Unveiling Linguistics with SpaCy

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In the world of language analysis, SpaCy allows us to dissect the linguistic features. Let’s explore some texts using SpaCy’s capabilities.

!pip install spacy
!python -m spacy download en_core_web_sm

import spacy

# Load the English language model in SpaCy
nlp = spacy.load('en_core_web_sm')

# Texts
text_1 = "Salim, a proficient programmer, designs innovative algorithms and develops software solutions, streamlining complex processes for tech companies."
text_2 = "Saska, a cybersecurity expert, meticulously fortifies networks, ensuring data integrity and safeguarding against cyber threats for large corporations."

# Process Text 1 and Text 2
doc_1 = nlp(text_1)
doc_2 = nlp(text_2)

# Function to display semantic analysis results
def display_analysis(doc):
    print("Tokenization & Part-of-Speech Tagging:")
    for token in doc:
        print(f"Token: {token.text}, POS: {token.pos_}")

    print("\nNamed Entity Recognition (NER):")
    for ent in doc.ents:
        print(f"Entity: {ent.text}, Label: {ent.label_}")

# Analyzing Text 1
print("Analysis of Text 1:")
display_analysis(doc_1)
print("\n----------------\n")

# Analyzing Text 2
print("Analysis of Text 2:")
display_analysis(doc_2)

This concise piece provides a snippet of code, analysis insights, and a brief conclusion on how SpaCy helps uncover the distinct linguistic traits of texts.