parallax background

The Benefits of Human Insight for a Brand

Thursday 25th January

Blog Author Beth Perrin by Beth Perrin

parallax background

The Benefits of Human Insight for a Brand

Thursday 25th January 2024

Here at 3sixfive, we’re all about the human approach when it comes to customer service, marketing, social media management and more - and despite the undeniable growth of AI in recent years, we always recommend that brands and businesses steer clear of using bots and automation where possible. In fact, parcel delivery company DPD’s recent chatbot mishap is an excellent example of the drawbacks of using AI for customer relations. A frustrated customer was trying to track down a missing package but wasn’t receiving any helpful answers, so asked the bot to connect him with a real person instead. When this didn’t happen, the customer decided to play around with the bot’s capabilities, eventually managing to make it swear at him and even write a poem about how useless DPD is!

Human Insight, our unique sentiment analysis service, is a great way to incorporate the human touch into your marketing strategy. Unlike any other sentiment trackers available, Human Insight is carried out by real people, not bots, giving you a much more accurate picture of how your brand is perceived online. We’ll manually sort all of your social media comments and messages into positive, negative and neutral categories so you can see which areas of your business are doing well and which ones need work. Discover more benefits of this system below.


Slang Terms and Informal Language

A major drawback of computer-generated sentiment monitoring is its inability to understand slang terms and informal language, which are everywhere on social media. Many adjectives that are likely to be labelled negative by a bot are actually used commonly by younger generations to convey positive emotions and reactions.

“Sick” (awesome, cool)

“Snatched” (attractive, on point with their appearance)

“Fire” (cool, positive, exciting)


Similarly, acronyms and other modern internet terms can present problems for bots.

“The GOAT” (greatest of all time)

“No cap” (I’m not kidding/no joke)

“SMH” (shaking my head, disapproving)


What’s more, social media slang is constantly evolving and it can be very difficult for algorithms to keep up with the rapid changes in online vocabulary. Digital trend cycles are extremely short, sometimes lasting just a few weeks. The meanings of words can quickly switch from positive to negative and vice versa, resulting in complications and confusion for AI systems which have been trained in a binary manner. That’s why it’s much better to leave the task in the safe hands of a real human team.


Sarcasm, Irony & Exaggeration

Sarcastic language is another big problem faced by AI sentiment trackers. In sarcastic comments and messages, people express negativity by using positive words, which presents a challenge for computer algorithms that have been programmed to only view these words and phrases in a good way. Think of how many times you’ve probably exclaimed “Oh, great” when it begins to rain just as you’re about to leave the house, “That’s just brilliant” when your laptop battery dies halfway through an important project, or “Mmm, delicious” when removing last week’s expired leftovers from the fridge!

Numerical sarcasm is a particularly common type of sarcasm on social media, which as this report explains, is another prominent reason for AI errors in sentiment detection. Numerical sarcasm relates to phrases where numbers are used to convey a smaller or larger amount than implied, such as “I love how my phone takes 5 hours to charge!” and “Thanks for finally responding to me, it only took you 2 weeks!”.

Unlike bots, humans can use contextual clues and other conversational elements to figure out when someone is being sarcastic online, meaning they can categorise comments more effectively than automated systems. Our Human Insight experts are highly skilled in analysing different tones of voice and identifying cases where exaggeration is being used to convey the opposite emotion. This makes your sentiment reports 100% accurate.


Regional Dialects and Accent Phonetics

The UK is home to a vast range of different dialects and accents which greatly impact the ways in which people communicate online. This can cause further difficulties for AI sentiment trackers.


In Scotland, “greeting” can be used to mean “crying”.

In the North of England, “dead” can be used to mean “very” (“I’m dead excited”), “made up” can be used to mean pleased or delighted (“I’m absolutely made up about my promotion”) and “scran” means “food”.

In MLE (Multicultural London English/Urban English), “peng” means “attractive” or “looks good” and “gassed” means “excited”.

Plus, social media users often type their posts using the phonetics of their accent, such as Liverpudlians using “yer” instead of “your” and Scots using “deed” instead of “dead”.


These are just a few examples of regional traits which can be hard for computer algorithms to decipher - there are so many more area-specific language characteristics that can only be understood by real people with genuine conversational experience. AI sentiment trackers may end up sorting these types of comments into the wrong category, or might fail to register them at all.

If your brand has multiple locations across the country, it’s highly likely that some of your incoming messages will contain dialect variations, which can impact the results of your sentiment tracking efforts when using AI. In contrast, our Human Insight analysts have real-world experience and are able to translate accent features much more effectively than bots, helping build a more precise picture of your brand perception.


Industry-Specific Terms

Industry-specific language can also present challenges for bots. When it comes to hospitality brands in particular, we’ve seen culinary terms like “slow-cooked” or “slow-roasted” resulting in negative sentiment being wrongly identified. Without context, the word “slow” would usually be considered a bad thing in a restaurant setting, such as slow service, a slow booking system, or a slow response to a request. But when paired with “cooked” or “roasted”, we can see that the commenter is simply naming the type of dish they tried during their visit.


Spelling & Grammatical Errors

In contrast to brands and businesses, who should always double-check their spelling and grammar before publishing a post or responding to a message, the majority of casual social media users see their favourite platforms as informal spaces where they can communicate in a relaxed manner. People feel less pressured to spell every word correctly and don’t feel the need to write in the same refined style they use at work or school. This means that errors regularly appear and non-standard grammatical structures are frequently used.

Once again, this can prove difficult for AI-based sentiment monitors to comprehend. If a sentence hasn’t been constructed in the way a computer expects, it might struggle to work out what the person is trying to say, therefore rendering it unable to detect the sentiment. Our Human Insight team can decipher what a user is saying even if typos are present in their comments, making it easy for us to determine whether the sentiment is positive or negative.


Emojis and Other Symbols

Emojis are widely used on social media to express emotions, moods and ideas, but they can be hard for computer algorithms to understand - especially when used for sarcastic or comedic purposes. For example, the text portion of a comment may seem positive, but if the user has included a rolling eyes emoji (🙄) at the end, its meaning can completely change. Similarly, laughing emojis (😂 🤣) can of course imply happiness or joy, but are often used on social media to indicate irony or a mocking tone. Even the skull emoji (💀) is commonly used to convey humour (e.g. “dying of laughter”).

Real people with knowledge of current social media trends can use context to decode the underlying sentiment behind these emojis, whereas bots might make mistakes when labelling them, ultimately skewing the outcome of your analysis.



Overall, it’s clear to see that AI-based sentiment monitoring systems can’t achieve the same level of detail and accuracy as real people, resulting in unreliable data that won’t benefit your business. If you’re struggling to find out how your brand is really perceived online, get in touch with 3sixfive today to learn how our Human Insight service can transform your sentiment analysis results, enabling you to make truly effective business changes that will elevate your customer experience to new heights.