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What is NLP?

- Unstructured text mining means extracting “features”

• Features are structured meta-data representing the document

• Goal: “vectorize” the documents

- After vectorization, apply advanced machine learning techniques

• Clustering

• Classification

- Decision Trees

- Naïve Bayesian Classifier

• Scoring

- Once models have been built, use them to automatically categorize incoming documents

Example: UFOs Attack

When I fist noticed it, I wanted to freak out. There it was an object floating in on a direct path, It didn't move side to side or volley up and down. It moved as if though it had a mission or purpose. I was nervous, and scared, So afraid in fact that I could feel my knees buckling. I guess because I didn't know what to expect and I wanted to act non aggressive. I though that I was either going to be taken, blasted into nothing, or…

If we really are on the cusp of a major alien invasion, eyewitness testimony is the key to our survival as a species.

Strangely, the computer finds this account unreliable!

Investigators need to…

Search

for keywords and phrases, but your topic may be very complicated or keywords may be misspelled within the document

Manage

document meta-data like time, location and author. Later retrieval may be key to identifying this meta-data early, and the document may be amenable to structure.

Understand

content via sentiment analysis, custom dictionaries, natural language processing, clustering, classification and good ol’ domain expertise.

Practice : Sentiment Analysis

# Load the library

library(RSentiment)

calculate_total_presence_sentiment(c("This is a good text", "This is a bad text", "This is a really bad text", "This is horrible"))

#output

[1] "Processing sentence: this is a good text"

[1] "Processing sentence: this is a bad text"

[1] "Processing sentence: this is a really bad text"

[1] "Processing sentence: this is horrible"

[,1] [,2] [,3] [,4] [,5] [,6]

[1,] "Sarcasm" "Negative" "Very Negative" "Neutral" "Positive" "Very Positive"

[2,] "0“ "3” "0“ "0“ "1“ "0"

calculate_sentiment

calculate_sentiment(c("This is a good text",

"This is a bad text",

"This is a really bad text", "This is horrible"))

#output

[1] "Processing sentence: this is a good text"

[1] "Processing sentence: this is a bad text"

[1] "Processing sentence: this is a really bad text"

[1] "Processing sentence: this is horrible"

text sentiment

1 This is a good text Positive

2 This is a bad text Negative

3 This is a really bad text Negative

4 This is horrible Negative

Calculating Sentiment Score

calculate_score(c("This is a good text",

"This is a bad text",

"This is a really bad text",

"This is horrible"))

[1] "Processing sentence: this is a good text"

[1] "Processing sentence: this is a bad text"

[1] "Processing sentence: this is a really bad text"

[1] "Processing sentence: this is horrible"

[1] 1 -1 -1 -1

Text Mining and NLP More Examples

String Matching in R Programming

- String matching is an important aspect of any language. It is useful in finding, replacing as well as removing string(s)

- A regular expression is a string that contains special symbols and characters to find and extract the information needed from the given data.

- Operations on String Matching

• Finding a String

• grep() function: It returns the index at which the pattern is found in the vector.

• grep(pattern, string, ignore.case=FALSE)

• str <- c("Man", "woman","baby", "amman", "happy")

• grep('man', str)

grep('man', str)

output: 2 4

- str <- c("Man", "woman","baby", "amman", "happy")

- grep('man', str, ignore.case ="True")

grep('man', str, ignore.case ="True")

Output: 1 2 4

grepl() function: It is a logical function that returns the value True if the specified pattern is found in the vector and false if it is not found.

Syntax

grepl(pattern, string, ignore.case=FALSE)

To find whether any instance(s) of ‘the’ are present in the string.

str <- c("Man", "woman","baby", "amman", "happy")

grepl('the', str)

output: FALSE FALSE FALSE FALSE FALSE

grepl('wo', str)

FALSE TRUE FALSE FALSE FALSE

regexpr() function: It searches for occurrences of a pattern in every element of the string.

Syntax: regexpr(pattern, string, ignore.case = FALSE)

example: To find whether any instance(s) of ‘he’ is present in each string of the vector.

str <- c("Hello", "hello", "hi", "ahey", "aahead")

regexpr('he', str)

regexpr('he', str)

output: -1 1 -1 2 3

example: To find whether any instance(s) of words starting with a vowel is present in each string of the vector.

str <- c("abra", "Ubra", "hunt", "quirky")

regexpr('^[aeiouAEIOU]', str)

regexpr('^[aeiouAEIOU]', str)

output: 1 1 -1 -1

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