Program 6:
Aim:
Use
a pre-trained Hugging Face model to analyze sentiment in text. Assume a
real-world application, Load the sentiment analysis pipeline. Analyze the
sentiment by giving sentences to input.
Theory:
Sentiment
Analysis using a Pre-trained Hugging Face Model
Sentiment
analysis is a Natural Language Processing (NLP) technique used to determine the
emotional tone of a piece of text. It helps identify whether the text expresses
a positive, negative, or neutral opinion.
In
real-world applications, sentiment analysis is widely used to analyze customer
reviews, social media posts, product feedback, and survey responses. Companies
use it to understand customer satisfaction and public opinion about their
products or services.
Hugging
Face Transformers provides many pre-trained language models that can perform
sentiment analysis without training a model from scratch. These models are
trained on large text datasets and can quickly classify the sentiment of new
text.
The
Hugging Face pipeline is a simple interface that allows users to apply NLP
tasks such as sentiment analysis with just a few lines of code. By loading the
sentiment-analysis pipeline, we can input sentences and the model will predict
whether the sentiment is positive or negative, along with a confidence score.
Thus,
using a pre-trained Hugging Face model makes sentiment analysis fast,
efficient, and easy to implement in real-world applications.
Program:
1.
Import Required Library
First
import the Hugging Face pipeline from the transformers library.
2.
Load the Pre-trained Sentiment Analysis Model
Load
the sentiment analysis pipeline.
Hugging
Face automatically loads a pre-trained model (DistilBERT) that is trained to
classify text as POSITIVE or NEGATIVE.
3.
Provide Input Sentences
Give
some sentences as input for analysis.
4.
Perform Sentiment Analysis
Pass
the sentences to the pipeline.
5.
Display the Results
Print
the sentiment and confidence score.
6.
Output
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