Intermediate
9h
Performing Natural Language Processing with R
Gain insights into NLP concepts using R, including the tm package, corpora, structured data conversion, and advanced search techniques. Discover quanteda and tidytext for text processing.
This course will cover concepts in natural language processing (NLP). Developers will find this valuable because of the high demand for NLP skills. This includes understanding natural language when building applications like chatbots, sentiment analysis, search engines, and content recommendations. NLP also provides tools for data analysis, personalization, and content filtering.
In this course, you will learn concepts of NLP, how to use the tm package, the use of corpora, how to convert text to structured data, advanced search techniques, metadata, and how to use quanteda, and tidytext.
With this information, you can expect to command in-demand skills in many text-processing-related fields.
This course will cover concepts in natural language processing (NLP). Developers will find this valuable because of the high dem...Show More
WHAT YOU'LL LEARN
Working knowledge of natural language processing, including sentiment analysis, statistical analysis of corpus contents, and use of metadata
Hands-on experience with R natural language processing packages, including tidytext, tm, and quanteda
Familiarity with term frequency, stemming, n-grams, and lemmatization
Understanding of parts of speech and ability to apply it to natural language research
The ability to use tf-idf to identify documents corresponding with terms
Working knowledge of natural language processing, including sentiment analysis, statistical analysis of corpus contents, and use of metadata
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TAKEAWAY SKILLS
Content
1.
Before We Begin
2 Lessons
Get familiar with natural language processing using R, focusing on practical application and foundational skills.
2.
Important Concepts in Natural Language Processing
10 Lessons
Understand crucial NLP concepts like tokenization, sentiment analysis, tf-idf, and R packages.
3.
Text Mining Package
2 Lessons
Break apart the tm package in R for effective text mining and NLP tasks.
4.
Understanding Corpora and Sources
4 Lessons
Enhance your skills in understanding corpora, corpus classes, and source types in R.
5.
Converting Text to Structured Data
4 Lessons
Map out the steps for converting and cleaning text for structured data analysis.
6.
Document Insights and Advanced Search Techniques
8 Lessons
Focus on R techniques for tokenization, stemming, DTMs, tf-idf, n-grams, and visualization.
7.
Working with Metadata in the tm Package
4 Lessons
Learn how to improve metadata management in text analysis using the R 'tm' package.
8.
Implementing NLP with the quanteda Package
7 Lessons
Learn how to use the quanteda package for advanced text analysis and sentiment evaluation.
9.
Implementing NLP with the tidytext Package
7 Lessons
Get started with text mining in R using the tidytext package for comprehensive NLP tasks.
10.
Concluding Remarks
1 Lessons
Examine your achievements in text mining, sentiment analysis, and NLP with R.
11.
Appendix
1 Lessons
Find out about sample file sources and key public domain novels used in the course.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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