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We’re excited to introduce a new enhancement to Dcipher Analytics’ AI-powered Research Matrix: the ability to generate executive summaries for both rows and columns. This update makes it even easier to extract key insights from complex datasets - whether you’re monitoring competitors, tracking industry trends, or mapping stakeholder activities across multiple dimensions.

We’re excited to introduce new analysis modes in Dcipher Analytics Radar, enhancing its capabilities for trend detection, horizon scanning, and arena analysis. What started as a trend radar - a tool for identifying emerging trends across large volumes of news, social media, and web-based sources - has now evolved into a more versatile solution with four distinct analytics modes.

We’re excited to introduce Research Agents - a powerful new feature in Dcipher Analytics that transforms web-based research. Traditional methods, whether manual or automated, have significant limitations. Research Agents overcome these challenges by combining human-like flexibility with the speed and scalability of AI-driven automation.

How about getting a shortcut to the sites that all the buzz is about? Dcipher Analytics’ automated AI workflow scans social media posts within your specified domains for links to external sites—and serves you the 100 links that have been shared the most. You can even schedule the workflow to automatically deliver you a fresh list of top links at a regular interval.

Are you ever curious about some online subculture, niche discussion, or even conspiracy theory—but don’t feel like reading through endless social media threads to get to know it? In this blog post, we explore how a Dcipher Research Bot can be used to learn about one of the more eccentric conversations out there: the debate about whether the Earth is actually flat.

Have you tried training Dcipher Research Bots on news yet? They are the perfect solution when you need to find answers about what is going on.

In this blog post, we take a look at how we can use a content landscaping workflow to pick up insights about Generation Z in Japan from Japanese news articles—without even knowing Japanese.

Have you seen our workflow for daily social media updates? Using this, you get daily updates with the top 15 trending topics from social media in your specific area of interest directly to your Slack or Microsoft Teams channel.

In this blog post, we take a look at how Dcipher Research Bots can be used to quickly find information hidden in long and complex texts, using the EU Artificial Intelligence Act as an example.

Do you wish you could get ready-made summaries of what is written within your topics of interest in global news media or in a set of reports? Dcipher Analytics offers workflows tailored exactly for that—letting you automate your desk research and get more time for your most high-value tasks.

A Dcipher Research Bot is a life saver whenever you have questions about what is reported and discussed in news or social media. In this blog post, we train a bot that can tell us about the news reporting from the U.S. primary elections.

What does today’s world believe will happen in year 2030? In this blog post we use Dcipher’s Insight Booster Toolkit for a foresight exercise to map out what news media around the world are talking about when they mention that year.

Artificial Intelligence has dramatically redefined the insight generation landscape, transforming the cumbersome process of gathering, processing, and analyzing massive amounts of textual data, be it news reporting, social media, or reports, into a more streamlined procedure. This blog post aims to provide you with a high-level overview of a two-phase approach to AI-empowered research.

Are you using our content landscaping workflows? Content landscaping is a highly effective method for mapping and exploring themes from texts in a visual way. We recommend using them whenever you get an overview of a large set of text data.

Designed to simplify content accessibility and optimize user interaction, our Knowledge Bot is more than just a regular chatbot. It's an automated analyst, a digital librarian, and a 24/7 customer representative, all combined into a powerful tool nested right on your website.

Let us introduce you to our new research bots! They can be used as your personal desk research assistant, to enhance the access to information inside your organization, and much more. You can even integrate our bots into your own website.

This blog post explains the usage of Active Learning workbench and training a customized ML model to classify legal cases and predict their possible outcomes in the legal context, such as "referred," "applied," "distinguished," "discussed" and "considered.

Deep dive into the dreams of people all around the world by using Natural Language Processing (NLP)

Mining and creating insights based on news articles about the Metaverse by using the news media scanning project template.

Text analysis can be used in various cases. One of the usages is analyzing emails as text data and figure out valuable insight from that.

Social media is one of the most helpful resources for data analysis. However, It can be challenging to analyze a high amount of data. In this text analysis, by using social media posts, you will discover where Americans plan to visit for this summer, in 5 quick steps.

Text analysis can be helpful in various aspects you cannot even think of. In this post, you find the best gift ideas for Father's Day by using social media analysis.

Open-ended survey questions are notoriously difficult to analyze. This post shows four easy ways of visualizing them.

Free-form text responses in surveys are a gold mine of information. This post describes how to mine it.

Sentiment analysis can be used to measure how positively or negatively people express themselves, for example in relation to a given product or brand. It is useful for analyzing and responding to feelings expressed in customer emails, calls, reviews, and more.

Social media contain open and public discussions about every conceivable topic. These discussions can provide invaluable insights into the views and narratives among consumers, influencers, and businesses. But the information is unstructured, in the form of text and images, and spread out across a large number of social media platforms. Making sense of it has typically required programming and data science skills: the data needs to be collected, preprocessed, structured, and analyzed. This post presents a new approach, one which does not require any programming or expert text analytics skills.

The COVID-19 Open Research Dataset (CORD-19) is a public dataset with over 59,000 coronavirus-related scholarly articles. It was prepared by the White House and a coalition of leading research groups. This post shows how Dcipher Analytics can be used to explore the CORD-19 dataset and extract useful information from it.

In this post, we analyze the discussion about Greta Thunberg in the public social media sphere to understand who is talking and what they are saying. We identify key influencers and map the discussion.

In this article, we offer a step-by-step guide to the news media mining approach in Dcipher Analytics. It does not require any programming or advanced analytics skills. Instead of reading articles one by one, the new approach allows you to visually find patterns in large amounts of articles and drill down in article clusters of interest.

The digital information universe is growing exponentially. It was only ten years ago, in 2009, that the total amount of digital information exceeded 1 zettabyte – a hundred million times more than what fits in the U.S. Library of Congress, the world’s largest library. Since then it has doubled every second year and is expected to exceed 40 zettabyte next year. The vast majority of this data is unstructured, particularly in the form of text, images, video, and audio.

Reviewing the academic literature around a topic is typically a qualitative process, where a small number of highly cited academic articles are studied in-depth. But useful finding may also exist in the “long tail” of less cited articles. Depending on the research topic of interest, this “long tail” may consist of thousands or even tens of thousands of articles. To map the entire landscape of academic research around a topic, we therefore need to use a different approach, one which combines quantitative mapping with qualitative analysis.

Netnography is a research method useful for studying online consumer culture. By observing naturally occurring discussions and phenomena on the internet, it seeks to unpack the cultural codes and expressions that influence consumption choices within the communities under study. It views social media as much more than likes, reposts, influencers, and keyword occurrences. To netnographers, social media are manifestation of cultural phenomena, making them ideal places for acquiring a rich and contextualized understanding of consumers. To make sense of such cultural data, the researcher is a fly on the wall, observing but not interfering.

Artificial intelligence (AI) is causing two major shifts in information search. First, it makes a transition from manual and criteria-based search to cognitive and adaptive search. Second, it replaces statistical and hypothesis-based approaches with open-ended and deep learning-powered techniques. This post outlines how anyone, with the right tool, can start searching information using the AI-powered approach.

Traditionally, analysis of free-form text data from surveys have required coding, where researchers read through the answers and manually code them with fixed or emergent categories. To ensure accuracy and consistency, each post needs to be coded by at least two researchers. If the survey is conducted in multiple languages, native speakers of each language need to do the coding. They also need to coordinate with each other to make sure they interpret answers in a consistent way.