54% of social media users use these platforms for product research. Negative tweets or scathing Tiktok reviews can literally make or break a brand. And that’s where social media sentiment analysis comes in.

AI analytics tools allow you to rapidly analyze thousands of posts from social media channels. They can give you insights into the underlying sentiment behind those interactions. Once you understand how your customers really feel, it’s easier to address complaints, improve the customer experience, and identify opportunities for growth.

In this guide we’ll cover what social media sentiment analysis is and why it matters. We’ll walk you through a step-by-step guide to doing your own analysis and take a look at the best social media sentiment analysis tools currently on the market.

What is Social Media Sentiment Analysis?

Sentiment analysis is the process of determining the emotional tone of textual data. The goal is to classify the sentiment of the text as positive, negative, neutral, or on a more granular scale. This allows you to better understand the writer’s attitude or emotions.

Sentiment analysis is a form of text analytics that uses various AI methods. Traditionally, sentiment analysis was automated using natural language processing (NLP) and machine learning. Most recently, Large Language Models (LLMs) such as ChatGPT have been successfully applied to this task.

Sentiment analysis can be combined with Thematic analysis which identifies and interprets patterns and themes in qualitative data. This enables you to dive deep into how your customers feel about a particular topic, such as customer support interactions or specific product features.

Why Does Social Media Sentiment Matter

Social media sentiment analysis isn’t just about tracking what people say online. It’s about understanding how they really feel. Let’s take a closer look at why social media sentiment analysis matters for all types of organizations.

1. Better Brand Management

A single negative incident that gets posted on Tiktok or Instagram can quickly spiral into a PR crisis and damage a brand's reputation. Social media sentiment analysis allows organizations to monitor brand perception in real-time and identify issues as they emerge so they can be dealt with promptly.

And customers expect prompt responses when they reach out on social media. According to a recent McKinsey report, 79% of consumers expect brands to respond within 24 hours and 40% expect an answer within the hour. This is especially true when it comes to complaints and negative feedback.

Example of Social Media Sentiment Analysis for Brand Management

Nike’s decision to partner with Colin Kaepernick for an ad campaign a few years back was controversial. But Nike had done their homework and knew that their core customers, mostly younger Americans, supported their message. That’s why they chose to release their Nike Dream Crazy ad on Youtube rather than on traditional channels. Nike monitored social media channels during the campaign using sentiment analysis and tracking public reactions in real-time. The result was a hugely successful campaign, generating earned media worth $163 million, adding $6 billion to Nike’s brand, and boosting sales by 31 percent.

2. Understanding Customer Feedback

Only 1 in 26 unsatisfied customers actually complain to the company. But the good news is that customers are more likely to share unfiltered feedback on social media than in direct surveys or customer service channels.

Social media sentiment analysis can complement Voice of Customer programs by adding more color to existing customer feedback. These comprehensive insights can then be used to improve customer satisfaction.

Example of Social Media Sentiment Analysis for Understanding Customer Feedback

Coca-Cola takes social media sentiment analysis seriously. The company monitors negative mentions on social media to identify any emerging issues their customers are talking about. For example, customers might complain about packaging, communications, or major innovations like the controversial New Coke. Coca-Cola can then move swiftly to fix the issue.

3. Gaining Actionable Insights

Organizations that use customer data to drive their decisions are 23 times more likely to acquire customers than their competitors according to McKinsey. They’re also 19 times more likely to achieve above-average profitability.

Applying sentiment analysis to social media gives brands big-picture insights into market trends and customer preferences. This enables companies to tweak their product development, marketing strategies, and overall business decisions in ways that will actually move the needle. Additionally, tracking social media sentiment around your competitors can help identify opportunities to outperform them or gaps in the market.

Example of Social Media Sentiment Analysis for Actionable Insights

A brand launching a new product can track real-time reactions on social media to monitor how the campaign is landing with their target audience. If there is a lot of negative feedback, they can quickly respond or adjust their messaging.

How Does Social Media Sentiment Analysis Work?

Sentiment analysis is a form of text analytics that uses various AI methods. Traditionally, sentiment analysis was automated using natural language processing (NLP) and machine learning. Most recently, Large Language Models (LLMs) such as ChatGPT have been successfully applied to this task.

Advanced AI sentiment analysis tools can pick up on the following aspects and uncover the feeling behind the words:

Tone: The overall tone of a piece of text or a phrase can be graded as positive, negative, or neutral. Words like "love" or "hate" make it easier to detect emotion, but they can be misleading when there are double negatives, jokes, or sarcasm.

Emotion: Emotion analysis is a specific type of sentiment analysis that seeks to recognize a wider range of emotions like happiness, anger, surprise, or disgust.

Context: Context is crucial for really understanding sentiment. And it’s especially important when it comes to the informal language used on social media. For example, "This is sick!" could be positive or negative depending on how it’s used. That’s where AI analysis comes in. Large Language Models (LLMs) take into account the entire sentence and surrounding posts to figure out the true meaning.

How Sentiment Analysis Tools Work

Sentiment analysis using analytics tools typically involves the following steps:

1. Data Collection: The first step is to pull relevant data from social media platforms and collate it in one place.
2. Data Preprocessing: Next the text is cleaned up. Irrelevant elements like URLs, spammy content, and special characters are removed.
3. Sentiment Scoring: The data can now be analyzed for sentiment. Machine learning models are trained on vast datasets to help them determine whether these tokens indicate positive, negative, or neutral sentiment. Modern AI-powered sentiment analysis tools can also factor in emojis and images.

Data Collection

The process begins with selecting and collating appropriate data from Twitter, Facebook, Instagram, and more. There are two primary methods for collecting this data:

• APIs (Application Programming Interfaces): Social media platforms usually offer APIs that allow developers to extract public data. Twitter’s API allows you to pick out tweets based on hashtags, keywords, or specific users.
• Web Scraping: When APIs aren't available or offer limited access, web scraping is another option. This process uses automated scripts to pull data directly from web pages. Web scraping can be handy for gathering large amounts of data, but you need to be careful to avoid potential violations of a platform’s terms of service.

Data Preprocessing

Raw social media data is often noisy. It might contain hashtags, links, mentions, or emojis. This type of data needs to be preprocessed to ensure accurate sentiment analysis. This is usually done automatically by AI sentiment analysis tools.

• Removing irrelevant elements: URLs, special characters, mentions (like @usernames), and hashtags that don’t add value to sentiment analysis need to be stripped out.
• Converting emojis and slang: Emojis and internet slang are important indicators of sentiment. They should be converted to corresponding meanings. For example, “😊” usually indicates positive sentiment.
• Tokenization: In this process the text is broken down into smaller chunks called tokens. For instance, the sentence “I love this product!” would be tokenized into ["I," "love," "this," "product"].
• Lemmatization or Stemming: These techniques reduce words to their base form.  For example, the word "running" becomes "run". It makes it easier to match words to a lexicon or detect patterns in sentiment.

AI Sentiment Analysis Tools vs. Traditional Sentiment Analysis

Traditionally sentiment analysis has been done using a set of rules to categorize sentiment as positive, negative, or neutral. For example, a simplistic rule could be “if a sentence contains the word ‘great’, then the sentiment is positive”.

However, these rules have their limitations. If we apply this rule to the text below, you can see that it doesn’t work well with more complex text:

Using LLMs for Sentiment Analysis

Most recently, Large Language Models (LLMs) have been successfully applied to this task. Instead of providing training data, as was previously the case with traditional Machine Learning solutions, you just need to write a prompt. LLMs have superseded all previous research by creating a universal model that can be applied to many language understanding and language generation tasks.

Large Language Models offer better accuracy compared to more traditional solutions. But they also require careful prompt engineering, are too slow for some applications, and can get costly.

Using ChatGPT for Sentiment Analysis

To show you how LLMs outperform traditional rule-based sentiment analysis we took the sentences above and ran them through ChatGPT.

AI analytics tools like Thematic use large language models (LLMs) such as GPT4 with their own algorithms. This makes it easier and faster to get specific and reliable answers and gain valuable insights.

The Challenges of Social Media Sentiment Analysis

Social media sentiment analysis comes with its own unique challenges. It’s tough even for us humans to understand what’s being said sometimes. Shorthand, emojis, and jokes all make it hard to gauge how people really feel. And since context changes so quickly, something that seems positive in one post might actually be negative when you dig deeper.

Irony and Sarcasm

With irony and sarcasm people use positive words to describe negative experiences (or vice versa). It can be tough for AI to understand the underlying sentiment. Let’s take something like "Oh great, another Monday!". It might sound positive at first glance, but it’s often used to convey frustration. Traditional rule-based sentiment analysis tools could misclassify this as positive because they rely heavily on the literal meaning of the words.

Humor

Humor often also involves nuanced meanings that are at odds with the surface content. A jokey tweet can mislead basic sentiment analysis algorithms. Humor often relies on cultural knowledge, double meanings, or playfulness. It can be hard for algorithms to pick up on these and gauge the correct sentiment behind a post.

Slang and Informal Language

Slang is another big one for social media. Traditional NLP models may not be up to speed on the latest language trends. For example, words like “lit” or “fire” may not be categorized correctly. The same goes for hashtags, abbreviations, multilingual posts, and expressions which are specific to certain regions.

Context

Context is crucial when it comes to understanding sentiment. Opinion words can change their polarity depending on the context. For example, the word "sick" could be negative in phrases like "I feel sick," but positive in a statement like "That car is sick!"  Machines need to learn about context in order to correctly classify a text.

This also applies to posts that are part of a wider conversation which may not be picked up by the algorithm. Social media posts are often short due to character limits. This leaves little room for detailed context and makes it harder to capture the underlying meaning.

Emojis, GIFs, and Memes

Emojis and GIFs are almost a language of their own when it comes to social media posts. We often use them to express complex emotions or meanings linked to contexts that can’t easily be translated into text. Tools need to incorporate multi-modal analysis (text + image) to better understand the sentiments expressed through these forms.

Negation

Negation can also create problems for traditional sentiment analysis models. For example, if a product reviewer writes “I can’t not buy another Apple Mac'' they are stating a positive intention. In the past, it was difficult to train models to recognize such nuances. Today, LLMs recognize them with ease.

Audiovisual Content

Video and audio are a very different type of data to text. Audio on its own or as part of videos first needs to be transcribed. The text can then be analyzed using Speech-to-text algorithms. Sentiment analysis can analyze transcribed text in the same way as any other text.

Top Tools for Social Media Sentiment Analysis

As we’ve seen above, sentiment analysis for social media needs advanced tools that go beyond simple rules-based categorization. A comprehensive sentiment analysis tool should be able to handle complex linguistic structures, make sense of context, and deal with the unique features of social media posts like hashtags and audiovisual content.

1. Thematic
2. Brandwatch
3. Sprout Social
4. Hootsuite Insights

Thematic

Thematic specializes in advanced thematic and sentiment analysis powered by AI. Thematic works by automatically identifying themes, or topics, in your feedback and layering this with sentiment analysis.

Key Features

Thematic is designed to handle large volumes of unstructured data from various sources like surveys, social media, and reviews. This makes it ideal for businesses with extensive customer interactions who want to collate and analyze all their data in one place.

Thematic offers robust customization features so organizations can tailor sentiment analysis to their specific needs and industry contexts. Users can create custom themes based on their unique feedback for greater relevance and accuracy. Thematic also leverages Generative AI to continuously improve accuracy and adapt to evolving language patterns. This is especially relevant for social media sentiment analysis where the use of language is constantly shifting.

Limitations

Thematic does not offer granular emotion analysis beyond positive, neutral, and negative sentiment.

Pricing

Thematic offers a free guided trial so you can try out the platform using your own data. Rates for larger enterprises are flexible and customized depending on your comment volumes and datasets.

Brandwatch

Brandwatch specializes in social media monitoring. The platform helps businesses track and analyze online conversations happening around their brand in real-time. This includes social media platforms, blogs, and news sites.

Key Features

​​Brandwatch offers real-time sentiment tracking, customizable dashboards, historical data analysis, and competitive benchmarking. A big pro is that Brandwatch gathers data from lots of different sources, so you get a comprehensive look at online chatter about your organization.

Limitations

On the flip side, G2 reviewers have reported that it can take a long time for data to load, especially after updating queries. Connecting the platform with your other tools and channels can also be challenging.

Pricing

Brandwatch offers several pricing plans ranging from standard users to enterprise clients. Some G2 reviewers have reported that the platform is more expensive compared to other social listening tools.

Sprout Social

Sprout Social offers robust social media management features alongside sentiment analysis. You can use it for everything from scheduling posts and identifying influencers to monitoring your brand reputation on social media.

Key Features

Sprout Social’s software makes it easy to build queries to track social media conversations on certain topics without much technical knowledge. Their Sentiment Summary and Sentiment Trends metrics show you how people feel about your brand on social media, while the sentiment analysis widget tracks positive, negative and neutral mentions over a set period.

Other useful features include Share of Voice (SOV) tracking, custom alerts, and “Word Cloud” visualizations which highlight the top trending keywords and hashtags associated with your chosen topic.

Limitations

G2 reviewers have reported issues with missing data and difficulties connecting their social media channels to the site.

Pricing

Sprout Social offers a 30-day free trial to new users. After that there is a sliding scale of pricing plans ranging from $199 to $399 per seat per month depending on what features you need. There are also custom plans available for larger enterprises.

Hootsuite Insights

Hootsuite is another social media management tool that also offers sentiment analysis and social listening. They claim to have the most comprehensive social listening coverage on the market.

Key Features

Hootsuite provides real-time data and integrates seamlessly with all the major social platforms. Their proprietary Blue Silk AI has been designed specifically for social media. Users can monitor brand mentions with the Quick Search tool which allows you to search the web in real-time. The results are color-coded by sentiment to make it easy to understand at a glance.

Limitations

G2 reviewers have reported that Hootsuite’s analytics capabilities are limited if you don’t have the advanced plan. Additionally, the platform can run a little slow and be cumbersome to use.

Pricing

Hootsuite offers a 30-day free trial. After that you can choose between Professional membership for individuals, a Team plan for 3 users, or a custom Enterprise plan for larger ogranizations.

Implementing Social Media Sentiment Analysis

Now we’ve covered the basics of what sentiment analysis is and how you can apply it to social media posts, let’s explore the practical steps you need to get started.

3 Steps to Get Started with Social Media Sentiment Analysis

Step 1: Pick the Right Tool

There are dozens of sentiment analysis tools on the market from basic free options to advanced AI-powered solutions. Simple tools are probably sufficient for small one-off projects, while larger organizations with larger datasets will need a more comprehensive platform with greater reporting and analytics capabilities.

Questions to Ask When Choosing a Social Media Sentiment Analysis Tool:

• Which platforms do you need? Make sure the tool integrates with the social channels where your audience hangs out.
• Can it handle multiple languages? You might need these capabilities depending on your audience.
• How customizable is it?  Look for tools that allow you to tweak settings or add your own keywords.
• Does it offer real-time updates? This is crucial for crisis management or quick response needs.
• How advanced are the analytics capabilities? Traditional rules-based tools tend to be less accurate than those using LLMs. Consider whether you need a highly-accurate tool capable of analyzing huge amounts of data or whether a simpler solution will do.

Step 2: Set Up Your Data Monitoring

Setting up your data monitoring is like tuning a radio. You want to lock in on the right frequencies (keywords, hashtags, and mentions) to get the clearest picture of your brand.

How to Setup Social Media Sentiment Analysis Monitoring:

1. Pick Your Keywords & Hashtags: Identify the keywords, hashtags, and phrases people use when talking about your brand, products, competitors, or industry.
2. Monitor Competitors: Track what people are saying about your competitors too. This can give you insights into gaps in the market and help you spot industry trends.
3. Set Up Alerts: Make sure your tool can notify you of significant shifts in sentiment. For example, a sudden uptick in negative feedback on a specific topic could be a sign of an emerging issue.

Step 3: Analyze and Interpret Your Data

At this point you’re ready to start analyzing and interpreting your data. The easiest way to do this is to use a comprehensive AI tool like Thematic that can automatically pick out themes in your data and summarize the underlying sentiment.

Interpreting Your Data with Social Media Sentiment Analysis:

• Summarize Overall Sentiment: Are people talking positively or negatively about your brand? Look for changes over time and monitor how and why your brand reputation fluctuates.
• Analyze Sentiment by Topic: Combine thematic and sentiment analysis to dive deeper into specific topics. This will allow you to see the aspects of your business your customers are the most upset or happy about.
• Trending Phrases: What are the most talked-about words and phrases? These can show you what’s important right now for your audience and help inform future business decisions.

Best Practices for Social Media Sentiment Analysis

Context is Everything:

As we saw above, a comment like “this product is sick” could be positive or negative depending on the context. Try to always look beyond the sentiment score and take context into account. More advanced AI-powered tools that use LLMs to categorize sentiment tend to give more accurate results.

Customize the Tool

Advanced tools often allow you to customize and fine-tune according to your specific business needs. For example, with Thematic you can add, merge, and delete themes using the Themes Editor. This can help improve accuracy and give you insights that better inform your business goals.

Combine Qualitative and Quantitative Data

Quantitative data like overall sentiment scores are useful for taking the temperature of your brand reputation, for example. But for added value we recommend combining them with qualitative insights from your data. Read through the associated comments and posts to understand exactly what your customers are saying and what issues might need to be addressed.

How Thematic Can Help You Analyze Customer Feedback Beyond Social Media

Sentiment & Thematic Analysis using Thematic

Thematic combines traditional AI and Large Language Models (LLMs) to perform sentiment analysis. A unique feature of Thematic is that it combines sentiment with themes discovered during the thematic analysis process. Let’s take a closer look at how you can use Thematic to analyze everything from social media posts and online reviews to support tickets and emails.

Step 1: Upload Your Data

Thematic’s wide range of one-click integrations make it easy to connect all your channels. These include Qualtrics, Trustpilot, Amazon, Facebook, Intercom, Twitter, Tripadvisor, and many more. Thematic then automatically cleans and prepares your data so it’s ready to be analyzed.

Step 2: Analysis

Thematic analysis using Thematic

Thematic’s AI automatically groups themes into a 2-level taxonomy. For a given text there will be core themes and related sub-themes. In the image below you can see the top themes for a dataset and the sub-themes displayed below these.

Sentiment Analysis using Thematic

Thematic uses sentiment analysis with thematic analysis to help you understand the emotion behind a theme. Sentiment analysis scores each piece of text or theme and assigns positive, neutral or negative sentiment.

In the example below you can see the theme “print boarding passes”. Thematic enables you to dig deeper and understand how your customers feel about this topic using the sentiment scores. In this case we can see that Southwest’s lack of assigned seating is associated with negative sentiment.

Step 3: Sentiment Analysis + Metrics

Thematic and sentiment analysis together can help you better understand key customer metrics like NPS. In the example below you can see the key themes impacting NPS and monitor the degree of impact they have had on the score. You can also track this over time to see how your efforts to improve certain issues have impacted NPS.

This feature is especially useful if you want to share your valuable insights with key stakeholders and you need clear visualizations that get your message across easily. This makes it really easy for stakeholders to understand at a glance what is influencing key business metrics.

Step 4: AI + Human

Another handy feature of Thematic is the ability to make manual tweaks to your analysis. This approach incorporates both AI capabilities and a human touch to help ensure greater accuracy. You can use the Themes Editor to delete, edit, merge or create new themes. This can be useful, for example, if you have themes which are not relevant to your organization.

Try Thematic on Your Data

Still not sure which social media sentiment analysis tool is right for you? The best way to find out is to try them out for yourself. Why not start right here with a free guided demo of Thematic using your own data.