Added code for assignment A7 (text analytics) and linked in README.

Codes/Code-A7 (Text Analytics).md

@@ -0,0 +1,157 @@
+# A10 - Data Visualization-3
+
+---
+
+## Pre-requisites
+
+- In the same directory as this Jupyter notebook, create a text file (eg. simple.txt) that contains some random text.
+
+---
+
+1. Import libraries
+
+```python3
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import nltk
+```
+
+2. Open a text file and download all NLTK resources
+
+```python3
+file=open('simple.txt','r')
+nltk.download('all')
+print(file)
+```
+
+3. Read the content of the opened text file
+
+```python3
+content=file.read()
+print(content)
+```
+
+4. Import the sentence tokenizer from the NLTK library
+
+```python3
+from nltk.tokenize import sent_tokenize
+```
+
+5. Tokenize the content into sentences and print the result
+
+```python3
+sentence=sent_tokenize(content)
+print(sentence)
+```
+
+6. Use a regular expression tokenizer to extract words from the content
+
+```python3
+from nltk.tokenize import RegexpTokenizer
+tokenizer=RegexpTokenizer(f"\w+")
+words=tokenizer.tokenize(content)
+print(words)
+```
+
+7. Use a regular expression tokenizer to extract whitespace from the content
+
+```python3
+tokenizer=RegexpTokenizer(f"\s")
+words=tokenizer.tokenize(content)
+print(words)
+```
+
+8. Import stopwords and word tokenizer from the NLTK library
+
+```python3
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+```
+
+9. Retrieve and print the set of English stopwords
+
+```python3
+stopWords=set(stopwords.words('english'))
+print(stopWords)
+```
+
+10. Tokenize each sentence, filter out stopwords, and perform POS tagging on the filtered words
+
+```python3
+for sen in sentence:
+	Words=word_tokenize(sen)
+	filteredWords=[word.lower() for word in Words if word.lower() not in stopWords]
+	print(f"words without stopwords{filteredWords}")
+	print(f"words with stopwords{Words}")
+	print(f"POS Tagging{nltk.pos_tag(filteredWords)}")
+```
+
+11. Print the POS tagging of the filtered words again (redundant)
+
+```python3
+print(f"POS Tagging{nltk.pos_tag(filteredWords)}")
+```
+
+12. Import stemming and lemmatization tools from the NLTK library
+
+```python3
+from nltk.stem import PorterStemmer
+from nltk.stem import WordNetLemmatizer
+```
+
+13. Apply stemming to each word and print the original and stemmed forms
+
+```python3
+stemmer=PorterStemmer()
+for word in Words:
+	print(f"{word}- After Stemming = {stemmer.stem(word)}")
+```
+
+14. Apply lemmatization to each word and print the original and lemmatized forms
+
+```python3
+lemmatizer=WordNetLemmatizer()
+for word in Words:
+	print(f"{word}:{lemmatizer.lemmatize(word)}")
+```
+
+15. Create a new sentence by joining the first three sentences from the original content
+
+```python3
+sentence=sentence[:3]
+new_sentence=[''.join(sentence)]
+new_sentence
+```
+
+16. Import the TfidfVectorizer from the sklearn library for text feature extraction
+
+```python3
+from sklearn.feature_extraction.text import TfidfVectorizer
+```
+
+17. Define a function to calculate the TF-IDF matrix and feature names from a document
+
+```python3
+def calculate_tfIdf(document):
+	tokenizer=TfidfVectorizer()
+	tf_matrix=tokenizer.fit_transform(document)
+	features_names=tokenizer.get_feature_names_out()
+	return tf_matrix,features_names
+```
+
+18. Assign the newly created sentence to the document variable for TF-IDF calculation
+
+```python3
+document=new_sentence
+```
+
+19. Calculate and print the TF-IDF matrix and feature names for the document
+
+```python3
+tf_matrix,feature_names=calculate_tfIdf(new_sentence)
+print('TFIDF')
+feature_names,tf_matrix.toarray()
+```
+
+---

README.md

@@ -17,8 +17,9 @@
 
 
 1. [Code-A5 (Data Analytics-2)](Codes/Code-A5%20%28Data%20Analytics-2%29.md)
-2. [Code-A9 (Data Visualisation-2)](Codes/Code-A9%20%28Data%20Visualisation-2%29.md)
-3. [Code-A10 (Data Visualisation-3)](Codes/Code-A10%20%28Data%20Visualisation-3%29.md)
+2. [Code-A7 (Text Analytics)](Codes/Code-A7%20%28Text%20Analytics%29.md)
+3. [Code-A9 (Data Visualisation-2)](Codes/Code-A9%20%28Data%20Visualisation-2%29.md)
+4. [Code-A10 (Data Visualisation-3)](Codes/Code-A10%20%28Data%20Visualisation-3%29.md)
 
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