Frequently Asked Questions (FAQ)
At Lanternoid, we collect reviews from platforms such as Amazon or Goodreads. In the book analytics page, we show you the number of reviews processed from each platform, and the latest date of the collected reviews.
Lanternoid has also its own data: You can add +1 to displayed review content that you think accurately describes the book and tag information that has been helpful to you.
Each book has a score. Lanternoid calculates the score by considering the content of the reviews and it does not consider the star rating of the reviewers as people have different criteria for star ratings. This will avoid biased rating and you can use it for a comparison between multiple options. Also, it considers the number of reviews. The larger the number of reviews, the more precise the score will be. This approach helps to avoid having issues where the decision of the final score is only coming from a few numbers of readers' opinion. If the number of reviews is too low, we set it to ‘?’.
We automatically process the review texts coming from the third-party platforms. We find topics reviewers mentioned in the reviews. We then group these topics based on their meaning, the relation to each other and similarity. For example, “perspective” is related to “view” and “perspectives”.
You can search for a topic on the “Most mentioned phrases” section to find out what reviewers mentioned about them, and what the majority of the opinions are about the topic.
We automatically separate what reviewers liked or disliked about a book and we group them to “I liked (that)...” and “I disliked (that)...” for easy lookup.
We have two different structure on the phrases for each group. You may read a phrase on “I liked (that)...” group like:
Example 1: I learned about another culture.
Read it like: I liked that I learned about another culture.
Example 2: the narrator on Audible.
Read it like: I liked the narrator on Audible.
We highlight some of the contents we have found from reviews that might be important for users to know before reading the book. These keywords can be related to the difficulty of the book, the emotion the book brings to its readers, or special topics such as “romance” or “violence”.
We collect to whom the reviewers referred the book to so that you can quickly have an idea of who is the book good for.
You can read each phrase like so:
Example: anyone who has ever questioned religion, society, or law.
Read it like: Reviewers recommend it (the book) to anyone who has ever questioned religion, society, or law.
We have an automated way to process the review texts coming from the third-party platforms. The first step is to extract and compress relevant information from the reviews. This is where we find attributes such as “great” or “intense” for any topics reviewers mentioned. We then group and categorise these attributes based on their meaning and the relation to each other. They are grouped by similarity (“Related”), or opposition in meaning (“Opposites”), and categorised depending on whether they have a positive (“Positive”), or negative (“Critical”) meaning. For example, “great” is related to “fascinating” and “awesome”. It is opposite to “bad”.
Note: You can access “Related” words by clicking on on the book analytics page.
Note: You can access “Opposites” words by clicking on on the book analytics page.
On the site, we display the related attributes for these topics: book, author, bio, memoir, autobiography, writer, novel, story, and biography
You can search on the “Most mentioned phrases” section for any other topics, adjectives or keywords of your choice to find out whether the reviewers talked about that or not. Lanternoid searches on attributes and topics, on both related and opposite contents to help you find an answer to your question.