sentiment analysis project.
Step 2: Authentication For Twitter API
To authenticate your Twitter API, you will need an Access Token (API Key) and an Access Token Secret (API secret) associated with your account. You need to have the app as it provides the base context to use the Twitter API.
Once you have finished the authentication, you can start using tweepy and textblob. Tweepy will help you get the text, while textblob calculates the text’s neutral, positive, and negative parameters.
Step 3: Getting Tweets Using A Hashtag Or Keyword
In this step, you can type a hashtag or keyword and the number of tweets you need to analyze. These are equally important and don’t work without each other. Ensure you don’t miss out on adding the number of tweets parameter, as it sets a limit.
Let’s say you look up “2000” tweets about “travel to Europe.” Once you get this data, you must look through it for sentiments. Your 2000 tweets could show-
- 800 tweets with a positive sentiment
- 500 tweets with a negative sentiment
- 700 tweets with a neutral sentiment
This data can be represented in a pie chart for easy understanding.
Step 4: Cleaning Tweets To Analyze Sentiments
When you get data from anywhere, you must clean it before you can go through it. This is because there can be incorrect or duplicate data, which will not help us with our analysis.
So, suppose the tweet list about “travel to Europe” has some duplicate data. You need to discard the duplicates using the drop_duplicates function.
Step 5: Sentiment Analysis
Now that you have finished cleaning the data, it is free of any duplicate and unwanted data. You can use this cleaned data to calculate the positive, negative and neutral parameters.
Then, you will see that the data is split into three groups based on their framework. You can name them (travel_list_negative, travel_list_positive and travel_list_neutral)
Step 6: Create A Worcloud
Using the data that you have 2000 tweets, you can create a worcloud. This will help you analyze the data better and notice the most repeated words.
The Benefits of Using Location APIs in Twitter
Twitter is a powerful tool for engaging with customers and promoting your brand. But did you know that you can also use Twitter to target your customers based on their location? Location-based APIs can help you do just that.
Location APIs allow you to access information about a user’s current location. This information can be used to target tweets to users in specific locations. For example, you could use a location-based API to target tweets to users in your city.
Summing up
Twitter falls under the Top 10 most used social media platforms. Since it is an app where people share their thoughts, it’s text-based. This makes it a must for a way to analyze the tone of what’s written.
Various types of analysis are done to determine the percentage of positive, negative, and neutral tweets. And besides just using this technology on Twitter, there is also Instagram, Facebook, and Amazon sentiment analysis API that benefits other websites.
But there are challenges faced by sentiment analysis online API, which are usually linked to blunders in the training models. It can be confusing to identify the tone when systems don’t understand the context. They are unable to distinguish between irony and sarcasm and understand emojis