Text Summarization and Sentiment Analysis Using Python Libraries
1. Text Summarization with spaCy
Text summarization is a process whereby the main concepts that serve to explain the text are extracted from the blocks of text. Since text summarization involves indexing and document frequency measures, it can be best done using the spacy that is a well-known Python module. Here’s an example of how spacy can be used for summarization:
!python -m spacy download en_core_web_lg
!pip install pytextrank
import spacy
import pytextrank
# Load the Spacy model
nlp = spacy.load("en_core_web_lg")
# Add PyTextRank to the pipeline
nlp.add_pipe("textrank")
example_text = """
Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and human language. It involves the development of algorithms and models that enable machines to understand, interpret, and generate human language in a way that is both meaningful and useful. NLP has become a cornerstone of modern data science and technology, driving advancements in various fields such as customer service, healthcare, finance, and more.
"""
print('Original Document Size:', len(example_text))
doc = nlp(example_text)
for sent in doc._.textrank.summary(limit_phrases=2, limit_sentences=2):
print(sent)
print('Summary Length:', len(sent))
Output:
Original Document Size: 488Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and human language.Summary Length: 27It involves the development of algorithms and models that enable machines to understand, interpret, and generate human language in a way that is both meaningful and useful.
2. Sentiment Analysis on Social Media with Python
Sentiment Analysis refers to the process of establishing in which direction the emotion is being geared, in a given sequence of words. It is often applied in analyzing sentiments on a particular topic or brand, particularly on social media. Here’s a Python code sample using the TextBlob library for sentiment analysis:
from textblob import TextBlob
tweet = "I love the new features in the latest update!"
blob = TextBlob(tweet)
sentiment = blob.sentiment
print(f"Sentiment: {sentiment}")
Output:
Sentiment: Sentiment(polarity=0.4204545454545454, subjectivity=0.6515151515151515)
3. Building a Basic Chatbot with NLTK
Python now includes a very robust and useful tool for the development of NLP applications, known as NLTK. Below is a simple example of a rule-based chatbot using NLTK:
import nltk
from nltk.chat.util import Chat, reflections
pairs = [
["hi|hello", ["Hello!", "Hi there!"]],
["how are you?", ["I'm doing well, how about you?"]],
["quit", ["Bye! Take care."]]
]
chatbot = Chat(pairs, reflections)
chatbot.converse()
Output:
>HiHi there!>how are you?I'm doing well, how about you?>byeNone>quitBye! Take care.
Unleashing the Power of Natural Language Processing
Natural Language Processing (NLP) is a transformative technology that bridges the gap between human communication and computer understanding. As a subfield of artificial intelligence (AI) and computational linguistics, NLP enables machines to understand, interpret, and generate human language in a meaningful way.
Power of Natural Language Processing
This article delves into the intricacies of NLP, its key components, challenges, and diverse applications across various industries.
Table of Content
- Introduction to Natural Language Processing
- Text Summarization and Sentiment Analysis Using Python Libraries
- 1. Text Summarization with spaCy
- 2. Sentiment Analysis on Social Media with Python
- 3. Building a Basic Chatbot with NLTK
- Challenges in Natural Language Processing
- Applications of Natural Language Processing
- The Future of NLP