What Is Social Sentiment Analysis and Why Is It Important?

Sergio Oliveri |
 02/22/23 |
21 min read


Since the emergence of social media, access to consumer opinions and sentiments has become considerably more manageable. And it’s more critical than ever to measure social sentiment, as it changes frequently and sometimes overnight. Sentiment analysis should be a part of your market research and social analytics—we’ll examine why and how it works.

Since the emergence of social media, access to consumer opinions and sentiments has become considerably more manageable. And it’s more critical than ever to measure social sentiment, as it changes frequently and sometimes overnight. Sentiment analysis should be a part of your market research and social analytics—we’ll examine why and how it works.

There’s a lot for us to explore, specifically how sentiment analysis can help you. We’ll cover the following:

Table  of Contents:

Let’s dive in!

What is Sentiment Analysis?

Sentiment analysis is an artificial intelligence (AI)-based capability that uses machine learning to recognize sentiments and assess the emotional content of texts and images. It is a layer of understanding applied to the rest of your market research and social analytics that puts data analytics into context. And it offers meaningful insights to drive everything from product innovation and packaging to messaging and maintaining a competitive edge. It categorizes consumer emotions and overarching market movements by type and intensity.

More specifically, sentiment analysis-based research is a neural network and is how we try to understand people’s thoughts and feelings based on what they write, photograph, or say about some particular thing (e.g., products, people, companies, and organizations). More generally, and with basic sentiment analysis when we analyze sentiment, it covers abstract emotional states.

But this analysis is not limited to understanding feelings or opinions (e.g., “this phone sucks”).

We’re also interested in objective facts (e.g., “the battery never charges properly”).

And really, any data that reports on the benefits or downsides of a product is relevant, including negative news articles about a company, as we can infer that people will have a negative sentiment about it.

Social listeningsocial monitoring,image analytics, and customer experience analytics – all of these rely on valuable and accurate sentiment analysis. So, it isn’t something that stands apart from the rest of your research; it completes your analytics package.

But sometimes, explaining sentiment analysis (and the above) is best done through images!

For example, a simple NetBase Quid® search on the term “gaming and eSports” provides a snapshot of overall sentiment that tells you there’s much conversation about this favorite pastime. As of this writing, our sentiment analysis model revealed 6,070,209 mentions of our topic with potential reach off the charts.

social media summary

But what do these numbers tell us? Not much of anything. We can see that there are both positive mentions (green) and negative (red) mentions, with the positive outweighing the negative. But we still don’t know what’s behind the sentiments.

Without a sentiment analysis system, you’ll never know what you’re doing right or wrong. More specifically – you’ll never understand why it’s being perceived as right or wrong. You can only assume. And those assumptions are often incorrect.

On the other hand, with sentiment analysis, you have a ton of clues to explore further and gain an  in-depth understanding of where your brand is doing well and where it needs to rethink messaging:

gaming word cloud

The sentiment expressed with positive words like “ultimate #web3 gaming experience gives you a hint at what people respond positively to, while negative words like “disgusting display,” “poor gaming performance,” and “broken” clue you into some negative consumer feelings.

And how does sentiment analysis work? We have details! Let’s start by discussing the various kinds of sentiment analysis.

The Different Types of Sentiment Analysis

There are different types of sentiment analysis:

  • Machine learning-based sentiment analysis: A machine learning algorithm learns from the data as it goes. It requires and is dependent upon its training and builds upon itself. If it becomes baked into the sentiment analysis algorithm, it learns something incorrectly. Machine learning models are often trained on very questionable datasets that typically contain little to no social channel data, making them an ill-advised option for understanding sentiment. Although it has made great strides in handling low-level tasks, when it comes to an understanding the meaningof human language, there are still many problems with machine learning-based sentiment analysis and many ways in which it is inferior to rule-based systems.
  • Rule-based sentiment analysis: This sentiment analysis model consists of manually created rules that count things up for an aggregated score. It sounds simplistic, and it is, but it’s also effective (to a degree). But it has limitations: “The result of this approach is a set of rules based on which the text is labeled as neutral, positive and negative words. These rules are also known as lexicons. Hence, the Rule-based approach is also called the Lexicon-based approach.”
  • Aspect-based sentiment analysis: This categorizes data by aspect and identifies the sentiment attributed to each. It associates specific sentiments with different aspects shared. For example, ”I want to go swimming so much.” It is clear that the person has positive feelings about swimming, as evidenced by their desire to frequent the location. Sentiment analysis identifies positive sentiment on the part of the subject towards swimming. We will discuss aspect-based sentiment analysis below and as we go, so keep reading!

Which method sounds most accurate to you?

One of the advantages of the NetBase Quid® sentiment system is our consistent criteria and focus on linguistically detailed “aspect-based” sentiment analysis. Many of our competitors and open-source sentiment systems cannot provide this level of detail because they are focused on labeling sentences or documents.

Oversimplified assumptions about the nature of ‘sentiment,’ such as “positive” and “negative,” lead to incorrect analyses.

Historically, sentiment analysis tools have relied on some pretty shaky assumptions –frankly, many still do. Even the idea that a document can, in total, be summed up as “positive,” “negative,” or “neutral” is quite suspect.

If only sentiment were as simple as “positive” or “negative” words. But it is not. Sentiment – like human emotion – is a wide-ranging spectrum of varying intensity. And intensity matters, especially when it comes to social monitoring.

When we calculate Brand Passion, we use a combination of Net Sentiment (a measure of positivity or negativity, from -100 to 100) and Passion Intensity (the strength of those emotions, from -100 to 100).


This categorizes whether consumers like a brand, are obsessed with it, or are just neutral. And this gives brands actionable data.

Sentiment analysis is about the details and where and when to focus your energy.

People who like your brand may not need immediate attention. However, if you can activate the consumers obsessed with your brand, they’ll help convert the fence sitters with their own enthusiasm.

But it’s not just those who love you that you need to locate; it’s also those who may despise your brand. For this, you want to focus on what is driving their displeasure and approach it head-on.

And then there are those pesky neutral emotions; they shouldn’t be ignored.

For these, you need to discover whether they don’t care or maybe something rubbed them the wrong way. Having social analytics connects you with them to discover the why and it could mean welcoming more consumers to your team.

The right social listening tool will help you discover things that move them, allowing you to effectively target them with individual messaging that speaks to their passions. It’s not about your brand – it’s about them.

Why Does Sentiment Analysis Matter?

Friendster, MySpace, and even Facebook were the early frontiers of social media platforms before their marketing power was fully realized. Its simple purpose back then was to stay in touch with family or friends.

Today, it’s a maze of consumer opinions that other consumers look to for guidance on which products to buy – or to avoid. Consumer opinions have a lot of power and the only way to thrive in such an environment is to understand precisely what’s driving consumer emotion and opinion.

This puts a small amount of power back in the hands of companies to a degree, as with this information, you can solve problems, correct misconceptions, provide desired products and services, and interact with consumers on their terms.

Below are five ways a sentiment analysis system can work for you:

Better Audience Understanding

Remember that your mentions—whether positive, negative, or neutral—don’t occur in a vacuum. Instead of concentrating on a single compliment or complaint, brands should consider the overall sentiment of their audience. Every brand loves a barrage of compliments, which can help you understand what you’re doing right—like Taco Bell’s Mexican Pizza…This consumer loved it so much that they created a painting of it which received 100% positive sentiment (And it’s since become a meme):

taco bell mexican pizza

Conversely, a deluge of complaints may indicate issues with your product, messaging, or service that require your attention.

Provides Actionable Insights

Consumers love to tag and talk about brands online, making a social media sentiment analysis ideal due to the wealth of information available. You can get accurate data points demonstrating how your brand performs over time and across all platforms.

Meet your consumers where they are

Because consumers are talking online, brands are given the distinct opportunity to meet them where they are and with precisely what they desire. You can engage directly with your audience, answer their questions, or even respond to criticism promptly. Your brand’s health is protected by solving customers’ problems or not letting a consumer complaint fester.

More profound insights about your brand, its messaging, and more

You can modify your brand messaging for a more significant impact when you account for how users interact with your brand and the type of content they value. A sentiment analysis system allows you to see what consumers love and hate.

Understand How You Measure Up in Your Industry

Brands can’t be everything to everyone. You can use social sentiment to gauge your position in your industry. Your ability to communicate effectively with the appropriate audiences at the proper times will benefit from this.

Without the information provided by sentiment analysis, you are sitting in a canoe without oars. But how does it actually work? We’ll jump into that next!

How Does Sentiment Analysis Work?

The way sentiment analysis works can vary from tool to tool. We employ Natural Language Processing (NLP) to identify word tokens (e.g., “delicious,” “pie,” etc.), parts of speech (noun, adjective, verb, etc.,.) and lemmas (e.,g. ‘pie’ is the lemma – or basic form – for the word “pies”). We also identify terms that are names, even when they consist of multiple terms (e,.g. ‘New York Stock Exchange’).

The critical elements of our sentiment analysis are morphosyntactic structure and semantic features, along with lexical knowledge of a language. We use grammatical analysis to combine words into syntactic constituents and assign semantic features to these grammatical units.

Here’s a sentence to help illustrate it: I want to go to IKEA bad.

We would identify positive sentiment on the part of the subject towards IKEA, namely the desire to visit the place and presumably buy their products. We also recognize the emotional state of ‘desire’ in this sentence. The expression “so bad” is recognized as an intensifying phrase that conveys no negative sentiment lexicon.

And all of these semantic roles in the sentence – Agent (‘I’), Action (“go”), Emotion (“Desire”), Object (“IKEA”) are extracted into our index where NetBase Quid® allows users to capture an in-depth understanding of what people are saying about brands, products, politicians, movies, tv shows, or any number of topics.

Many of our competitors do not provide this in-depth level of text analysis. For each sentence, or even for entire documents, they simply output a label: ‘positive,’ ‘negative,’ or perhaps ‘neutral.’ Some also present a score alongside or instead of a label on a range from strongly negative to strongly positive (e.g.,-100 to +100)

And most important: They often provide little detail about the ‘source’ of these positive or negative sentiments, hiding sentiment results that track back to individual posts. Instead, they offer a summary score across an entire group of results.

None of this is conducive to understanding the details that give rise to the positive and negative opinions found in online content. And transparency around this intel is crucial, as it validates an accurate analysis. Without it, the results of your text analysis are unsubstantiated and make its reliability questionable.

Sentiment Analysis Use Cases

Human language is complex, so your social listening tool needs to break it down to identify emotional terms. Thankfully, as mentioned above, our sentiment analysis uses Natural Language Processing (NLP), which can do precisely that, and isn’t limited to English versus French versus Cantonese, etc. A natural language toolkit can identify slang and pop culture terms, as well as emojis, and even images.

And Image analysis is no less critical than the rest. It can be even more so in our increasingly visual online world. With great frequency, images stand in for textual data. And as this trend continues to build, the value of analyzing sentiment in images increases correspondingly.

A picture is worth a thousand words, and without text data, it offers no real insights unless you have image analysis to recognize your brand’s logo (for example). Today, it’s critical to have that post about your brand – and all like it – counted.

And there are numerous ways to apply sentiment data once it’s in hand:

Measure Brand Health

Think of sentiment analysis like an EKG, which shows you peaks and valleys of emotion indicative of anomalies in overall brand health.Social lets you investigate what’s behind them and act accordingly, keeping your brand on an even keel. As an example, below our timeline comparison, is set to show sentiment around a popular beverage brand, Coca-Cola.

coca cola net sentiment

And just as in medicine, you can explore what’s causing a blip in the radar. We can investigate this summary above by clicking anywhere on the chart. Selecting the peak on February 11th brings us to this tweet about Coca-Cola’s and their big step into the cell phone game:

fluctuation summary

With 85% positive sentiment, Coca—Cola knows what they’re doing. But if this had been negative sentiment, you can bet they’d be on it. They haven’t become leaders in their industry by sitting back and letting things happen-they’re proactive and savvy.

And if you hope to keep your brand healthy, sentiment analysis can give you more clues to who your audience is as well

Find Your Audience

Sentiment analysis can only improve whatever you think you know about your audience. Perhaps you’re Amazon and want to nail down more specifics about your consumers to campaign effectively. Using a competitive intelligence tool such as Quid Social can draw out demographics such as gender and which particular aspects of your company they are more interested in.

Our bar chart is filtered to show only male interest and is colored according to sentiment. For example, we find that Amazon Kits is their most considerable interest and overall, they look favorably on this aspect of Amazon. Coming in second is Food and Books. There are some mixed sentiments, negative, and a sizable neutral sentiment spread throughout these groups –  Amazon may want to explore further.

amazon discussion filtered by gender

In contrast, when we look at Amazon’s Female audience, we can see that the order of their interests changes. Amazon Kits still come in first, with the same sentiments shown as in our Male audience. However, instead of Prime Video being their secondary interest, Food slides into second, with Books closing in as third. Once again negative, mixed, and neutral sentiments can be seen—and should never be ignored:

amazon discussion filtered by female gender

You need to approach the way you market to each of these segments differently.

Brands do this by using social analytics to monitor targeted sentiment, revealing common ground amongst members of their audience. This allows them to speak personally to each of them, at scale, while delivering the experience they want.

And one way to accomplish that is with the help of influencers.

Identify Influencers

Consumers trust other consumers more than they trust brands and marketers. And this is where social influencer marketing can come into play for your brand. It’s where passion intensity truly reveals itself.

Locating social consumers who share their love for your brand on social media and that have a devoted following can help boost your brand’s messaging.

Whether you pay them to speak on your behalf, or simply engage them with a public thank you, it’s essential to know who they are, as they hold sway over your consumers.

And NetBase News can take it one step further and separate news influencers from social influencers. This offers perspective around the differences in how the news talks about your brand versus how people actually see you.

top influencers by mentions

And the potential reach of some of these influencers rivals any news media distribution. That’s a reach any brand would love.

Reveal Emerging Trends

Trends come and go, and just because there’s a trend doesn’t mean you should automatically seek to leverage it.

Sentiment analysis with the aid of social media monitoring helps you determine how invested your specific audience is in any trends that come along. And what, exactly, they feel about said trends too.

sentiment wheel

In the past couple of years, we’ve seen a trend toward being more eco-conscious. Terms like reuse, sustainability, and upcycle have become a part of our daily vocabulary, and they have asserted themselves in consumer shopping behaviors. Will this trend toward sustainability continue? Will trends in your vertical shift?

Only your social data will tell you. Our sentiment wheel around sustainability shows large amounts of positive sentiment, a good indicator that it’s still a big hit.

However, it’s not the complete picture and would need further social analysis around what kind of topics are resonating – and with whom. Psychographic insight (capturing values, opinions, attitudes, interests, and lifestyles) complements demographic intel here

And there could also be regional differences to be aware of (there usually are)! And all of this should be explored before planting your flag in a trend and calling it ‘the next big thing.’

New Products/Services Ideation & Feedback

The ability to get honest customer feedback so quickly is something we take for granted with social channels, yet it’s one of the most significant attributes of sentiment analysis.

One of the best uses of this is before launching new products or marketing campaigns – to be sure your audience even wants what you’re offering.

Additionally, sometimes when brand monitoring, companies can capture intel from a post that sparks an idea for something consumers crave and can’t find—highlighting that monitoring your consumers is critical in getting not only your current endeavors right, but your future ones as well.

And then once you know that you’re making something that consumers want, monitoring online conversations to get continuing customer feedback is a must. And it’s fantastic when you discover that your hard work and efforts are paying off:

reddit clothing conversation

And if something does go wrong, you’ll be prepared to resolve those issues…

Anticipate and Resolve Problems

If there’s one thing consumers do well, it’s talk about the brands they love (as our example above illustrates) – and those they do not. Social platforms have become the sounding board for all disgruntled customers to vent.

Sentiment analysis helps you catch these negative sentiments and determine whether the intensity of emotion is headed into the danger zone. Savvy brands have learned to automate business processes and set alerts for damaging keywords, so they know immediately if something is about to spiral out of control.

But if you use social media analysis to keep an eye on sentiment and resolve negative issues before things get to that point, that’s even better.

You only need to look back in history to see that brands sometimes miss their mark. An older but perfect example of this is Kraft Mac and Cheese’s “Send Noods” campaign. They thought it would be a funny campaign. However, a significant segment of their consumers were not impressed:

kraft-send-noods-campaign-crashKraft could have been more careful; however, they quickly realized their mistake and smoothed things out. And that’s the mark of a good brand—one that can immediately recognize when they misstep and correct the issue. Through careful monitoring, they were able to redirect.

The other side of not monitoring for shifts in customer sentiment is that you open the door for competitors to step in and gain the attention of your customers.

Assess Competitors

We discussed the importance of how you’re doing online, but that’s only part of the equation. Social media is an open book (for the most part), so applying sentiment analysis to your competitors as well makes sense. After all, you share an audience, so it makes sense to know what consumers love and hate about other brands in your category.

You’ll save a ton of time and money by letting other brands do your research for you and make mistakes, so you don’t have to.

Challenges when Performing Sentiment Analysis

Language isn’t always easy to navigate—slang, acronyms, and emojis are more challenging examples of how difficult it can be to navigate human language, let alone read the sentiment behind it. Additionally, there are words, and terms are popping up all the time. Here are a few sentiment analysis challenges you may face if your tool isn’t up to par!

Contextual errors

It’s happened to us all—we read a text or social posts and we take what’s being said out of context because we’re unable to understand the emotional tone or decide whether the person is being sarcastic. Context is key when it comes to sentiment analysis. Your tool should be able to read:

  • Sarcasm: Sarcasm typically conveys negative emotions. But a basic sentiment analysis could identify sarcasm as positive sentiment. For example, “I am so delighted that I have to get a root canal!” These words may be identified as favorable when it is actually sarcastic and quite negative.
  • Polarity: The emotional undertone in some social posts can be very obvious (for example, “It was a terrible experience. “However, it can sometimes be difficult to categorize the emotional undertone in others as positive, negative, or neutral. For example, “The quality of service was okay.” As a result, the algorithms sometimes have trouble determining tone.
  • Polysemy: One word can have multiple meanings, such as nail, bark, or season. And some sentences could be understood differently, such as, “The bat flew across the yard.” Are we talking about the animal or a baseball bat? If the word isn’t analyzed in its context, the sentiment analysis results could be skewed.

Negation Detection

Negation in a sentence (e.g., no, not, -non, -less, -dis) does not always imply that the statement’s overall tone is negative. For instance, the phrase “That was not unpleasant” has negation and could be categorized as negative, but it actually may convey a positive connotation. Having sentiment analysis tools that can decipher these subtle nuances is critical.

Inefficient Languages Detection

Even though English is the most widely spoken language, as businesses expand, they interact with customers around the world. Customers use various languages, and sometimes sentiment analysis tools are only able to pull sentiments in one language—which doesn’t give you a good grasp on what your brand sentiment is globally. This is a serious issue when you’re looking for customer feedback in a language other than English. So having a multilingual sentiment analysis is a must.


Today, people will just as easily use an emoji to show they’re sick, then type out, “I’m feeling unwell.” And because emojis have evolved to become their own language, if your sentiment analysis datasets can’t read or don’t offer tracking for these modern-day hieroglyphics, your analysis will not be complete.

Potential Biases

If your analysis is based on human researchers, your results will be biased. In contrast, AI-based analysis doesn’t exhibit bias. But not all AI is created equally, so do your research before committing to a social sentiment tool.

So, with all these potential issues, what’s the answer?

A sentiment analysis with Advanced AI which uses Natural Language Processing (NLP) algorithms and machine learning techniques.

Top-tier AI that can categorize text can distinguish similar words and sentences that convey different things from context. This is crucial to understand how customers feel about your product on social channels such as Facebook, TikTok, or Twitter.

Advanced sentiment analysis and strong classification abilities are offered by NetBase Quid’s ® NLP, which includes the following:

  • High degree of accuracy when reading and interpreting the significance of consumer opinions on social media.
  • Analyzes and provides information depending on every variation of a search query that is entered as it occurs, in more than forty different languages.
  • Uses visual processing to extract information and sentiment from misspellings, sarcasm, emoticons, and brand logos. Examples comprise Slang or “urban terms,” like “My new phone is sick!”
  • Other spellings, such as “love,” “kewl,” or “gr8.”
  • Acronyms such as “TBH” or “LOL.”
  • Typical typos include “the/teh.”

For brands adopting analytics solutions that include sentiment analysis, this degree of precision is crucial. Working with an incomplete dataset means you’re missing out on blogs or social media posts that contribute to the conversation but have one or more of the aforementioned issues.


What is meant by sentiment analysis?

Sentiment analysis, often known as opinion mining, is a tool that uses deep learning and machine learning techniques used in natural language processing (NLP) to determine the emotional undertone of text. Sentiment analysis lexicons include identifying words such as neutral, negative, and positive to categorize posts. In simpler terms, the basic concepts of sentiment analysis are to provide customer feedback and understand your audience better.

This is a common method used by companies to identify and classify opinions about a certain good, service, or subject.  These opinions are pulled from social sites, online reviews, survey responses, forums, news articles, blogs, research papers, and more.

What is an example of sentiment?

Sentiment can refer to a feeling as well as a point of view. In a social post, the sentence, “I love my new gloves from X brand!” would be viewed as positive sentiment. Whereas a post saying, “X brand’s popcorn is the worst ever” would be categorized as negative sentiment.

What are some questions to ask before doing a sentiment analysis?

This will vary depending on what you want to know. Often, brands are looking for feedback on existing products, campaigns, and services. But a sentiment analysis can also be used to uncover sentiment behind trending topics to inform innovation. And it can be used to help you understand your competitors better. It can even answer very specific questions such as, “How do consumers feel about the new feature of X product?” Taking the time to compile a list of questions will keep you organized and help you understand your audience more effectively. 

What are examples of questions to compile for sentiment analysis?

Common questions that sentiment analysis can provide answers to are:

  • What aspects of our goods and services do buyers find appealing?
  • What aspects of our products and services do customers dislike?
  • Are we getting too many complaints lately?
  • Has the volume of negative sentiment grown over time?
  • Which product has received the most positive comments?
  • Has the proportion of good, neutral, and negative comments stayed the same since the previous quarter?
  • Has the level of positive or negative sentiment shifted?
  • How does our brand sentiment compare to our competitors?

Tips and Summary for Performing a Sentiment Analysis

We’ve covered a lot of ground. So, here’s a summary of top tips—or must-dos—that every brand should be doing when they asses their sentiment.


Benchmarking is critical to building and maintaining a healthy brand. Defining measurable goals and objectives for your brand is crucial for long-term sustainability. If you didn’t do this, it may be challenging to report back to the C-suite on how things are progressing. And just like any other metric, it’s crucial to benchmark sentiment as well.

Below we have a top eyewear brand and their sentiment over the past month, divided into weeks. Their sentiment has been pretty steady over the past month—but there is a lot of neutral sentiment to be seen. This is a possible opportunity for them to delve deeper and see how they can get these fence-sitters to go green. What is the issue that’s holding them back? Is it a product, or lack of one? Or is it general customer satisfaction? Exploring more will provide the answers.

timeline view of sentiment

This is also a good time to establish where your ideal sentiment scores should sit. For some brands, anything above 15 may be acceptable (a sentiment classification of positivity or negativity, from -100 to 100) —but most want to see it sit more comfortably above 50. By having an agreed-upon sentiment score, you can set an alert that will inform you when your sentiment scores dip below a certain number.

Understand Sentiment Shifts and Adapt

If the past few years have taught us anything, it’s to adapt and overcome. Your newest campaign may have seemed like a great idea, and the data may have supported it as well. But then overnight, consumer opinion changed. You’re doing it wrong if you wait until a campaign has ended before evaluating its results—imagine if brands had done that during the pandemic (and some did and sadly are no more)! Will you be left in the dust? No, you’re too savvy for that! With the use of real-time dashboards, you can monitor progress right away and make necessary adjustments in response to new sentiment shifts and secure your seat on top.

Recognize and Resolve Potential Issues

And speaking of sentiment shifts, if sentiments dip, you need to be able to dig beneath and see why it happened. Sentiment analysis tools that don’t pull back the curtains on negative sentiments to reveal their drivers are useless. How else are you to know what caused this shift? A proper tool can dissect the conversation in many different ways and help you see if it’s related to a product, your messaging, or something completely different.

For example, we used the filters “brand issues” and “brand attributes” to help us dissect specific topics of this top eyewear company. Our sentiment model below reveals how consumers feel about advertising, customer care, sustainability efforts, and their authenticity:

crosstab showing sentiment

Obviously, their sentiment scores are pretty good—though sentiment for Customer Care and Quality could stand to sit a little higher, for comfort’s sake. But suppose one of these were glaring red with a low sentiment score, after clicking through to the individual posts behind it, this brand would immediately know where they need to focus their attention

Once you know what the issue is, you can pivot quickly to avoid a crisis, or meet it head-on and diffuse a volatile situation.

Compare your competition’s efforts against your own

You can always do better, and a brand that becomes complacent is a brand that will quickly fail—there’s always someone else to take your place. Keeping a healthy eye on the competition and their brand sentiment is a good way to judge if you could do better. If you see that there’s more green on their side than on yours, it may be worth investigating what they’re doing to gain such admirable affection. Likewise, if their sentiment is in the dirt, you may want to see what went wrong so you don’t repeat their mistake.

Communicate with C-Suite

It’s important to have metrics that can clearly convey what you need them to. And it’s especially critical when relaying data to busy executives. A sentiment analysis tool needs more than red/green indicators if you’re going to properly communicate why sentiment is low. So, ensure that you have detailed and easy-to-read metrics to present C-Suite.

And if you don’t, this may be the time to make your case…

Making the Case to Stakeholders

With so many social listening tools out there, decision-makers may be motivated to cheat and get by with lesser tools. Budgets are real, but don’t let the bottom line keep you from researching your options, as a shoddy intel capture is worthless so you’re not actually saving money – you’re throwing it away.

Analytics for social media  is an investment worth making – especially when that insight includes sentiment analysisimage analysis, and customer experience analysis

If you purchase tools that integrate with other systems, like your in-house CRM, you’ll not only gain insights that offer more accurate and highly relevant data quickly, but you will finally put all of that previously unused intelligence gathering to work! This is something your own data scientists will be excited about. Not to mention, that’s an ROI most brands can experience immediately.

And if you’re still unsure, look at the top brands using social analytics effectively, and follow their lead.

There isn’t much time for trial and error as news spreads fast on social media, so you want consumer intelligence and market research data you can trust straight out of the gate. Sentiment analysis provides that – and offers solutions that stretch beyond the marketing department to benefit your entire organization and increase customer lifetime. Talk about something to love! If you need your own sentiment analysis solution to help you gauge brand reputation and take control of your brand health, reach out for a demo today!

Premier social media analytics platform

Expand your social platform with LexisNexis news media

Power of social analytics for your entire team

Media analytics and market intelligence platform

Enrich your media analytics with social data

Social media benchmarking
and competitive intelligence

Data streams & custom KPIs for advanced data science

AI, Image Analytics, Reporting Tools & more

Out-of-the-box integration with other data sources