The document discusses natural language processing (NLP) and its applications in text analytics, highlighting the importance of processing human languages through methods like tokenization, stopword removal, stemming, and lemmatization. It introduces the Natural Language Toolkit (NLTK) as a tool for performing these tasks and explains key processes such as data cleaning, text classification, and named entity recognition. Overall, it emphasizes the significance of extracting meaningful information from text data.

1. TEXT ANALYTICS
USING NLTK
By,
Vaishnavi A
III CSE B

2. What is Natural Language Processing?
NLP is a part of computer science and
artificial intelligence which deals with
human languages.

3. Think about how much text you see each day:
• Email
• SMS
• Web Pages
• Newspaper
• and so much more…
The list is endless.

4. What is Text Mining?
Text Mining / Text Analytics is the process of deriving
meaningful information from natural language text.

5. Applications of NLP

6. Applications of NLP

7. NLTK

8. What is NLTK?

9. Data Cleaning

10. •TOKENIZATION
Tokenization is the first step in NLP
Tokenization is the first step in NLP

11. Tokenization

12. Removal of StopWords
•Stopwords might not add much value to the meaning of
the statement
•Perform tokenization before any stopwords removal.
Eg: “There is a book on the table”
The words “is”, “a”, “on” and “the’ 🡪 Stopwords
Words like “there”, “book” and “table” 🡪 Keywords

13. STEMMING
Normalize words into its base form or root form
Affects Affections Affected Affection Affecting
Affect

14. Groups together different
inflected forms of a word,
called Lemma
Somehow similar to
Stemming, as it maps several
words into one common root
Output of Lemmatization is a
proper word
For example, a Lemmatizer
should map gone, going and
went into go

16. Text Processing

17. Vectorization

18. Text Classification

19. POS : Tags and Description

21. NAMED ENTITY RECOGNITION