Gain insights into text preprocessing with Python. Explore text cleaning, normalization, and advanced techniques like BoW and TF-IDF. Discover skills to handle unstructured data effectively for NLP.

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Text Processing

This course is designed to empower you with essential skills for effectively handling text data in the context of natural language processing (NLP). You’ll embark on a transformative journey that will equip you with a solid foundation in text manipulation, enabling you to tackle the challenges of unstructured data. 

The course discusses both fundamental and advanced text preprocessing techniques. You’ll learn how to clean text and remove noise, irrelevant characters, and inconsistencies in text data. Once the data is ready for analysis, you’ll learn text normalization techniques such as stemming, lemmatization, and casing. In addition to mastering preprocessing fundamentals, you’ll also learn techniques such as bag-of-words (BoW) and term frequency-inverse document frequency (TF-IDF).

By the end of the course, you’ll be able to position yourself for success in a data-centric world where the ability to extract meaning from unstructured textual information is a prized skill.

Text Preprocessing with Python

Words like "run" have only one possible part of speech.

The grammatical structure of such words is too complicated.

These words can have multiple possible parts of speech depending on the context.

Part-of-speech tagging does not work well with verbs.

What challenge does part-of-speech tagging face when dealing with words like "run"?

What challenge does part-of-speech tagging face when dealing with words like “run”?

In the sentence "The cat sat on the mat," what's the part-of-speech tag for "mat"?

In the sentence “The cat sat on the mat,” what’s the part-of-speech tag for “mat”?

Common nouns, possessive pronouns, proper pronouns

Predicate adjectives, participial adjectives, determiners

Which are examples of named entities we can identify using NER?

Which are examples of named entities we can identify using NER?

To ensure accurate entity extraction and to adapt to variations in text

To reduce the number of categories recognized by NER

To maintain a fixed set of recognized named entity categories

Why's it important to regularly evaluate and iteratively improve NER techniques?

Why’s it important to regularly evaluate and iteratively improve NER techniques?

A numerical representation of text content

A categorical variable that we want to predict

A measure of the length of a text document

In the context of text classification, what is a label?

In the context of text classification, what is a label?

```
nltk.download('punkt', quiet=True)
```

```
df.to_csv("processed_reviews.csv", index=False)
```

```
df['pos_tags'] = df['tokens'].apply(nltk.pos_tag)
```

```
df['pos_tags'] = df['tokens'].apply(word_tokenize)
```

What's missing in the following code that performs part-of-speech tagging?
```
import pandas as pd
import nltk
import string 
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
nltk.download('averaged_perceptron_tagger', quiet=True)

df = pd.read_csv("reviews.csv")  
df['tokens'] = df['text'].apply(lambda text: word_tokenize(text.lower()))
stop_words = set(stopwords.words('english'))
df['tokens'] = df['tokens'].apply(lambda tokens: [token for token in tokens if token not in stop_words])  
print(df.head())

```

What’s missing in the following code that performs part-of-speech tagging?
<pre><code>import pandas as pd
import nltk
import string 
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
nltk.download('averaged_perceptron_tagger', quiet=True)

df = pd.read_csv(&quot;reviews.csv&quot;) 
df['tokens'] = df['text'].apply(lambda text: word_tokenize(text.lower()))
stop_words = set(stopwords.words('english'))
df['tokens'] = df['tokens'].apply(lambda tokens: [token for token in tokens if token not in stop_words]) 
print(df.head())

</code></pre>

The `extract_entities()` function is not defined correctly.

The code uses the incorrect syntax for loading the spaCy model.

The code does not apply the `extract_entities()` function to the `text` column.

What's wrong with the following code that performs NER tagging?
```
import pandas as pd
import spacy
nlp = spacy.load("en_core_web_sm")

df = pd.read_csv("reviews.csv")
def extract_entities(text):
    doc = nlp(text)
    entities = [(ent.text, ent.label_) for ent in doc.ents]
    return entities
df['ner_tags'] = df['text'].apply()
print(df['ner_tags'])
```

What’s wrong with the following code that performs NER tagging?
<pre><code>import pandas as pd
import spacy
nlp = spacy.load(&quot;en_core_web_sm&quot;)

df = pd.read_csv(&quot;reviews.csv&quot;)
def extract_entities(text):
 doc = nlp(text)
 entities = [(ent.text, ent.label_) for ent in doc.ents]
 return entities
df['ner_tags'] = df['text'].apply()
print(df['ner_tags'])
</code></pre>

Test yourself on what you’ve learned about advanced text preprocessing.

About This Course

Introduction To Text Preprocessing

Regular Expressions

Irrelevant Text Data

Basic Text Preprocessing Techniques

Indexing

Text Transformation

Text Representation

Text Feature Engineering

Advanced Text Preprocessing

N-grams

Text Classification of Customer Reviews

Conclusion

Quiz: Advanced Text Preprocessing