Sentiment Analysis for Marketers

The Local Marketer’s Introduction to Sentiment Analysis

Sentiment analysis empowers businesses to better understand their customers – to better meet (and exceed) their expectations. Explore how it's done!

Lara Siebert • 5 min read • Aug 26, 2025

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The modern marketer relies on a treasure trove of data at their fingertips: from starred reviews on business profiles to social media performance metrics, we are no longer in the dark about what our customers think, how they feel, or ultimately, how they tend to behave.

While amazing, that mass of data poses another massive challenge. With all this raw information coming in at a mile a minute, how can local marketers make sense of it for bigger moves and tactical deployments in their local marketing strategies?

Thankfully, marketing tech stacks exist to assist with analyzing data and inform next steps across the user journey, from how to target a “try us” campaign to setting up loyalty programs. While quantitative feedback like net promoter scores can be useful, qualitative feedback such as text-based insights offer a potential goldmine— if you can comb out the insights from all that unstructured data. That is why sentiment analysis affords one of the biggest and most useful tools in the marketer’s arsenal these days.

What is Sentiment Analysis?

At its most basic level, sentiment analysis looks at what people are saying about a subject—in this case, a business or a location—and works out whether they’re saying something good, bad, or indifferent.

More detailed, sentiment analysis examines the views or opinions of people who came into contact with something. It’s a way to determine the opinions, judgments, or emotions behind the “natural language”, or the words real people actually use, even in larger sample sets.

What are Sentiment Analysis Tools?

Sentiment analysis tools are software applications or platforms designed to analyze text data and determine the sentiment expressed within it. Specifically, they employ techniques such as natural language processing, machine learning, statistical analysis and lexicon-based analysis to classify the sentiment of text as positive, negative, or neutral.

How Do Sentiment Analysis Tools Work—In Theory

Modern technology makes digital sentiment analysis much more accessible. In fact, efficient sentiment analysis at scale emerged as a main driver of multi-location businesses adopting AI models, by harnessing machine learning techniques and using natural language processing.

Digital tools also save a human having to go through every single mention of your brand, parsing what the person behind the comment actually means to say, and categorizing them—a process that also tends to bring that person’s own biases to the categorization, too.

What Goes Into Sentiment Analysis?

With sentiment analysis, businesses can take all of that raw data from customers— all the reviews, social media mentions, feedback forms, surveys, emails, and so on—then run it through algorithms designed to spot patterns and extract information.

Specifically, sentiment analysis tools categorize text into three primary sentiment polarities:

Categorizing these manually would be time-consuming and virtually impossible for smaller marketing teams or local marketers. That’s where sentiment analysis tools come in, turning the unstructured data into useful insights at the click of a button, tracking incoming data in real-time, too, so you can spot emerging trends or issues early.

Behind the scenes, sentiment analysis either builds on:

Rules-Based Algorithms

Rules-based algorithms assign scores to words based on predefined rules or dictionaries. Each word is evaluated individually, and its sentiment score contributes to the overall sentiment of the text.

For example, words like "happy" or "love" might be assigned positive scores, while words like "sad" or "hate" might be assigned negative scores. The sentiment of the entire text is then calculated based on the aggregate scores of its constituent words.

Machine Learning Models

Machine learning models are trained on large datasets of natural language feedback, where the sentiment of the text is labeled by humans (e.g., positive, negative, neutral). The model learns to recognize patterns and associations between words and sentiments during the training process.

Once trained, the model can predict the sentiment of new, unseen text based on the patterns it has learned from the training data. These models often utilize techniques such as neural networks, support vector machines, or decision trees to make predictions about the sentiment of text data.

How Do Sentiment Analysis Tools Work—In Practice

Let’s look at two front-end examples of how location-based marketers can use sentiment analysis tools and how those work in situ.

Social Media Sentiment Analysis

Social media mining is one of the most accessible areas for discovering what people are saying about their experience with your brand. Users are generally off-the-cuff and less inhibited on social media, more likely to share their perceived experiences, both good and bad, or even neutral.

EXAMPLE: Imagine launching a new product and announcing it on social media platforms like Twitter, Facebook, and Instagram. Social media sentiment analysis tools would monitor the comments, mentions, and posts related to the product across these platforms.

Reviews Management Sentiment Analysis

Whether you’ve prompted a customer to leave a review as part of the sales journey, or they’ve taken it upon themselves, customer reviews can be used to identify gaps and pain points.

EXAMPLE: Consider receiving numerous online reviews on platforms like TripAdvisor, Yelp, or Google Reviews. Review management sentiment analysis tools help you analyze the sentiments expressed in these reviews to understand customer satisfaction levels.

Why Should Businesses Care About Sentiment Analysis?

Yes, people are out there talking about your business, your locations, your staff, right now. What they say matters: electronic word of mouth (eWOM) has become one of the strongest indicators of social proof for today’s businesses.

You might not always think of people forming an “emotional connection” to a brand, but every interaction does elicit an emotion of some kind. Marketers need to understand those emotions so they can know how their brand performs and whether they need to improve anything to continue growing their business. Customer feedback remains king, and sentiment analysis marketing empowers more effective data-driven decision-making.

The Future Horizons of Sentiment Analysis

Future trends and innovations in sentiment analysis are poised to revolutionize the way businesses understand and interact with customer feedback. One significant advancement is sentiment analysis for multimedia content, which extends beyond text to analyze images, videos, and audio clips.

Additionally, sentiment analysis is expanding to encompass multiple languages, facilitating cross-cultural understanding and global market insights.

Furthermore, advancements in machine learning and natural language processing techniques continue to enhance the accuracy and sophistication of sentiment analysis algorithms.

The Bottom Line: You Need Sentiment Analysis!

Sentiment analysis marketing helps businesses in gaining deeper insights into their customers, enhancing customer experiences, and bolstering their brand reputation.

By the way: did we mention that sentiment analysis is built into our location marketing platform? Gain full control over your reputation, and simplify reading, managing, and analyzing local reviews across the web from a single AI-powered platform.