Identify book chapter sentiment
using AI
Below is a free classifier to identify book chapter sentiment. Just input your text, and our AI will predict the sentiment of each chapter in the book - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("book-chapter-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/book-chapter-sentiment/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_text_here"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_text_here"}' \
https://www.nyckel.com/v1/functions/book-chapter-sentiment/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict the sentiment of each chapter in the book.
This pretrained text model uses a Nyckel-created dataset and has 24 labels, including Angry, Appreciative, Content, Cynical, Disappointed, Discouraging, Encouraging, Enthusiastic, Excited and Frustrated.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of each chapter in the book).
Whether you're just curious or building book chapter sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify book chapter sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Academic Publishing Insight: In the academic publishing industry, the 'book chapter sentiment' identifier can be employed to analyze the sentiment of individual chapters in scholarly texts. This insight can help editors gauge the overall tone and emotional impact of educational materials, ensuring alignment with the intended audience and subject matter.
- Market Research Analysis: Businesses can utilize the sentiment analysis tool to assess consumer sentiments expressed in book chapters related to specific market trends. This data-driven approach allows companies to refine their product offerings and marketing strategies based on public perceptions and preferences highlighted in literature.
- Content Recommendation Systems: E-commerce platforms and streaming services can leverage the sentiment identifier to enhance their recommendation algorithms. By analyzing the sentiment of book chapters, they can suggest titles that match the emotional landscape a user has previously enjoyed, fostering a more personalized reading experience.
- Educational Material Development: Educators and curriculum developers can use sentiment analysis to evaluate the emotional tone of textbooks and supplementary materials. This ensures that content is appropriately engaging for students, helping to foster a positive learning environment and catering to diverse educational needs.
- Author Feedback Mechanism: Authors can utilize sentiment identifier technology to gain insights into how readers perceive different chapters of their work. This feedback can be crucial in tweaking narrative elements or themes before final publication, ultimately enhancing the overall reception of their writing.
- Sentiment-Based Social Listening: Organizations can leverage the sentiment identifier as part of their social media listening strategy. By analyzing sentiments in discussion forums or book reviews that reference specific chapters, brands can better understand public perceptions and adjust marketing messages accordingly.
- Library Resource Management: Public and educational libraries can apply this sentiment analysis to curate their collections based on reader preferences and emotional responses to different topics. By selecting materials that resonate with their community's sentiments, libraries can ensure they are promoting relevant and engaging resources.