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
Round() - Full Python Script of Twitter Sentiment Analysis
- FAQ
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.
Easy2Digital APIs
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 [-1,1], -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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FAQ:
Q1: What is Social Content Sentiment Analysis?
A: Social Content Sentiment Analysis is a brand product that helps businesses analyze the sentiment behind social media content. It uses natural language processing algorithms to determine whether social media posts have a positive, negative, or neutral sentiment.
Q2: How does Social Content Sentiment Analysis work?
A: Social Content Sentiment Analysis works by analyzing the text of social media posts and applying machine learning algorithms to determine the sentiment expressed in the content. It takes into account the words used, context, and other factors to accurately classify the sentiment of each post.
Q3: Why is Social Content Sentiment Analysis important for businesses?
A: Social Content Sentiment Analysis is important for businesses because it allows them to understand how their brand is perceived on social media. By analyzing sentiment, businesses can identify areas for improvement, monitor customer feedback, and make data-driven decisions to enhance their online reputation.
Q4: What are the benefits of using Social Content Sentiment Analysis?
A: Using Social Content Sentiment Analysis provides several benefits for businesses. It allows them to track and measure customer sentiment, identify trending topics or issues, monitor brand reputation, and gain insights into customer preferences and behaviors.
Q5: Can Social Content Sentiment Analysis be customized for specific industries?
A: Yes, Social Content Sentiment Analysis can be customized for specific industries. The algorithms can be trained on industry-specific data to improve the accuracy of sentiment analysis for that particular industry. This customization allows businesses to gain more relevant insights and make informed decisions.
Q6: Is Social Content Sentiment Analysis compatible with different social media platforms?
A: Yes, Social Content Sentiment Analysis is compatible with different social media platforms. It can analyze content from popular platforms like Twitter, Facebook, Instagram, and more. This ensures businesses can monitor sentiment across a wide range of social media channels.
Q7: How accurate is Social Content Sentiment Analysis?
A: The accuracy of Social Content Sentiment Analysis depends on various factors, including the quality and quantity of training data, the complexity of the language used, and the specific algorithms used. With proper customization and training, it can achieve high levels of accuracy in sentiment classification.
Q8: Can Social Content Sentiment Analysis detect sarcasm or irony?
A: Detecting sarcasm or irony in social media posts can be challenging even for advanced sentiment analysis algorithms. While Social Content Sentiment Analysis can capture some sarcastic or ironic expressions, it may not always accurately interpret them. However, continuous improvement and updates to the algorithm can enhance its ability to detect such nuances.
Q9: How can businesses utilize the insights from Social Content Sentiment Analysis?
A: Businesses can utilize the insights from Social Content Sentiment Analysis in various ways. They can identify potential issues or negative sentiment early on and take appropriate actions to address them. They can also identify positive sentiment and leverage it for marketing and brand-building purposes. Additionally, the insights can help businesses understand customer preferences and tailor their products or services accordingly.
Q10: Is Social Content Sentiment Analysis GDPR compliant?
A: Yes, Social Content Sentiment Analysis can be GDPR compliant. It is crucial for businesses to ensure that the data they collect and analyze through sentiment analysis adheres to the regulations set forth by GDPR. By implementing appropriate data protection measures, such as obtaining consent and anonymizing data, businesses can use Social Content Sentiment Analysis in a compliant manner.