5 Steps To Conduct Twitter Sentiment Analysis

Twitter gives businesses the chance to engage with consumers on a personal level. Nevertheless, with the abundance of data that is provided on this social media platform, it can be hard for brands to process which sort of tweets are the most effective for their audience. 

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This is where sentiment analysis really shows its worth. It has become a vital social media marketing tool. This involves monitoring social media conversations to understand the emotion behind each message. After all, words are interpreted in different ways, so to truly understand your audience, you need to grasp the feeling behind each message. 

Listening to the voice of customers by using sentiment analysis on Twitter is going to enable you to get a much better understanding of the people you are connecting with, as well as keep on top of what is also commonly being said in your industry so you can be abreast of the latest trends.

With that being said,  we will explain what sentiment analysis is, as well as reveal how to conduct Twitter sentiment analysis.

What is sentiment analysis?

Sentiment analysis is a term used to describe the automated process of identifying and classifying any subjective information in a piece of text. When you classify tweets with sentiment analysis, this typically includes a feeling, judgment, or opinion on a certain product, product feature, or topic. 

"Polarity detection" is a common form of sentiment analysis. This involves classifying Tweets into one of the three categories: neutral, positive, or negative. This will help you to get an understanding of the general feel around a topic, product, service, or event.

For instance, if you have announced a music festival line-up on Twitter, you can use polarity detection to determine how people have reacted to it. If the overall sentiment is negative, you may want to consider adding some more bookings to the line-up. 

To make sense of the human language, sentiment analysis makes the most of Natural Language Processing (NLP). To ensure the results are accurate, machine learning also plays a vital role. 

There are a lot of different sentiment analysis tools available on the market today for you to choose from. Many of these tools will not only help you to perform sentiment analysis on Twitter but on other social media platforms as well. 

How do you perform sentiment analysis on Twitter?

Now that we understand of what sentiment analysis is and why it is so important for Twitter, let's take a look at the different steps you need to follow to perform this:

Gather data on Twitter

There is only one place to begin: by gathering all of the data you need on Twitter. It is critical to ensure the data you accumulate on Twitter represents what you are trying to find out.

This is because the data will not only be used for training your sentiment analysis model but also to test how your model performs on Twitter data. 

Think carefully about the kind of tweets you want to assess. Do you want to simply look at current tweets? This can be helpful when wanting to track hashtags and keywords in real-time so you can refine your Twitter marketing efforts and publish tweets that are likely to perform well right now. 

Another option that can be beneficial is the comparison of historical tweets. This can be used to determine and compare sentiments over different time periods. 

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If you do not have the data you need, saved in an Excel file, ready to go, you may be wondering how you go about getting all of your data together. 

One of the easiest options is to use Zapier and create a Zap. A Zap is basically an automated workflow on Zapier. You can select one app as the Trigger, which is for data extraction, and then another for the Action, which is where the data is going to be sent. 

Another option is to use the "Export Tweet" function on Twitter. However, we would recommend upgrading to the premium option if you go down this route, as there are many limitations with the free version. 

Prepare your data

Now that you have gathered all of the data you need, it is time to prepare it. This is because the data that we extract from social media is unstructured. Before it can be used to train a sentiment analysis model, it needs to be cleaned up. 

Do not underestimate this step. While it may be tempting to skip it, remember that high-quality data will lead to better and more accurate results when all is said and done. 

There are a number of different tasks that need to be completed to preprocess a Twitter dataset. For example, you need to remove all kinds of information that is irrelevant, such as extra blank spaces and special characters. 

Some of the other steps you may need to take include deleting tweets that have less than three characters (you're not going to learn much from those), deleting duplicate tweets so that they do not cause inaccurate results, and making format improvements. 

Create your model for sentiment analysis on Twitter 

Now, it is time to create your model to carry out a Twitter sentiment analysis. To do this, you are going to need to have the right tool in your armory. 

As mentioned, there are different sentiment analysis solutions on the market today to select from. When narrowing down your search, make ease of using a priority. You need something that your team is going to be able to get to grips with, so they can easily make adjustments and use pre-trained models for greater efficiency. 

No matter the solution you choose, your first port of call is typically selecting a model type. From here, you will select that you want to perform sentiment analysis. You will then need to import your Twitter data. Once you have done this, tag data so you can train your classifier. By manually tagging tweets as either neutral, positive, or negative based on the polarity of the opinion, the machine will start to learn how to categorize tweets in an effective manner. 

You can then get the tool to perform sentiment analysis on a set of data. You can make corrections for the first few, ensuring that the software is learning all of the time so that it can provide you with the most accurate results in the long run. 

Remember that the more data you tag during the training phase, the more accurate your classifier is going to end up being. 

Another effective way of enhancing how accurate your model is by checking all of the false negatives and false positives, re-tagging any that are not correct. 

Analyze your data on Twitter for sentiment

Now that you have everything set up, your model is ready to go and you can start assessing your data on Twitter, looking for the sentiment. 

You will need to integrate the data from Twitter that you wish to analyze with the sentiment model that you have just generated. 

Depending on the platform you have chosen, there are different ways you can go about this.

The majority of the solutions on the market today have Batch Analysis. Simply go to "Batch" and then upload an Excel File or a CSV with new, unseen tweets. All of the tweets will then be processed. Typically, you will receive a new file that has all of the results of the sentiment analysis. 

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Aside from this, the leading analysis tools on the market today also come with integrations that you can make the most of. For example, if you have used Zapier, as suggested before, you should be able to simply connect this to input your data and perform the sentiment analysis. The same goes for any other popular tool, such as Google Sheets. 

There may be other options available, such as API, depending on the tool you have chosen.

Visualize the results

The final piece of the puzzle is to use data visualization tools so you can see what the results mean in a clear, visual format. 

While this is not a necessity, it can certainly help you and your team to get a clearer picture, so we highly recommend using some form of data visualization. 

Understand your audience better with sentiment analysis

So, everything you need to know about performing sentiment analysis effectively on Twitter. 

Sentiment analysis is a great way of truly understanding how people feel toward a product, service, or topic. 

This will help you to get a better understanding of the general feeling toward your brand so that you can make better decisions and move your company forward. 

Invited writter

Kerry Leigh Harrison has over 11+ years of experience as a content writer. She graduated from university with a First Class Hons Degree in Multimedia Journalism. In her spare time, she enjoys attending sports and music events. 

Feel free to contact us to see the unprecedented growth of your business.

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