Sentiment analysis has become a necessary, game-changing tool for businesses looking to understand customer feedback and improve their offerings.
After all, brands can uncover valuable insights into customer opinions by analyzing the tone behind online reviews, social media posts, and other text data.
That means, leveraging sentiment analysis is a must if you want to stay competitive in a customer-first market. So, whether you’re managing a single store or multiple locations, understanding how your audience feels impacts your strategy.
For that reason, I’ve prepared this guide to explore sentiment analysis, how it works, and why it’s an essential tool for businesses today. Let’s dive in!
What is sentiment analysis?
Sentiment analysis is the process of using natural language processing (NLP), machine learning, and text analysis to determine whether a piece of text expresses a positive, negative, or neutral sentiment. Businesses conduct this type of analysis to interpret the emotional tone of their customer feedback, reviews, and social media mentions.
‘Sentiment analysis’ definition
This is not a simple analysis of positive and negative words, as it studies the context and intent behind opinions by analyzing large volumes of text data to gain actionable insights into how customers perceive a certain brand.
Ultimately, sentiment analysis is an essential tool for businesses aiming to improve customer experiences and maintain a positive social media reputation.
So, once you are done with your social media listening, you must implement accurate sentiment analysis if you want to find the insights that will push your brand forward.
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How does sentiment analysis work?
Sentiment analysis relies on natural language processing (NLP) and machine learning to analyze text and determine its emotional tone. Here’s how it works step by step:
- Data collection—the process begins by gathering text data from various sources such as customer reviews, social media posts, support tickets, or surveys.
- Text preprocessing—the collected data is cleaned to remove irrelevant information like stop words, punctuation, or emojis. This step ensures the analysis focuses only on meaningful content.
- Sentiment classification—using NLP sentiment analysis algorithms or machine learning sentiment analysis models, the text is categorized as positive, negative, or neutral. Advanced tools can even detect emotions like joy, anger, or sarcasm.
- Scoring and analysis—each piece of text is assigned a sentiment score that quantifies the intensity of sentiment. This score helps businesses track trends, measure customer satisfaction, and identify problem areas.
By automating this process with tools like EmbedSocial, businesses can analyze feedback quickly and accurately, turning raw data into actionable insights.
What is a sentiment score?
A sentiment score is a numerical representation of the sentiment expressed in a piece of text, typically on a scale from -1 to 1 or 0 to 100. Negative scores indicate negative sentiment, positive scores indicate a positive one, with neutral scores near the middle.
For example, a sentiment score of 0.8 might represent highly positive feedback, while -0.5 could indicate dissatisfaction. Businesses use sentiment scores to quantify and analyze customer opinions at scale, making it easier to track trends and measure sentiment.
What’s a good sentiment analysis example?
The simplest and best example of sentiment analysis involves studying customer reviews. Imagine looking at the following restaurant review example:
“We ordered lasagna and tomato souce tagliatelli. Not very tasty, 2/5 for the two pasta. After that we ordered pistachio cake, it was delicious.”
In this case, sentiment analysis would identify “not very tasty” as a negative sentiment related to the main dish and “delicious” as a positive sentiment related to the dessert.
This allows the restaurant to pinpoint areas for improvement (main course) while recognizing strengths (great desserts), thus enabling targeted actions.
Benefits of sentiment analysis for multi-location businesses
Sentiment analysis offers numerous advantages for multi-brand and multi-location businesses, including but not limited to the following:
- Identify location-specific trends—helps analyze feedback unique to each location and address region-specific issues;
- Enhance customer satisfaction—enables businesses to quickly respond to bad reviews and improve experiences;
- Replicate successful practices—uses positive sentiments to identify winning strategies that can be applied across locations;
- Monitor brand reputation at scale—provides real-time insights into how customers feel about the business for optimizing your online reputation management;
- Boost local marketing efforts—leverages sentiment insights to create targeted campaigns that resonate with local audiences.
By implementing sentiment analysis, multi-location businesses can optimize their operations and maintain a consistent brand reputation. There’s no better market research, nowadays, as the customer’s voice is the one that matters the most.
How to do sentiment analysis for your multi-location business using EmbedSocial?
When you finally decide to conduct sentiment analysis for your business, products, and services, you should look into sentiment analysis software that can do it right!
One such option is EmbedSocial—a complete UGC platform that offers social listening and sentiment analysis features, such as the following:
- Aggregating social media content
- Tracking social media trends
- Responding to reviews from a central dashboard
- Managing social media hashtags and mentions
- Analyzing sentiment analysis reports
- Embedding social media posts on websites
That said, setting up an effective sentiment analysis workflow via EmbedSocial is rather straightforward, as it includes a few simple steps:
Step 1: Sign up and connect your accounts
First, you have to sign up for EmbedSocial and connect your social media:
- Create your account: Sign up for an account and log in;
- Add your sources: Click on ‘Sources’ → ‘Add new source’;
- Choose your platforms: Instagram, X, Facebook, or YouTube → ‘Hashtags’ or ‘Mentions’.
Step 2: Set up tracking of hashtags and mentions
Once you choose your sources, it’s time to set them up:
- Enter the hashtags or mentions: Add the specific hashtags you want to monitor, like your brand name or campaign-specific hashtags;
- Filter by location or language: You can set filters to make sure you’re tracking relevant social media posts in specific regions or languages.
Step 3: Set up your widget and start tracking
Now it’s time to set up your widget so you can generate all posts:
- Customize your widget: Choose a widget template, set up its layout and all elements, choose your color scheme, and more to get the look you want;
- Apply filters for location-specific posts: In the ‘All Posts’ tab, apply filters to show only posts from the hashtags and mentions you’ve added as sources. This way, you can focus on the locations and countries that matter most to your business.
Step 4: Filter, export, and analyze your hashtags and mentions
Time to analyze your tracked mentions and hashtags:
- Access the ‘Reports’ section: From your dashboard, navigate to the ‘Reports’ section, where you can analyze all your tracked mentions and hashtags;
- Refine your data with filters: Before exporting, use filters to narrow down your mentions by time, location, or media type. This helps you quickly download only the most relevant social media mentions, saving you valuable time in the process;
- Analyze your tracked posts: Now you can dig in and analyze your mentions and hashtags, looking for trends and sentiments related to your brand and products.
Step 5: Embed your hashtags and mentions on your website
Once you get your collected data of thousands of posts and user-generated content, the job is very easy. Use any AI LLM model, such as Chat GPT, to upload the data you downloaded from EmbedSocial and ask prompts to generate insights you never knew you could get in a second:
Here are the steps:
- Go to ChatGPT
- Upload the .csv file you generated from EmbedSocial
- Post a prompt to categorize and provide a sentiment analysis out of the data
- See the results and ask more questions
For help, here is a walk-through on how to do sentiment analysis out of the data you generate with EmbedSocial:
Step 6: Embed your hashtags and mentions on your website
Finally, EmbedSocial offers a robust solution for embedding your UGC across all your websites, and you only have to follow a few steps to do that:
- Open the widget editor: Access ‘Widgets’ → specific hashtag or mentions widget;
- Copy the embeddable widget code: Tap ‘Embed’ (top-left corner) → ‘Copy code’;
- Paste the code in your website: Open your website editor, navigate to the page that you want to edit, place an empty HTML container, and paste the code.
Bonus step: Use EmbedSocial’s advanced AI review summarizer
Another important EmbedSocial feature that you can use to gauge the overall sentiment about your products and services is our AI review summarizer.
As part of our strategy to offer AI-powered review management tools to our users, the review summarizer is both efficient and easy to use:
This feature automatically generates concise summaries of customer reviews to provide businesses with a brief sentiment analysis of the feedback they have.
Activating this summarizer tool is very easy: log into EmbedSocial, choose your newly-created widget, and under ‘General’ (left-hand ribbon), toggle ‘AI summary’.
The system will then generate the summary card and display it as shown above.
How to integrate sentiment analysis software into your business processes: Best practices
Now that you know what EmbedSocial does, how do you make the most of it? Here are several best practices to make the most out of your sentiment analysis system:
1. Set clear goals for your sentiment analysis
Start by identifying the specific areas where sentiment analysis can add value.
For example, do you want to improve customer satisfaction, add reputation management services to the mix, or fine-tune your marketing strategies?
Clear goals ensure that your sentiment analysis efforts are focused and effective.
2. Identify key data sources
Determine where your customer feedback is coming from.
For multi-location businesses, this might include online reviews, social media mentions, support tickets, and surveys.
Tools like EmbedSocial allow you to connect multiple sources seamlessly, ensuring no valuable feedback goes unnoticed.
3. Customize your sentiment analysis workflow
Choose sentiment analysis software that fits your workflow.
With EmbedSocial, you can set up custom tracking for hashtags, mentions, and reviews based on specific regions, languages, or platforms.
This ensures that the insights you gather are relevant to your locations and target audience.
4. Train your team to interpret results
Sentiment analysis is only as effective as the actions it inspires.
Educate your team on how to read sentiment scores, identify trends, and use the insights to make decisions.
For example, a marketing team can create campaigns around trending positive sentiments, while support teams can address recurring negative feedback.
5. Take immediate action based on insights
Use sentiment data to implement changes quickly.
If a specific location has negative sentiment about service speed, address it by improving staff training or operations.
Similarly, amplify positive feedback by sharing it on social media or featuring it in marketing materials.
6. Monitor and refine the process
Sentiment analysis isn’t a one-time effort.
Continuously monitor sentiment trends and refine your approach based on the results.
EmbedSocial provides tools for ongoing tracking, helping you stay ahead of shifts in customer sentiment and adapt your strategies accordingly.
7. Leverage AI-powered features for deeper insights
Consider integrating advanced features like AI summarizers for reviews.
EmbedSocial’s AI review summarizer, for instance, distills lengthy feedback into actionable insights, saving your team time and effort.
This ensures you focus on what matters most without missing important details.
Challenges to sentiment analysis
While sentiment analysis offers valuable insights, businesses must navigate several challenges to implement it effectively, especially for all their locations:
- Understanding context and nuance—sentiment analysis tools often struggle with sarcasm, irony, or complex emotions, which can lead to inaccurate results;
- Handling multilingual data—analyzing sentiments in multiple languages requires robust tools capable of understanding cultural and linguistic nuances;
- Processing unstructured data—customer feedback often comes in various forms like text, emojis, or slang, which can complicate analysis;
- Dealing with data volume—managing and analyzing large volumes of feedback from multiple locations can be overwhelming without scalable tools;
- Achieving actionable insights—it’s challenging to move from raw sentiment scores to practical strategies that drive meaningful change.
Investing in the right sentiment analysis tools and workflows can help businesses overcome these obstacles and unlock the full potential of customer insights.
Conclusion: AI sentiment analysis for multi-location businesses not only saves time but gives new insights
Sentiment analysis is no longer a luxury—it’s a necessity and one of the biggest reputation management trends of 2025—you need it to stay competitive and customer-focused!
After all, opinion mining to understand customer emotions empowers brands to make data-driven decisions that enhance experiences and boost reputation.
Thankfully, for multi-brand and multi-location businesses, the challenge of managing feedback at scale is met with innovative tools like EmbedSocial.
By offering features like AI-powered insights, social listening, and real-time tracking, EmbedSocial ensures that no customer voice goes unheard.
That said, as sentiment analysis evolves, businesses can expect even more sophisticated technologies to emerge, making it easier to understand feedback.
Ready to transform your customer feedback into actionable insights? Start leveraging sentiment analysis today and see the difference it can make for your brand!
FAQs about sentiment analysis
What is meant by sentiment analysis?
Sentiment analysis is a technique that uses natural language processing (NLP) and machine learning to determine whether a piece of text expresses a positive, negative, or neutral sentiment. It works by analyzing words, phrases, and context to assess the overall content sentiment.
What are the three types of sentiment analysis?
The three types are fine-grained sentiment analysis, which measures sentiment on a scale (e.g., very positive to very negative); aspect-based sentiment analysis, which focuses on specific aspects of a 3product or service; and emotion detection, which identifies emotions like happiness or anger.
What are the four main steps of sentiment analysis?
The four steps include collecting text data from sources like reviews or social media, preprocessing the data by cleaning it, using NLP or machine learning to classify the sentiment, and analyzing results to inform business actions.
How can sentiment analysis benefit businesses?
Businesses can use sentiment analysis to understand customer feedback, monitor brand reputation, enhance customer experiences, and identify areas for improvement in their products or services.
What tools are best for sentiment analysis?
There are several tools available, such as EmbedSocial, IBM Watson, and Brand24, which offer sentiment analysis capabilities to help businesses extract valuable insights from customer data.
