Exploring the Power of Hugging Face Transformers for Sentiment Analysis

Introduction:

The study of processing and comprehending human language is known as natural language processing or NLP. NLP has grown in importance as a tool for organizations to monitor customer sentiment, product reviews, and feedback with the emergence of social media platforms. Understanding the meaning and context of words in a phrase is one of the significant issues in NLP. Hugging Face Transformers can be helpful in this situation. A cutting-edge NLP package called Hugging Face Transformers employs deep learning techniques to examine and comprehend the context of natural language text. There are various applications of HuggingFace Transformers like Sentiment Analysis, Text Classification, Named Entity Recognition, Language Translation, Question Answering, Text Summarization, Language Modeling, and Text Generation to name a few. In this blog, we'll look into using hugging face transformers to analyze sentiment.

Application:

Sentiment analysis is a widespread use of NLP that includes identifying the text's emotional tone. Hugging Face Transformers will be used in this article to categorize movie reviews as either excellent or unfavorable. In the next section, we will see the usage of Hugging Face to perform sentiment analysis operations.

Code Implementation:

First, we need to install the transformers library. We can do this using the following command:

!pip3 install transformers

This installs the library.

Next, we need to import the necessary libraries:

import torch
from transformers import pipeline

Now, let's load the sentiment analysis pipeline using Hugging Face Transformers:

classifier = pipeline('sentiment-analysis')

We can now use the classifier to analyze the sentiment of a piece of text:

result = classifier("I kinda liked those T-shirts on amazon but myntra has better collection.")
print(result)

The output will be a dictionary containing the predicted sentiment and confidence score:

[{'label': 'POSITIVE', 'score': 0.9547143578529358}]

We can also analyze the sentiment of multiple sentences at once:

results = classifier(["I kinda liked those T-shirts on amazon but myntra has better collection.", 
"Those trousers I bought were unthinkably horrible! Never buy them, ever!"])
print(results)

The output will be a list of dictionaries, where each dictionary contains the predicted sentiment and confidence score for a sentence:

[{'label': 'POSITIVE', 'score': 0.9547143578529358}, {'label': 'NEGATIVE', 'score': 0.9995360374450684}]

Conclusion:

We have looked at the potential of hugging face transformers for sentiment analysis in this post. We loaded a pre-trained sentiment analysis pipeline using the transformers library and showed how to use it to categorize product reviews as positive or negative. Hugging Face Transformers is a potent NLP library with many applications, so it's definitely worth looking into further.

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Github: https://github.com/abhigyan631