Chapter 35: Social Media Sentiment Analysis Using Python, Twitter, and Easy2Digital APIs
Regarding a full scope of intelligent business data dashboard, organic traffic, sales number, and top product GMV from worldwide are of course indispensable. It might have potential risk if you ignore what people are talking about your business, and what the sentiment is when people mention your brand. In fact, there are many valuable channels that could give you hints and signals, such as Reddit, social media (Facebook, Instagram, etc), the microblogging arena of Twitter.
This chapter shows you how to leverage Easy2Digital APIs to read the tone and sentiment of people’s opinions behind some Twitter topics. It’s called social media sentiment analysis. This should be the second half chapter after the Twitter bot. By the end of the chapter, you master why sentiment analysis is valuable to your business and are able to implement the analysis yourself.
Ingredients: Python, Easy2Digital API, Twitter API
Table of Contents: Social Media Sentiment Analysis Robot
- Why Sentiment Analysis Is Vital for Business and Marketing?
- Easy2Digital APIs
- Extract the Polarity and Subjectivity of Specific Queries
- Split into positive, neutral, and negative by score
- Define a percentage function and Use
- Full Python Script of Twitter Sentiment Analysis
Why Sentiment Analysis Is Vital for the Business?
As a marketer or businessman, you might be curious about what people’s opinions are about the most popular topics, products, and events. Perhaps as an analyst, you wish to study the effect of your company’s recent brand marketing campaign. And most importantly, planning a content marketing campaign to engage with the customer’s needs. It’s because the right tone matching the sentiment and perception of customers’ minds is critical to success.
Sentiment analysis is extremely useful to help your business. It allows us to gain an overview of the wider public opinion behind certain topics, products, and events and give you an insight into content, and ongoing campaign effect.
Meanwhile, you are able to find answers to the most important issue of a business from sentiment analysis. You can based on the customer feedback, and tone of voice adjust the strategy of a business. Meanwhile, you can observe and monitor your competitor’s branding and word of the month as well. And absorbing what they did well, and leveraging what they did worse are super helpful.
In today’s environment, it’s totally feasible to collect the data and find the answers regarding the curiosity mentioned above. However, lunch might not be for free, or even saying that people are now suffering from data overload. Businesses might have mountains of customer opinions collected. Yet for mere humans, it’s still impossible to analyze it manually without any sort of error or bias.
Luckily with Python, collecting and visualizing the sentiment analysis data in one hub is easy and automatic. And Twitter would be one of the most perfect social media channels to pump you the noise and voice data.
Basically, API returns two main sentiment data of a sentence. The sentence can be from the blog, Q&A, social post, etc. They are polarity and subjectivity. From the quantitative perspective, these two essential methods are core and critical for you to analyze the generated dataset.
Polarity lies between
-1 defines a negative sentiment and 1 defines a positive sentiment. Negative words reverse the polarity. It has semantic labels that help with fine-grained analysis. For example — emoticons, exclamation marks, emojis, etc.
Subjectivity lies between
[0,1]. It quantifies the amount of personal opinion and factual information contained in the text. The higher subjectivity number means that the text contains more personal opinions.
Extract the Polarity and Subjectivity of Specific Queries
Like building a Twitter bot for scraping specific topic content, sentiment analysis also needs to scrape the content first in Twitter. And the further step is to read through the sentiment information from the text rather than just finding the most popular ones.
It’s very straightforward by just using
API. And then you can create the other two variables to get all posts’ polarity numbers and subjectivities numbers. Here are the codes:
Split into positive, neutral, and negative by score
As mentioned, there is a range of number indexes to point out if a post is positive or negative from Polarity. In the previous paragraph, we fetched the total Polarity number. So here we need to split out the positive, negative, and neutral ones (the number is equal to zero) by using if conditions in the script
Define a percentage function and Use the Round() method
Both positive and negative numbers can’t present a percentage out of all scraped posts. So you are not able to instantly find out the sentiment and the tone. Thus, you can create a def function that uses the positive number divided by the total posts or the negative number divided by the total posts.
What’s more, those number floats might have too much and make it hard to read if you use the float method in the def percentage function. So here you can plug and use the round method to shorten the number floats, such as 2, or 3.
Full Python Script of Social Media sentiment analysis using Easy2Digital API and Twitter
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