Trying to “read” people can be incredibly difficult. Honestly, it’s easier to decipher a cat’s mood, and they’re experts at the poker face. Even knowing what your partner wants to have for dinner tonight involves questions, second-guessing and total analysis of their body language. I mean, it’s either pizza or fried chicken, why does it need to be so NUANCED?
And this is without considering things like cultural differences, different lived experiences and even language barriers.
With the accelerated rise of AI and AI meeting assistants there are now new features being launched to help combat this. And for this particular problem we now have many companies offering sentiment analysis as part of their video call recording software.
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What is Sentiment Analysis?
Sentiment analysis, also known as opinion mining or emotion AI, is a feature that uses Natural Language Processing (NLP) to determine the emotional tone or attitude behind a series of words. The goal is to figure out if the text is having a good day, a bad day, or just meh – and in the deluxe version, to pinpoint emotions like ‘jumping for joy’, ‘ready to explode’, or ‘totally gobsmacked’.
This initially was used for the written word, but has since branched into the audio visual realm with call recording. With the rise of GPT-3, GPT-4 and other AI smarty-pants things, the machine learning has accelerated to a point where it’s now “feasible”.
How Does Sentiment Analysis Work in Video Calls?
For video calls, sentiment analysis has expanded beyond just interpreting text. AI now processes visual cues like facial expressions, body language, and voice modulation to gauge the sentiment of the participants. It’s essentially having a tool that can, in real time, alert you if the person on the other end of a business call is confused, agitated, or pleased based on their non-verbal cues. Just to give you an idea, on average, 93% of our overall communication is non-verbal.
The Benefits of Sentiment Analysis Software
The digital age, driven by data, is always on the hunt for tools that can provide deeper insights into human behavior. So any tool that offers sentiment analysis stands out, promising a better understanding of the emotional dynamics during video calls. And it does have SOME benefits:
The Promise of Enhanced Customer Insights: Sentiment analysis is tipped to provide a window into the unspoken emotions of customers, offering companies a chance to tailor their communication more effectively.
“Objective” Feedback: This tool is offered to ensure unbiased, objective feedback, a boon in a world where opinions can be swayed easily.
Allows For Real-time Adjustments for Sales Strategies: By gauging real-time reactions, sales pitches can be modified on the go, maximizing effectiveness.
All in all, it sounds pretty good… right?
Problem #1. The Role of Bias In Sentiment Analysis
All tools, no matter how sparkly and impressive they seem, were originally cobbled together by us humans – and trust me, we’re far from perfect. Even whizzy algorithms have some person, right at the beginning that would have tried their best to not let their own life cloud their work.
But it’s impossible.
Bias is an inevitability.
And when it comes to sentiment analysis, this bias can seep in, affecting the very results the tool is designed to produce. How the AI interprets a facial expression, the tone of voice, or even a particular phrase can be shaped by the biases of those who developed it.
Problem #2. Same Language, Different Meanings
While English is a common language across the UK, USA, Canada, Australia, and New Zealand, cultural and historical differences have led to unique linguistic nuances in each region.
The Misunderstood Compliment
Take the phrase, ‘You’re quite good at this.’ Sounds simple, right? But it’s like a chameleon; it changes its colors depending on where you are.
For example, in British English, the word “quite” often diminishes the adjective it modifies, meaning the person is good but perhaps not exceptionally so. It would even be interpreted as an insult.
However, in American English, the same phrase would typically be seen as more complimentary, implying the person is notably skilled.
An Australian might interpret it more neutrally without diminishing nuance, whereas a New Zealander might look for contextual cues like tone or body language to interpret the sentiment.
A Canadian, valuing politeness, might see it as a gentle affirmation but would also be attuned to the possible understatement.
Equally, someone with English as as second language would again entirely depend on the “type” of English they learned, the nationality of the teachers, etc.
Such subtle differences pose challenges for sentiment analysis tools. Without a deep understanding of regional linguistic nuances, these tools might score the same sentence differently depending on the cultural context. This could lead to misaligned sales strategies or misinterpreted feedback in global markets.
And this is just one phrase, in one language 😱
Problem #3. The Challenges Extend Beyond Linguistics
It doesn’t just stop at language nuances. Consider non-verbal cues. In many Asian cultures, direct eye contact can sometimes be perceived as impolite or aggressive, especially when interacting with someone of a higher status or older age. However, in Western cultures, direct eye contact is often interpreted as confidence or honesty.
If sentiment analysis doesn’t factor in these cultural nuances, it could mistakenly classify someone as disengaged or inattentive when they’re merely being respectful.
Add to this that things are evolving constantly. What is deemed “offensive” for an older generation, is totally normal for the younger generation. I mean, just look at Gen-Z’s opinion on the period mark in text messages.
And also, let’s talk about tone. The upward inflexion at the end of a sentence, commonly associated with the ‘Valley girl’ accent in the U.S., often indicates a question or uncertainty. But in Australia, this ‘high rising terminal’ is common in everyday speech and doesn’t necessarily convey doubt. An AI tool could misconstrue an Australian’s confidence for uncertainty based on this tonal pattern alone.
Listening Is Why Humans Still Reign Supreme
Technology is advancing, rocketing ahead, in fact. But when it comes to understanding why we cry at puppy videos or laugh at the most random things, there are still some gaps. and there is a question over whether technology will ever truly be able to “understand” them. Algorithms, no matter how sophisticated, still fail to grasp the intricate tapestry of human experience fully.
Factors like past scares, individual life stories, and unique contexts can really impact a person’s sentiments in ways that can’t be digitally codified. Human intuition and senses, honed over millennia, can catch these nuances. Thus, while algorithms can guide, they can’t replace the profound depth of human understanding.
There is also the risk of over-relying on technological tools. This poses the danger of eroding essential human skills over time, much like many crafts and techniques have been lost due to mass production.
In an area such as sales, where the emotional connection often seals the deal, becoming too dependent on automated insights might make one lose touch with genuine customer interactions. Active listening, a cornerstone of effective sales, is more than just “hearing” words—it’s about understanding, empathy, and connection. This skill is irreplaceable, and over-dependence on sentiment analysis might dull it.
While sentiment analysis in sales tools boasts remarkable capabilities, there remain substantial nuances that only the human touch can decipher. Yet, the undeniable benefit of video call analytics and recording cannot be ignored.
tl;dv stands out in the crowded landscape of video call tools, not as a replacement to human intuition but as a complementary aid. The focus with the tool isn’t just on understanding the sentiment but on analyzing the broader dynamics of a video call. By capturing essential details, summarizing conversations, and providing actionable analytics, tl;dv empowers users with data-driven insights while leaving room for that all-important human judgment and touch.
Why tl;dv is Different
Many tools with sentiment analysis take a one-size-fits-all approach, but tl;dv recognizes the limitations of such an approach.
Our tool focuses on video call itself, offering more value than sentiment analysis. Users can share wider to gauge calls, note key discussion points, and evaluate the effectiveness of their communication strategies. There is AI but it’s used to create factual summaries and offer rock solid facts rather than “interpretations”. This broad approach ensures that users get a comprehensive understanding of their calls, rather than a singular, and possibly misleading, sentiment score.
Empowering, Not Replacing Human Skills
Where tl;dv truly shines is its emphasis on being an aid rather than a replacement. It doesn’t pretend to have all the answers but provides a foundation upon which professionals can build.
Salespeople can combine the insights from tl;dv with their intuition, experience, and cultural nuances knowledge to effectively tailor their strategies.
User research teams can watch back without a computer program telling them what someone “is thinking” and using their own skills and team work to deduce what is right.
This approach acknowledges the value of technology while championing the irreplaceable nature of human connection. By emphasizing listening, understanding, and genuine interaction, tl;dv showcases a future where technology and humanity coexist harmoniously.
Video Call Dynamics: More than Just Emotion
The beauty of video calls is their multidimensional nature. Beyond words and emotions, video calls capture visual cues, engagement levels, and even the environment in which the conversation takes place. When pieced together, these elements can paint a fuller picture of a call’s dynamics. This is where tools like tl;dv bring significant value. It offers a holistic view of the conversation by emphasizing video call analytics over pure sentiment analysis.
An Intelligent Archive
One of the standout features of tl;dv is its capability to create an intelligent archive of video calls. Users don’t have to rely solely on memory or sparse notes. Instead, they have a treasure trove of recorded calls, marked with essential details and summaries. This video call repository becomes immensely useful for training, reflection, or even dispute resolution.
Integration and Collaboration
The modern workspace thrives on collaboration. The ability of tl;dv to seamlessly integrate with other tools and platforms means that insights from the tool can be quickly shared and acted upon. Be it a CRM system where sales strategies are tweaked based on video call and transcripts that are automatically pulled through, or a project management tool where discussion points become actionable tasks; tl;dv promotes a culture of collaboration.
Sentiment Analysis Tools Won’t Change The World
While sentiment analysis tools present a promising frontier in understanding human emotions during conversations, they are not without flaws. Cultural nuances, individual experiences, and biases can sometimes make these tools less reliable. However, tools like tl;dv offer hope. By focusing on the broader dynamics of video calls and emphasizing the importance of human judgment, they provide a more rounded, adaptable, and valuable solution for professionals across industries.
While we’re not saying that sentiment analysis won’t become the standard in years to come, we really do want to emphasize here that technology is here to support not overtake. If you fancy testing the waters, then why not take tl;dv for a FREE spin today. Just click the buttons below and we’ll let you make your own assumptions (no sentiment analysis being all judgey on our end, haha!)
