Why Sentiment Matters

Sentiment shapes many of the decisions we make every day. Whether it’s reading product reviews, browsing social media comments, or listening to someone’s feedback, we rely on the opinions of others to guide us. But why do these feelings and opinions matter so much, and how can we understand them better?

What is Sentiment, Really?

Sentiment, at its core, is about feeling. The word itself traces back to Latin and French roots, originally meaning something personal, an emotional response. By the 18th century, people began using phrases like, “My sentiments exactly,” to express alignment with others’ feelings. Today, when we talk about sentiment, especially in the context of technology, we’re referring to understanding opinions—whether they’re positive, negative, or somewhere in between .

Sentiment analysis, the process of evaluating emotions and opinions in text, didn’t really take off until the early 2000s. Before that, the focus in research was elsewhere. But as people started leaving reviews online and sharing their thoughts in massive volumes, the need to analyze this data became essential. Now, sentiment analysis helps us gauge how people feel about products, services, or even entire brands .

Why Sentiment Matters to Us All

We are social beings by nature. It’s hardwired in us to care about what others think—about us, about things we buy, about shared experiences. It’s no surprise then that before making a purchase, 82% of us read reviews to understand how others feel about something we’re considering buying. We trust those reviews because they offer us a peek into what others experienced, helping us decide what’s worth our time, money, or energy .

For companies, this means customer feedback is more than just useful—it’s essential. Understanding how people feel about their products or services gives them direction. Is something making customers happy? Keep doing that. Are there frustrations bubbling up? Fix those. Sentiment analysis tools help bring quantifiable data from the treasure trove of free text data. Good tools help reveal common themes and a clear picture of what’s working and what’s not .

One interesting thing about sentiment is how quickly we pick up on it. Emotional content in reviews—whether it’s excitement, frustration, or disappointment - jumps out. Studies show that emotional reviews are easier for us to process. If a review is positive and emotionally charged, we’re more likely to trust it and feel confident in our choice. Negative reviews, on the other hand, make us stop and think harder, looking for the potential problems that might come with the product .

Mixed reviews, though, can be the most helpful. A blend of positive and negative feedback feels more balanced and credible. It doesn’t sugarcoat anything, which is what makes us trust it even more .

Going Deeper Than Just Positive or Negative

Basic sentiment analysis labels opinions as positive, negative, or neutral. But modern tools can go deeper. They can look at the specific things people mention in reviews, tying sentiment to those details. For example, instead of just saying a product is “good” or “bad,” these models can show what exactly people love or hate about it—whether it’s the design, the durability, or the customer service. For example a restaurant can discover that the sentiment about it's food is very positive, but the service is very bad. Legacy tools that only show simple positive vs negative miss out on this distinction.

We built this Sentiment Lab to explore how 12 different sentiment models perform on different datasets. To see which understand nuance and sarcasm - which are the closest to humans in evaluating sentiment. But then we enjoyed the process of comparing the models so much, we decided to release the laboratory for you to try on your own text and to see which models agree with you.

At the end of the day, sentiment matters because it’s about people — understanding how they feel about different things. By paying attention to sentiment, whether in reviews, social media, or feedback forms, we get to a deeper level of understanding of that collective opinion. This is especially useful when we analyze the text into themes and sub themes within a body of text, and once we've identified the themes, to then analyze sentiment shifts understanding. This is the next level of analysis we look forward to showing you in AddMaple.