Social listening has evolved beyond simply tracking brand mentions on Twitter or monitoring Instagram hashtags. Today's most potent social intelligence platforms like Infegy Starscape can extract insights from virtually any text-based source like Goodreads reviews (in the data science world, we call this unstructured data). Analyzing unstructured data with ad-hoc scripts is messy, time-consuming, and error-prone. That's why we at Infegy have spent the last 18 years pioneering approaches that apply our advanced analytics to diverse datasets, providing a comprehensive understanding of audience sentiment and behavior. Let's dive into how looking at unstructured datasets in addition to traditional social listening can boost insights.
With over 150 million members and 3.5 billion books added to shelves, Goodreads represents one of the world's largest communities of engaged readers. For publishers, authors, and marketers, this platform offers a gold mine of authentic consumer feedback—unfiltered opinions from people who have invested significant time with a product. Interestingly, we learned on our last webinar with Kris Longfield over at Fanthropology that Goodreads review strength is often a leading indicator of what Hollywood movies will come out in the next 3-5 years.
Unlike the more temporary nature of social media posts, book reviews represent deeper engagement. Readers typically spend hours with a book before forming and sharing their opinions, resulting in more nuanced and considered feedback, making Goodreads reviews particularly valuable for understanding how stories resonate after extended use. Remember, though, that you can apply these same lessons to other narrative-based reviews like those on Amazon, Rotten Tomatoes, and New York Times Recipes (the list goes on and on).
Our research has revealed the power of the "marketing funnel" approach to text analysis. By examining both social media conversations and detailed reviews, we can track consumer journeys from initial awareness through post-consumption reflection:
This dual perspective provides a complete picture that is impossible to achieve through either source alone. By monitoring both datasets, we can observe how early signals on platforms like TikTok or Instagram often predict trends that later materialize in detailed Goodreads reviews.
Now that we’ve talked about the value of Goodreads reviews, let’s take a look at the specific metrics you can use to guide your analysis.
When analyzing social datasets, post volume is usually our first stop. This can help you validate trends and look at growth patterns. You can use review volume (e.g., how often reviewers post about a particular book) to understand many of the same factors (albeit much lower on the funnel than general social data).
Figure 2: Month-by-month review for Midnight Black (#2 NYT Bestseller) showing the typical growth pattern for book releases (November 11, 2024, through February 24, 2025); Goodreads review data.
Figure 2 demonstrates how rapidly engagement typically grows following a book release (Midnight Black, our example book, released on February 20). We see low review counts for the reviews provided by early reviewers, with a significant spike after the book's release to more general audiences. While the pattern can vary based on marketing strategy, author platform, genre trends, or whether it's a subsequent novel in a book series, the general post-release spike is very typical.
On the other hand, just looking at review counts can only take you so far. Our sentiment analysis capabilities extend far beyond simple positive/negative classification. Infegy Starscape's AI-powered engine recognizes nuance, context, and intensity of opinion—often matching human judgment with remarkable accuracy:
Figure 3: Month-by-month positive, negative, and neutral reviews for Midnight Black (November 11, 2024, through February 24, 2025); Goodreads review data.
Figure 3 shows the number of reviews by sentiment (positive, negative, and neutral). Our example book also follows a traditional pattern, with advanced review copies typically being more positive. Once actual readers get their hands on books, we sometimes see an increased distribution of negativity.
So far, we’ve looked at trend-based views. However, to conduct a thorough review of a book’s performance, we also need to look into the very words reviewers are saying. For that, we can use Infegy’s topic modeling to get an idea of the terms and ideas that drove the conversation.
Figure 4: Word cloud visualization of the most prominent topics extracted from Midnight Black reviews (November 11, 2024 through February 24, 2025); Goodreads review data.
This granular topic analysis helps authors and publishers understand exactly which elements connect with readers and which might need refinement in future works by the author or in the same genre. From character development to pacing issues, these insights provide specific, actionable feedback.
While we've focused on Goodreads reviews here, the same methodologies apply to virtually any unstructured text source:
The ability to extract consistent, comparable insights across these diverse sources provides a major competitive advantage, enabling businesses to develop a comprehensive understanding of consumer sentiment.
As AI technology continues to advance, our ability to extract meaningful insights from unstructured text will only improve. Organizations that leverage these capabilities now—applying sophisticated analysis to diverse data sources—will develop a deeper understanding of consumer sentiment and behavior.
The most valuable insights often come from connecting data points across multiple channels. By analyzing both traditional social media and specialized platforms like Goodreads, businesses can track the complete consumer journey from initial awareness through post-purchase reflection.
Infegy Starscape makes this comprehensive approach accessible, transforming complex, unstructured text into clear, actionable intelligence. Whether you're analyzing book reviews, product feedback, or customer support interactions, our platform provides the tools to uncover the insights that drive strategic decision-making.
Different industry players can leverage these insights in specific ways:
For Publishers:
For Authors:
For Marketers:
For Researchers: