Sentiment analysis is the process of analysing text to determine its emotional tone—whether positive, negative, or neutral. With the vast volume of online conversations, businesses increasingly rely on this technique to understand customer/public perceptions and enhance decision-making.
Why is sentiment analysis essential?
As customer conversations continue to expand across digital platforms like social media, review sites, and forums, businesses need effective ways to extract insights from unstructured feedback. Sentiment analysis enables real-time tracking of public perception, helping brands monitor trends, assess reputation, and respond proactively.
By integrating sentiment analysis, companies can pinpoint paritcular customer friction points, optimise service, refine products, improve regulatory reporting, and manage reputational risks—turning consumer sentiment data into actionable insights.
How does sentiment analysis work?
Sentiment analysis relies on artificial intelligence and analytical techniques, often complemented by human review, to extract insights from large volumes of text.
While online customer feedback is a major source of sentiment data—offering real-time insights into public opinion—any text-based content, from news articles to call centre transcripts or survey data, can be analysed to measure sentiment shifts.
The value of sentiment data
Organisations leverage sentiment analysis for various strategic purposes. A multinational corporation may track sentiment for market research, while governments and political parties may monitor public response to policies or election candidates.
Case study - How DataEQ predicted the US Election and Brexit result by analysing public opinion.
Not all data is created equally - why accuracy matters
To make informed decisions based on sentiment data, companies should ensure that all sentiment data they work with is based on a minimum of 90% accuracy. The challenge with AI-only sentiment data is that it often sits between 60 - 70%, especially when dealing with complex data sets. This can result in mislabeling of data, and could see compliments being marked as complaints and vice versa.
Root cause analysis: the why behind the how
Simply knowing whether sentiment towards an engagement is positive or negative isn’t enough—businesses need to understand why consumers feel the way they do, in order to make the necessary adjustments. This relies on highly accurate sentiment data.
A smartphone brand, for instance, may notice a decline in sentiment after launching a new model. Without root cause analysis, they might attribute this to general dissatisfaction, when in reality, the issue could stem from a poor battery life or an unintuitive user interface.
Similarly, a financial services company might find that while branch service is highly rated, its digital banking experience is frustrating users, leading to negative sentiment. A deep dive into the topics driving this data could further reveal that a over complicated document upload process is the real driver of complaints.
Root cause analysis breaks down broad these sentiment trends into specific, actionable insights, allowing businesses to:
✔ Pinpoint specific issues affecting sentiment
✔ Identify product or service weaknesses
✔ Identify specific areas of friction along the customer journey
✔ Make targeted improvements based on consumer feedback
By moving beyond surface-level sentiment tracking, organisations can implement data-driven changes that directly impact customer experience and business performance.
Automotive topic wheel looking at main drivers of sentiment towards automotive brands
Net sentiment: a real-time alternative to traditional CX metrics
Net Sentiment is a modern customer satisfaction metric that provides an authentic voice-of-customer perspective, offering a real-time alternative to lagging indicators like Net Promoter Score (NPS).
Operational vs Reputational Net Sentiment
One of the biggest challenges in measuring Net Sentiment is separating brand reputation conversations (media coverage, PR, and marketing) from operational feedback (customer service and product experience).
Since customer service feedback tends to be more negative, it can obscure positive sentiment about broader brand reputation, while at the same time painting a potentially positive view of operational/CX performance. 
Granular Net Sentiment analysis of SA Banking industry
Challenges of sentiment analysis
Despite its advantages, sentiment analysis presents challenges that can impact accuracy and effectiveness.
Context and nuance
Human communication is complex—filled with sarcasm, irony, and slang that AI often misinterprets. A phrase like "Great service… as always" could be genuine or sarcastic, making automated sentiment classification difficult.
Words also change meaning depending on context. For example, "This bank is killing it!" illustrates how phrases can have dual meanings—positive or negative—depending on the context in which they are used. It stresses the importance of contextual awareness in sentiment analysis, as customer feedback online often contains implied meanings that contradict the literal text. For instance, the statement "best retailer ever!" might initially seem positive, but if it's followed by a complaint like waiting in line for 5 hours, it actually points to a negative experience. This example underscores the need for nuanced interpretation and response in sentiment analysis to avoid misleading results.
Mixed sentiment
A review like "The product is fantastic, but delivery was a nightmare" expresses both positive and negative sentiment in one mention, adding another layer of complexity to the analysis of the text.
The growing importance of sentiment analysis
With digital platforms increasingly shaping public discourse, sentiment analysis is replacing traditional market research and polling. It provides an instant, real-time pulse on public opinion, giving businesses a competitive edge in decision-making.
Today, accurate sentiment data isn’t a luxury—it’s a necessity. Organisations must leverage sentiment analysis to:
✔ Monitor brand perception
✔ Identify emerging risks
✔ Optimise customer experience
✔ Make data-driven strategic decisions
By addressing its challenges and refining its methodologies, sentiment analysis remains a powerful tool for business intelligence, helping brands stay ahead in an increasingly customer-driven landscape.