There’s no one-size-fits-all list of NLP tools, but here’s a breakdown of some of the most helpful options for content creators—organized by what they’re best at.
1. Tools for Writing and Generating Content
- ChatGPT – Great for idea generation, outlines, and draft writing.
- Jasper – Helps create marketing copy and blog content with custom tones.
- Writesonic – Useful for generating and rephrasing text, plus AI images.
- Copy.ai – Focused on short-form content like intros and social posts.
2. Tools That Improve Clarity and Style
- Grammarly – Checks grammar, spelling, tone, and even plagiarism.
- Hemingway Editor – Highlights long or complicated sentences.
- Readable – Scores your writing’s difficulty and suggests improvements.
- QuillBot – Offers paraphrasing, grammar checks, and summarizing tools.
3. Tools for Research and SEO Optimization
- SurferSEO – Helps optimize content based on search engine data.
- BuzzSumo – Shows trending topics and content ideas.
- GWI Spark – Offers audience and consumer insights for better targeting.
- Google Cloud Natural Language API – Analyzes text to uncover sentiment and key topics.
- Yoast SEO – A WordPress plugin for real-time content and SEO feedback.
4. Tools for Managing Social Media Content
- Buffer – Supports scheduling posts and analyzing engagement.
- Hootsuite – Helps manage content and track audience trends across platforms.
Things to Keep in Mind
- These tools are based on Natural Language Processing—not to be confused with “Neuro-Linguistic Programming.”
- Many offer free versions with limited access—advanced features often require a subscription.
- NLP tools continue to change fast, so keep an eye on new updates and features.
If you’re a content creator looking to stay ahead, understanding how Natural Language Processing (NLP) tools work can make a big difference.
These tools help you analyze, improve, and create content that aligns better with what people are searching for online.
1. spaCy
Why it matters: spaCy is one of the most efficient libraries for NLP tasks like tokenization, sentence segmentation, and named entity recognition.
Pros:
- Fast and production-ready
- Great for custom pipelines and models
- Strong support for many languages
Cons:
- Steep learning curve for beginners
- Requires coding knowledge (Python)
2. NLTK (Natural Language Toolkit)
Why it matters: NLTK is a great toolkit for experimenting with language processing and prototyping content analysis workflows.
Pros:
- Ideal for research and educational use
- Includes a large selection of pre-built corpora and tools
Cons:
- Slower than spaCy
- Not ideal for production environments
3. Gensim
Why it matters: Gensim is designed for topic modeling and semantic analysis. It’s great for identifying content themes.
Pros:
- Powerful for analyzing document similarity
- Efficient with large volumes of text
Cons:
- Requires solid Python experience
- Not ideal for beginners without coding background
4. TextRazor
Why it matters: TextRazor offers entity extraction, topic tagging, and relationship analysis via API. It’s useful for keyword-rich content workflows.
Pros:
- No setup required—API based
- Good for quick content analysis
Cons:
- Usage limits on free tier
- May require API integration experience
5. MonkeyLearn
Why it matters: MonkeyLearn is beginner-friendly and provides visual dashboards for analyzing content, including keyword and sentiment tools.
Pros:
- No coding required
- Drag-and-drop functionality
Cons:
- Limited advanced features on free plan
- Less customizable for technical users
6. IBM Watson Natural Language Understanding
Why it matters: It’s ideal for businesses that want detailed sentiment and emotion detection across content.
Pros:
- Rich language analytics
- Integrates well with enterprise platforms
Cons:
- Can be expensive for small teams
- Requires API setup
7. Google Cloud Natural Language API
Why it matters: This tool lets you analyze syntax, sentiment, and entities with the power of Google’s AI.
Pros:
- Scalable for large content operations
- Good documentation
Cons:
- Pricing based on usage
- Requires familiarity with Google Cloud setup
8. Amazon Comprehend
Why it matters: Comprehend is Amazon’s NLP tool focused on language detection, entity recognition, and custom classification.
Pros:
- Scalable for large datasets
- Good for multilingual analysis
Cons:
- Costs add up fast with high volume
- Best suited for tech-savvy teams
9. Lexalytics
Why it matters: Focused on sentiment and intent, Lexalytics is a popular tool for marketers and enterprise content teams.
Pros:
- Visual dashboards and clear reporting
- Good for structured and unstructured data
Cons:
- May not be ideal for small businesses
- Pricing details are not transparent
10. Frase
Why it matters: Frase helps content creators research, write, and optimize content around user questions and SERP data.
Pros:
- Great for blog and content briefs
- Easy to use for non-technical users
Cons:
- Not a full-scale NLP engine
- Focuses more on SEO use cases
11. SurferSEO
Why it matters: This tool blends NLP analysis with SEO data to help you write content that ranks better.
Pros:
- Real-time keyword suggestions
- Integrates with Google Docs
Cons:
- Best results come with premium plan
- Focuses mostly on SEO, not broad NLP
12. Copy.ai
Why it matters: This tool uses language models to help create content faster with built-in keyword awareness.
Pros:
- Templates for product descriptions, blogs, and more
- Beginner-friendly interface
Cons:
- Not customizable for deep NLP analysis
- Can produce generic outputs
13. Jasper (formerly Jarvis)
Why it matters: Jasper is an AI writing assistant that supports keyword-driven content creation for marketing and blogging.
Pros:
- Includes templates for SEO
- Can scale content creation quickly
Cons:
- Requires a subscription
- Needs editing to avoid repetition
14. Cortical.io
Why it matters: This tool uses semantic fingerprinting to find deeper meaning in language—helpful for research-driven content creators.
Pros:
- Unique approach to language modeling
- Strong contextual analysis
Cons:
- Not as beginner-friendly
- Targeted more toward data scientists
15. Apache OpenNLP
Why it matters: A Java-based toolkit with support for various NLP tasks, including parsing and entity detection.
Pros:
- Open-source and flexible
- Good for building NLP systems
Cons:
- Requires programming background
- No built-in visualization tools
Final Thoughts
Whether you’re writing blog posts, analyzing content, or planning SEO strategies, these NLP tools can help you work smarter. Each one brings something unique—so choose the one that fits your needs, budget, and experience level. If you’re looking to build a content strategy around NLP and AI, our team at AI SEO Services can help you get started with the right tools and methods.
