Blog / Sentiment Analysis

X (Twitter) Sentiment Analysis: 6 Simple Steps [2026]

Updated: April 2, 2026

13 min read

Are you wondering how people really feel about your brand on X (Twitter)? Without sentiment analysis, it’s hard to quickly understand whether the conversation is positive, negative, or shifting.

In this guide, you’ll learn how to run X (Twitter) sentiment analysis in 6 simple steps and understand what X users think about your brand, and how it changes over time.

How to do X (Twitter) sentiment analysis?

There are two ways to do X (Twitter) sentiment analysis: manually or with a social media monitoring tool.

Manual analysis

It means reading tweets and assigning sentiment yourself (positive, negative, neutral). It works for small datasets or quick checks, but becomes slow and inconsistent as volume grows.

Social media monitoring tools

They collect tweets/X posts automatically and classify their sentiment using AI. This lets you track large volumes of mentions and spot changes or trends in sentiment.

In addition to X, many social media monitoring tools analyze sentiment analysis for Facebook, Instagram, and other social media platforms.

Method	How it works	Best for	Limitation
Manual analysis	You read and label tweets yourself	Small datasets, quick checks	Time-consuming, not scalable
Social media monitoring tools	Tool collects mentions and detects sentiment automatically	Ongoing monitoring, trend tracking, spike alerts, brand analysis	Requires setup and tool access

Now let’s walk through the process step by step:

Step 1: Define what you want to analyze

X (Twitter) sentiment analysis can answer very different questions, for example:

• Are people reacting positively or negatively to our new campaign?
• Did a product update improve sentiment?
• Are complaints growing this week?
• Is our brand sentiment stronger than a competitor’s?

Your final tool setup will depend on the goal you choose, for example:

More examples:

• Monitoring brand reputation: Analyze overall brand sentiment
• Analyzing campaign performance: Track sentiment reactions to a campaign hashtag or influencers
• Monitoring product reception: Focus on negative customer feedback and complaints
• Competitor benchmarking: Compare sentiment breakdown

Step 2: Create a project for the keyword(s) you want to analyze

Start with the topic you want to track. This can be your brand name, product name, campaign hashtag, competitor, or any keyword people use when talking about you.

This step matters because sentiment analysis is only as good as the query behind it:

If your keyword setup is too broad, you’ll collect a lot of irrelevant mentions out of the topic. On the other hand, if it’s too narrow, you’ll miss important ones.

👉 If you use Brand24:

Create a project for your chosen keyword/keywords or hashtags.

You can also use Advanced settings and choose the language of mentions or type in required/excluded keywords if you want to narrow the results from the start.

Step 3: Narrow the mentions dataset to X (Twitter)

X sometimes works differently from other socials, news sites, forums, or review platforms, so if you want to understand sentiment there, you need to look at X mentions only.

This step ensures you’re analyzing only X (Twitter), not general online sentiment.

👉 If you use Brand24:

1. Open your project
2. Go to Mentions tab
3. Use the panel on the right: Filters → Source → X (Twitter)
4. Save this filter if you’ll use it often

Step 4: Check the volume and sentiment split

Before you dive into sentiment more granularly, it’s good to look at the overall, bigger picture:

This step helps you understand the scale and direction of the X conversations.

For example, a sudden increase in negative mentions may signal that something specific happened (like a viral negative post or a controversial event) and needs your attention.

👉 If you use Brand24:

Go to the Mentions tab and look at the chart at the top. Focus on:

1. The number of mentions over time
2. The proportion of positive vs negative mentions
3. Any visible spikes or drops

If something looks unusual, you can narrow the date range to zoom in and browse through the mentions to get a clearer picture of what’s going on.

Step 5: Analyze what caused the sentiment shift

After identifying a spike, the key question becomes: what caused it?

Numbers alone won’t give you that answer. You need to look at the actual tweets behind the change and understand the context.

This is where sentiment analysis becomes useful: when you move from “what happened” to “why it happened.”

You can start by reviewing the most influential tweets in that time period:

Compare what shows up in positive vs negative mentions. This helps you understand the sentiment breakdown and the context behind it.

👉 If you use Brand24:

Go to the Analysis tab, set the time range to the spike, and filter by:

1. Source: X (Twitter)
2. Sentiment → Negative (or Positive)

You can also ask the AI Brand Assistant to explain which exact event, trend, or issue caused the spike.

Step 6: Compare sentiment in context

At this point, you know what the sentiment breakdown is. However, you still need context to interpret it correctly.

A single number (for example, 20% negative sentiment in the last 30 days) doesn’t tell you much on its own. You need a point of reference to decide whether it’s good, bad, or normal.

You can compare:

• current results vs previous period
• before vs after a campaign or event
• your brand vs competitors

This step helps you answer more meaningful questions, such as:

• Is sentiment improving or getting worse?
• Did the campaign change how people react?
• Was the spike temporary or part of a larger trend?
• Are we perceived better or worse than competitors?

👉 If you use Brand24:

Go to the Comparison tab, where you can:

1. Compare your brand’s sentiment with a competitor
or
2. Compare your brand’s sentiment across different time periods

Then analyze differences in:
– positive vs negative sentiment,
– number of mentions,
– reach.

What to do with X (Twitter) sentiment analysis results

Once you’ve analyzed sentiment on X (Twitter), the next step is simple: use it to make decisions.

Sentiment data becomes valuable only when it changes how you respond, what you improve, or how you communicate.

Here’s how to put it into practice:

01 Address negative feedback

A flood of negative tweets can damage your reputation and hurt your business. If there is a lot of negative feedback about a product or service, it’s essential that you address those issues quickly.

Sometimes it is good to react to individual mentions, but you can also look for patterns:

• Are people complaining about the same feature?
• Is one issue being repeated by different users?
• Did negative sentiment increase suddenly?

Some social media monitoring tools (e.g., Brand24) have Storm Alert features. So you will be notified whenever there is a rapid change in the mentioned volume.

👉 If you use Brand24:

Set up Storm Alerts to get notified when mention volume spikes. This helps you catch potential issues before they grow.

02 Observe what brings positive tweets

By keeping an eye on X activity around your brand, you can get a clear sense of what people actually like about it.

Using social media monitoring is a solid way to spot what types of content spark positive tweets and which topics get people engaged.

Look beyond “people liked it” and ask:

• Are the same themes repeated?
• What exactly are they reacting to (A whole post? One comment? Viral video? A fresh POV on some issue?)
• Which campaign, message, or feature triggered it?

For businesses, those insights can help shape smarter marketing and customer service strategies.

03 Prioritize product changes

Use social media sentiment analysis data to figure out which areas need attention first, so you can prioritize your updates.

For example, if customers are frustrated about slow shipping, it makes sense to fix that before tackling anything else.

04 Monitor trends

Use X (Twitter) sentiment analysis over time to monitor rising trends on X and hashtags in customer feedback and respond quickly if needed.

Tracking it over time helps you:

• detect emerging issues
• measure the impact of campaigns
• see whether improvements are working

This will help ensure that customer satisfaction remains high over the long term.

Twitter sentiment analysis use cases

Twitter sentiment analysis is used to support business decisions across different areas. It’s often combined with opinion mining, which goes one step further.

While sentiment analysis tells you whether an X (Twitter) mention is positive, negative, or neutral, opinion mining focuses on what exactly people are reacting to: for example, price, quality, delivery, or specific product features.

Here’s a short video that sums up how to use sentiment analysis to find out what your customers like (or dislike) about your company or product:

Because Twitter provides large volumes of fast, unfiltered reactions, both approaches are widely used in practice:

01 Business

In business, companies use opinion mining tools and X (Twitter) sentiment analysis tools to understand what customers think about their product, service, brand, or campaigns.

The difference is:

• sentiment analysis: shows overall attitude (positive/negative)
• opinion mining: shows why people feel that way

For example:

• negative sentiment may increase or decrease over time or spike at a single point of time
• opinion mining reveals that most complaints mention delivery delays and pricing

This allows teams to move from “people on X are unhappy with our product” to “people on X are unhappy with our product because of A, B, and C”, and fix the right problem first.

02 Finance and market analysis

In finance, Twitter sentiment analysis is used to track how people react to companies, industries, and news using X data and other social media sources.

One common application is stock market prediction using Twitter sentiment analysis. Analysts look at huge volumes of tweets to detect changes in public opinion.

For example, increasing negative sentiment around a company can reflect declining confidence before it even shows up in traditional metrics.

Another finance-related use case is Twitter crypto sentiment analysis, which uses X sentiment to track reactions to cryptocurrencies like Bitcoin and Ethereum.

03 Politics

In politics, sentiment analysis and opinion mining are used to track public opinion at scale.

Instead of surveys, analysts use Twitter data to monitor reactions to:

• policy changes
• political statements
• public figures

Opinion mining is especially useful here because it helps identify:

• which topics people react to most strongly
• what specific issues drive positive or negative sentiment

For example, sentiment analysis was used in research around the Brexit vote, where large volumes of tweets were analyzed to track how public opinion evolved over time and in some cases, to predict election outcomes.

04 Public actions and cultural events

X (Twitter) sentiment analysis is also used to study reactions to major cultural and social events.

Examples like Pokémon Go, the premiere of Game of Thrones, or the Oscars show how quickly sentiment can shift and how discussions evolve in real time.

Here, sentiment analysis shows how strong the reaction is, and what exactly people are reacting to (storyline, features, performance, etc.).

Conclusion

You can quickly analyze posts on X with a Twitter monitoring tool. It can automatically spot positive and negative posts and display the sentiment results in a clear, easy-to-read format.

Thanks to sentiment analysis on X (Twitter), you can:

• See what people are saying about your brand or product,
• Spot whether the buzz is positive or negative,
• Assess overall customer satisfaction,
• Catch emerging trends early,
• Find trending links and hashtags,
• Find the most active Twitter influencers,
• Keep an eye on your brand reputation and prevent PR crises.

FAQ

What is X (Twitter) sentiment analysis?

X (Twitter) sentiment analysis is the process of identifying whether tweets express positive, negative, or neutral opinions about a brand, topic, or event.

It helps you understand how people feel by analyzing large volumes of X (Twitter) data in real time.

Why is X (Twitter) sentiment analysis important?

X (Twitter) sentiment analysis is important because it helps you understand how people react in real time to your brand, product, or campaign. It can help you to:

• monitor and manage brand reputation
• detect negative trends early
• prevent PR crisis at an early stage
• understand what drives positive reactions
• make faster marketing and PR decisions

How does X (Twitter) sentiment analysis work?

Every tool uses its own sentiment analysis algorithm. Here’s how Brand24’s works, explained from a technical perspective:

How to get X (Twitter) data for sentiment analysis?

You can get X (Twitter) data for sentiment analysis in two ways:

• manually, by collecting tweets yourself
• automatically, using a social media monitoring tool or API

In practice, most teams use tools that collect tweets in real time based on selected keywords and prepare them for analysis.

What is the scope of X (Twitter) sentiment analysis?

The scope of Twitter sentiment analysis includes:

• brand monitoring
• customer feedback analysis
• campaign evaluation
• competitor analysis
• trend and topic tracking

Because X provides fast, public data, it’s often used to analyze real-time public opinion across different industries.

Why use X (Twitter) for sentiment analysis?

X (Twitter) is widely used for sentiment analysis because:

• users share opinions quickly and publicly
• discussions happen in real time
• reactions to events, brands, products, globan and local news appear almost instantly

This makes X (Twitter) one of the best sources for tracking live sentiment and opinion shifts.

What sentiment analysis can you do on X (Twitter)?

On X (Twitter), you can perform several types of sentiment analysis, including:

• classifying tweets as positive, negative, or neutral
• tracking sentiment trends over time
• detecting spikes in negative or positive sentiment
• identifying topics behind sentiment (opinion mining)
• comparing sentiment between brands or time periods

How accurate is X (Twitter) sentiment analysis?

The accuracy of X (Twitter) sentiment analysis depends on the method and tool used.

Modern AI models based on Machine Learning, Deep Learning, and Pretrained Language Models can achieve high accuracy, especially at scale. That said, they can still struggle with context-heavy tweets, sarcasm, or slang.