What Is Sentiment Analysis? How to Spot the Gap Between Market Data and Investor Behaviour
Sept 17, 2026
Understanding Sentiment Analysis in the Stock Market
What is sentiment analysis, and how accurately does it capture what investors actually believe? In financial markets, sentiment analysis attempts to measure the prevailing emotions, expectations and attitudes influencing investor behaviour, using sources such as surveys, market indicators, online discussions and other forms of behavioural data. The underlying assumption is that understanding how people feel about the market can help explain price movements, identify shifts in mass psychology and potentially reveal divergences between sentiment and market direction.
But what happens when the sentiment readings do not appear to match what people are actually saying or doing? That is the question emerging from a series of observations suggesting a potentially significant disconnect between reported bullish sentiment and the enthusiasm surrounding AI, Bitcoin and the broader market. The observations remain preliminary and subjective, but they raise an important question about whether some sentiment indicators are measuring the same thing their users assume they are measuring.
When Bullish Behaviour Contradicts Sentiment Readings
The market has risen substantially, major indices have reached new highs, and enthusiasm surrounding artificial intelligence has become increasingly visible. Yet some of the major commercial sentiment readings examined have not shown bullish sentiment reaching even 60, creating an apparent divergence between the market’s performance, the enthusiasm being expressed in everyday conversations and the readings reported by sentiment providers.
Initial conversations with people from different backgrounds suggested that bearishness was not the dominant attitude. Quite the opposite: many expressed confidence that AI would make them wealthy, despite having limited knowledge of what the technology can actually do. This enthusiasm was also reflected in browsing patterns observed through arrangements with two high-traffic websites, where interest in bullish Bitcoin articles was substantial, while interest in articles about the future of AI was roughly three times greater.
These observations do not establish that commercial sentiment indicators are wrong, because the people encountered and the readers observed may not represent the broader investing population. They do, however, raise a legitimate research question: if people are expressing considerable optimism about AI and markets, why do some widely followed sentiment readings appear comparatively subdued?
What Is Sentiment Analysis Actually Measuring?
The central issue is that sentiment analysis is only as useful as the data, sampling methods and assumptions underlying it. A survey may measure the stated outlook of a particular group of investors, while online browsing patterns may reveal which subjects attract attention, and conversations may expose emotional convictions that people do not necessarily express in formal questionnaires.
These are different forms of evidence, and they should not automatically be treated as interchangeable. Someone can believe AI will transform the world without being bullish on the stock market at current valuations, while another person can be bullish on equities without having much interest in AI. Similarly, reading an article about Bitcoin or AI does not necessarily indicate an intention to buy either asset.
This distinction matters because sentiment analysis can produce misleading conclusions when the measured population, the questions being asked and the interpretation of the results are not clearly understood. A sentiment indicator may accurately describe the group it surveys while failing to capture a broader behavioural shift occurring elsewhere. The problem may therefore lie not in the data itself, but in how that data is selected, framed and interpreted.
The Difference Between Market Sentiment and Market Behaviour
One of the more interesting observations concerns the growing enthusiasm surrounding AI. Advertisements promoting AI courses have become increasingly visible, frequently suggesting that people who fail to use tools such as Claude or ChatGPT will fall behind those who do. Some promotions make sweeping claims about productivity, earnings and business opportunities, often with a level of confidence that the available evidence may not justify.
Attempts to obtain firm guarantees of improved productivity, earnings or genuine earning prospects from several marketers have so far produced no acceptance. That does not establish that the courses are ineffective, but it highlights the difference between confident promotional claims and independently verifiable outcomes.
The same distinction applies to investment sentiment. People may express strong confidence in AI’s future while having little understanding of the technology’s limitations, the economics of its adoption or the valuations of companies involved. Their enthusiasm is still relevant behavioural evidence, but it should not automatically be interpreted as proof of informed investment conviction.
This is where sentiment analysis becomes more complicated than simply asking whether people are bullish or bearish. The intensity of a belief, the knowledge behind it and the willingness to act on it can all differ substantially.
How Data Framing Can Distort the Picture
Data can be accurate while the picture created from it remains incomplete. Imagine a town with an adequate food supply, but the media repeatedly presents images of starvation and reports shortages elsewhere. Residents may become fearful about food availability even when their immediate circumstances have not changed.
The example illustrates how the framing and selection of information can influence perceptions without requiring the underlying data to be fabricated. Financial markets are vulnerable to similar effects because investors are constantly exposed to narratives about crashes, bubbles, technological revolutions and economic threats.
A sentiment indicator may capture one part of this environment while missing another. Survey respondents might express caution about valuations even as their interest in AI-related opportunities increases, or online enthusiasm might be concentrated among people who are not represented in traditional sentiment surveys.
Consequently, an apparent contradiction between sentiment readings and observed behaviour does not, by itself, establish manipulation or faulty data. It establishes a reason to examine the methodology, sample composition and assumptions behind the readings more closely.
Why the AI Boom Makes This Divergence Worth Investigating
The current enthusiasm surrounding AI provides a particularly interesting environment in which to examine these questions. People across different backgrounds are expressing confidence that AI will change the world, while commercial sentiment indicators may be capturing a more cautious outlook toward equities as a whole.
These positions are not necessarily contradictory. Investors can believe AI will transform the economy while remaining uncertain about which companies will benefit, how much future growth is already reflected in share prices or whether current valuations leave sufficient room for further gains.
However, if the divergence persists across broader and more representative samples, it could reveal limitations in how conventional sentiment indicators capture emerging themes and behavioural shifts. The objective should be to establish whether the difference is genuine, which populations are expressing it and whether it has any meaningful relationship with subsequent market behaviour. That requires more than anecdotal evidence, but the observations provide a reason to investigate rather than dismiss the question.
From Observation to a More Rigorous Study
The initial findings are not sufficient to support a definitive conclusion. The available conversations and browsing observations represent limited samples, and the historical comparisons involving major sentiment providers still require careful verification before claims of unprecedented divergence can be made.
A broader study would need to examine how different sentiment providers construct their indicators, which populations they survey, how frequently their data are updated and whether their readings capture changes in investor behaviour consistently across different market environments. It would also need to distinguish between general enthusiasm for AI, bullishness toward specific assets and expectations about the direction of the overall market.
Additional questionnaires, aggregate browsing patterns and observations from other groups could help expand the evidence base, provided the research respects privacy and avoids treating individual anecdotes as representative of the wider population. Comparisons across multiple data sources would also help determine whether the apparent divergence reflects a genuine behavioural shift or simply differences in what each source measures. The goal is not to force the data to support an existing suspicion. It is to establish whether the pattern persists when examined through broader, more representative and methodologically consistent evidence.
What the MOAB Shift May Be Signalling
A recent shift in the MOAB (Mother of all buys)to neutral prompted adjustments to the Joy Indicator and Vector Psychology systems. Following those adjustments, the results indicated relatively strong coherence for a significant correction, rather than the much weaker coherence previously observed.
That change is worth monitoring, but it should not be treated as confirmation that the sentiment divergence is objectively established or that a correction must occur. Indicators are tools for interpreting market conditions, and their outputs need to be assessed alongside the underlying evidence rather than treated as independent proof.
The distinction is especially important when the data itself is still being investigated. A model can identify a potential configuration of market conditions, but the interpretation remains provisional until the observations and assumptions supporting it have been tested more thoroughly.
The Real Question Behind Sentiment Analysis
What is sentiment analysis ultimately trying to reveal? It is an attempt to understand the relationship between what people believe, how they behave and how those beliefs interact with market prices, but the quality of the conclusions depends on whether the measurements capture the relevant population and the right dimensions of behaviour.
The apparent disconnect between reported sentiment and observed enthusiasm surrounding AI is not proof that commercial indicators are defective, nor does it establish that the market is approaching a major correction. It is a hypothesis that deserves broader investigation because the relationship between sentiment, behaviour and price is central to understanding market psychology.
The next step is to gather more representative evidence, verify historical comparisons and determine whether the divergence persists across different sources and methodologies. Until then, the most useful conclusion is not that the data is wrong, but that understanding what sentiment indicators actually measure is just as important as understanding what their readings appear to say.
