How To Use Sentiment Analysis for Social Media

Sentiment analysis for social media is a powerful method of tracking brand sentiment, customer satisfaction, and numerous other valuable metrics for your company as customers express them on social media. Utilizing the right tools, it is possible to create a consistent and real-time view of what customers believe about your brand. This can then inform business decisions you make.

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It is critical to create a social media sentiment analysis based on as much data as you can. Sentiment analysis creates a way for you to get an accurate view of what customers are saying about your company, which could directly impact your brand reputation. Consider what sentiment analysis could do for your company and why it may be easier than you realize to put in place tools to help you consistently monitor social media statements about your business.

What Is Sentiment Analysis in Social Media?

Sentiment analysis is a method any business can use to monitor what people are saying about their brand, products, or services. You can also use it to monitor industry trends or factors that impact your business. With sentiment analysis, social media becomes a tool for improving your business and gauging how well you are doing.

Advantages of Using Sentiment Analysis for Social Media Monitoring

It’s easy enough to create an online brand monitoring tool that will provide you with all mentions of your business on social media. However, “mentions” is not enough. With sentiment analysis, we can learn what people actually think and pull information about the emotions they express in the posts and comments they leave on social media. 

When you apply this technology to achieve these goals, your business could see numerous advantages (including those that help you beat out your customers). Consider some of the advantages of using sentiment analysis specifically for monitoring social media.

• Improve your customer service: Studies show that almost a third of all social media users use the platform to complain about the bad service they receive from a company. As a business, this customer sentiment analysis enables you to find these negative comments quickly after being posted and handle them. Additionally, you can learn from those comments to make improvements to your business model, customer service priorities, and best practices. 
• Improve your product. With sentiment analysis, you can monitor what people “want” or “wish” your product would offer them. For example, you can use sentiment analysis of social media posts to pick up on changes in your industry or new features customers want from your business. That allows you to improve your business and stay ahead of the competition.
• Complete a competitor analysis: Utilize sentiment analysis social media tools to learn how well your company is doing. Anything you are tracking about your business, you can track about the competition. That directly creates a way for you to see challenges and opportunities that could impact your business model. You learn how they are marketing, what they are offering, and how well their customers receive their product. 
• Ensure accuracy in your marketing: With sentiment analysis, it’s possible to better understand your customers, and with that comes the ability to determine how well your marketing is working or if it might not be producing the ROI you desire. For example, you can monitor mentions and sentiment about your business after a new campaign roles out, allowing you to make tweaks as necessary.
• Monitor your brand health: With social media sentiment analysis, you have the ability to monitor your brand over time, looking for areas of opportunity as you go. One of the best reasons to use this method is because it allows you to create a live, real-time method of monitoring your brand – meaning you can react when you need to if there is negative posts. But you can also use it to monitor your brand’s health over time.

Ultimately, sentiment analysis for social media posts allows you to know how your customers feel about you. This is crucial. Remember, you did the work to market and bring that customer to you. You want them to come back to you. But, when they post negative social media posts, not only are they less likely to be a return customer, but they are also influencing the decisions of other prospective customers. That’s a costly mistake you simply cannot make.

How to Do Social Media Sentiment Analysis

Now that you know the value and benefits of social media analysis of sentiment, consider exactly how you can do this. As an automated process, it is easy to put in place real-time monitoring of sentiment, so you’re not doing the work yourself.

There are several ways to do this. One option is to use sentiment analysis social media tools. There are some tools listed here that are a good starting point. However, tools have limits. First, they may not give you the flexibility you need to capture very specific data and sentiments about your customers. That makes them limitedly beneficial. Second, some sentiment analysis social media tools are costly, and they may not be worth investing in if you’re not getting the accurate, detailed information you need.

If you are just getting started and want a simple tool, the following options may work for you:

• Hootsuite: As a free tool, Hootsuite can be an attractive option. If you want to know what’s trending about a topic, this tool works well.
• Google Alerts: Perhaps one of the simplest tools to use is Google Alerts. While white Google Alerts can provide you with some insight, they have limitations to accuracy, and data quality issues are common with this tool.
• MonkeyLearn: MonkeyLearn is a full suite of tools that offers text analysis. It can provide you with good accuracy. The problem with this tool is that you really do have to have a tech team working on it on a consistent basis. With so much customization work to do, it is harder for smaller and mid-size companies to get the most out of this tool.

Sentiment analysis social media tools can be very helpful and useful. If you are looking for basic information, they are the route to take. However, there are limitations and many of them could be overcome if you build your own tools.

How to Build a Custom Tool for Sentiment Analysis on Social Media

As noted, sentiment analysis for social media is a very important methodology for capturing customers’ emotions and feelings about your products. However, to do this, you need accurate and in-depth information. For that reason, consider the value of using web scraping for data analysis.

When we talk about web scraping, we’re referring to an automated tool that will visit target websites, capture specific data for your analysis, and then bring that data back to you for analysis. As a custom solution, this enables you to control the type and comprehensive detail of all of the content you want to be analyzed. If needed, you could even implement real time sentiment analysis, but it really depends on your specific needs.

Before starting the process using the following steps, we recommend using proxies for web scraping. A proxy is an intermediary service that eliminates the target website from being able to trace back to your IP address. It works as a type of block, limiting any access to your identity. As a result, the target website isn’t likely to shut down your efforts, and it definitely does not have the ability to learn where you are coming from. Instead of seeing your IP address, it sees that of a web server you’ve set up through a proxy service. Rayobyte offers proxy services to help you with this process.

With that information in place, it is time to start working on the process of creating a sentiment analysis for social media tools using web scraping. Follow these steps to do so.

Data Collection

To pull data from social media sites, you will need to use an API designed for that site. Here are a few that you can use:

• Facebook Graph API
• Twitter API – now called X API

You may find, however, that these APIs are limiting – and we should also mention that not all social media platforms offer an API at all. To use web scraping for data collection, you can start with learning how to scrape the web using BeautifulSoup. BeautifulSoup parsing allows you to capture the specific information you need no matter what you are after.

You can use other web scraping tools as well. Another robust option is to use Selenium for web scraping. Ultimately, these tools allow you to create a tool that will:

• Visit the target websites you want them to
• Parse the HTML on those websites to find information about your target topic, such as your brand
• Pull that information back – through the proxy – configure it, and save it in a way that works for you.

This is what you can do to learn about your mentions or other specific information. However, it only provides you with just that – mentions. To understand the sentiment behind this information, you would need to read each and every one of these methods to make those decisions. Even then, it would be challenging to understand the trends and concerns in your brand mentions truly.

For this reason, we want to employ AI for web scraping and then for sentiment analysis.

Preprocess the Text

With this raw data, you’ll need to clean it up some before you actually can put it through a sentiment analysis tool. There are several reasons for this. First, we want to make sure no sensitive information is being exposed. That could include real names, contact information, or other data about a person, including customers. We also want to ensure any proprietary information is also protected. This is done through the process of tokenization. The sensitive information is removed and replaced with a token – none of the actual sentiment changes. 

Next, we need to improve the efficiency of the data processing. That includes two steps. The first is to work through your collected content and remove some of the non-essential information. Specifically, we want to target stopword removal. This gets rid of extra words like “a” and “the” in the scraped day. The second strategy for improving speed and efficiency is lemmatization. This allows the sentiment analysis tools to analyze the simplest versions of a word.

There are several tools you can use to achieve this preprocessing. These tools use natural language processing (NLP) to make decisions about the content you collect. The most effective strategies include:

• NLTK: A straightforward tool, it makes your content easier to process.
• SpaCy: This tool is also easy to use and provides robust features to help you with the process.

Sentiment analysis of social media is the next step, but before we get to that point, keep the following in mind. This process can seem like too many steps and too much work. However, it’s certainly not. Once you create an AI for web scraping, you can use it to handle much of this work. The first step takes a few extra minutes but will be streamlined later.

Sentiment Classification

Your text is ready to have sentiment analysis applied to it. Sentiment classification is a process in which your tool analyzes the content and then classifies it as either positive, negative, or neutral. This is when you can start weeding out the content that is not helpful so you can work on the mentions that are valuable. 

There are various strategies to allow you to do this.

Lexicon-based methods for sentiment classification. The most common option is to use VADER for sentiment analysis. It is a rule-based sentiment analysis tool that is very good for social media analysis. That is because it does a great job with short texts. It does not take a lot of work to learn how to use it either. 

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Machine learning is another route to consider. There are several tools that could help you to achieve these goals, including Naïve Bayes and SVM, which are two of the most common. Both are straightforward tools to use that will provide you with very specific analysis. This is better for longer-form content, but social media content is still a reliable component of this process.

A third route is a more advanced option, best for more complex and bigger projects. For advanced deep learning, use BERT models. These provide the most robust application of NLP and can often detect emotions very accurately. Ultimately, this method costs more but can prove worthwhile.

Aspect-Based Sentiment Analysis (ABSA) is another strategy to consider using. This tool allows you to capture and analyze more in-depth content, right down to the specific features, product names, or services related to your company. This is often ideal for marketing sentiment analysis or when you are trying to single out a specific concern or expression.

Visualize It

The final step is to take your social media sentiment analysis and bring it to life. This is done through the use of visualization software. In short, the software works to capture valuable information from the data you’ve just analyzed and brings it to life. By using a data visualization tool, you can see some of the best overall reflection of your content. Visualization breaks it down into charts and interactive apps that can put this information to good use. 

There are various types of visualization tools available today. For sentiment analysis for social media visualization, we recommend:

• Matplotlib 
• Streamlight
• Power BI

Each one is a bit different, but all provide you with a way to take your raw data and turn it into something that allows you to get to work. Using these tools, you can see trends in your posts and track sentiment in real-time. When there is a concern, you will know about it far faster, and that means reacting to it sooner.

Social Media Sentiment Analysis Is a Smooth Resource for Your Business

For business owners of all types, even small companies, understanding how customers feel about the product they received or the way you treated them becomes critical. This understanding can define your growth and success in a company with limited projections for the future.

We recommend using only ethical methods and functions for sentiment analysis on social media. Ethical data mining or sentiment analysis preserves the peace of mind of the general public.

Now that you know how sentiment analysis social media functionality is up and running, you can start putting together your own tool. Start by connecting with Rayobyte. We have a comprehensive library of content you can use to build your web scraper and are also the best resource for proxy services. Connect to us now to learn more about what we can do for you.

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