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Ever since British economist John Maynard Keynes first declared that investors are prey to people’s urge to act, however irrationally, the financial world has tried to quantify the impact of public sentiment on stock prices. Solving the puzzle would give investors in the know a huge advantage over the competition.

Over the past decade, one vibrant corner of that still ongoing research has been data analysis. The goal has been to tease out clues about sentiment that are hidden in news articles, regulatory filings, transcripts, and press releases.

With the rise of artificial intelligence, the sophistication of sentiment-measuring technology is increasing. And a number of companies such as AlphaSense, Alexandria Technology, and Aiera are racing to perfect the software that makes it possible.

But they face a number of challenges. The technology is still imperfect and still must prove that it can outperform more basic investment strategies like stock index funds.

“If you can systematically and objectively track over time how the facts change, in terms of positive facts and negative facts that emerge in a conference call, say, that could have real value,” says David Wong, an investment analyst with Instinet.

Last month, AlphaSense, a startup that sells its service to hedge funds and financial analysts, introduced technology that sifts though documents to determine the tenor of their language. The so-called sentiment tool then provides an overall score for the tone that traders can then use to invest.

Last week, AlphaSense announced that it had received an additional $50 million in funding, led by venture fund Innovation Endeavors with participation by Soros Fund Management. Since its founding in 2011, AlphaSense has received over $90 million in financing.

On a recent day at the company’s New York headquarters, a large monitor on the conference room wall flashed green and red—indicating changing sentiment for various companies. After being fed transcripts, such as from corporate earnings calls, the software spits out a score on a scale of plus 100 to negative 100.

To help investors quickly find the text that is responsible for the sentiment score, AlphaSense’s service color codes the transcripts or other text fed into it. Green or red coloring on a conference call transcript, for example, highlights encouraging and discouraging passages.

On this particular day, the publicly traded company with the most negative score was Sephaku Holdings, a South African maker of cement plants. It’s not a surprising conclusion considering that the company’s CEO said during an earnings call that his customers—construction companies— had “blood on the floor” due to recessionary trends.

The company with the most positive sentiment was supply-chain services company Synnex. Its latest earnings call was filled with upbeat language, such as “excellent,” “solid,” and “very pleased.”

If it all seems a bit obvious, that’s the intention. The automatic analysis is supposed to reach a similar conclusion that a human would make.

“If you listened to that earnings call, you would say our algorithm accurately reflects your own sentiment of the tone of that call,” says AlphaSense CEO Jack Kokko.

AlphaSense says the sentiment produced by its technology mirrors what humans conclude 90% of the time.

Still, Kokko encourages customers to use their own brains and not just follow what the algorithm says. He describes his technology as “augmented intelligence”—a help, not a replacement for humans.

“This is a product for a human analyst, to make their workflow more efficient,” says Kokko.

Sentiment scores can also be used as the basis for a broader economic analysis. In a demonstration, Kokko showed a heat map on his laptop that highlights sentiment about sectors of the U.S. economy over time. In 2007, a year before the Great Recession hit, most business sectors of the U.S. economy in the heat map were flashing green. But a few, such as those representing the industrial sector, had turned pale red, warning of possible trouble.

AlphaSense isn’t alone in trying to crack the code. Alexandria Technology, in Los Angeles, has been working on the problem for 12 years, for example. Dow Jones uses Alexandria’s software for measuring sentiment in news reports while financial information service FactSet makes it available to subscribers.

Meanwhile, another firm, called Aiera — a play on “A.I.”— uses A.I. to compare the tone in news that mentions a given company to its stock performance. Ultimately, the technology produces a score along with a buy, hold, or sell signal.

In past, with less sophisticated technology, computers assessed language for sentiment by counting the frequency of words in a text, such as “debt,” “layoffs,” “foreclosure.” Modern sentiment analysis can make many more connections between such terms and more distant words in the same paragraph, to understand the context.

Still, there will be limits to such tools. In the end, the technology may be just one of many that investors use to decide how to invest.

“Software like this can be another signal for quant traders and another resource for stock analysts,” says Pierre Ferragu, managing partner with stock research firm New Street Research, using industry jargon for math-driven investors.

However, Mr. Ferragu warns that while the software can speed up work for people in the financial industry, the public nature of the data it bases its opinions on, like conference call transcripts, reduces the benefit. Over time, rival technologies that conduct the same kind of textual analysis may reach identical conclusions about individual stocks—limiting any trading edge the technology provides.

Then, too, gauging sentiment is tricky because computers have trouble with irony and metaphors. Human thinking can also be a mystery.

Still, the biggest question about using such technology for investing is whether it accurately measures factors that lead to successful trading results. Whether sentiment in documents is a good indicator of future stock performance has yet to be proven, and some signals can be misleading if other factors aren’t considered.

In June, memory chipmaker Micron Technology held a conference call to reiterate yet another dreary outlook for the chip industry. After analyzing the conference call transcript, AlphaSense’s service came up with a score of negative 24, a big red flag that reflected a decline in sentiment from the prior quarter.

But the next day, Micron’s shares jumped 8%.

The dour earnings call had been the tipping point that big investors had been waiting for. Bad sentiment is one thing, but really bad sentiment can often be a positive indicator of an impending turnaround, as it has been for Micron for some time.

“Investing is about having an intuition about something the market is missing,” observes New Street’s Ferragu. He then talked about the limits of artificial intelligence. “These tools can help you source an intuition and check it against the data, but it will never replace your own intuitions about the market.”

Correction: An earlier version of this article incorrectly identified the lead investor in AlphaSense’s recent funding round. It was Innovation Endeavors.

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