Sentiment Analysis with BERT Neural Network and Python

Nicholas Renotte
27 May 202131:56
32 Likes 10 Comments

TLDRThis video tutorial demonstrates how to perform sentiment analysis using a state-of-the-art model called Vert, facilitated by the transformers package. It covers the installation of necessary libraries, the use of a pre-trained Vert model for sentiment scoring, and the practical application of scraping Yelp reviews for sentiment analysis. The process includes encoding text, calculating sentiment scores, and storing results in a pandas dataframe, offering a comprehensive guide for users to analyze and understand sentiments from text data.

  • ๐Ÿค– Introduction to sentiment analysis using a state-of-the-art model called Vert.
  • ๐Ÿ“ฆ Installation of the transformers library, which is essential for NLP tasks including sentiment analysis.
  • ๐Ÿง  Utilization of a pre-trained Vert model from Hugging Face's Transformers for sentiment scoring.
  • ๐Ÿ” Sentiment scoring on text with the model providing a rating scale from one to five, akin to star ratings.
  • ๐ŸŒ Web scraping of Yelp reviews for practical implementation of sentiment analysis.
  • ๐Ÿ“ˆ Data structuring and analysis using Beautiful Soup, pandas, and other Python libraries.
  • ๐Ÿ”ข Limitation on the number of tokens (512) that can be processed by the NLP pipeline at a time, requiring workarounds for longer texts.
  • ๐Ÿ“Š Creation of a function to streamline the sentiment analysis process for individual reviews.
  • ๐Ÿ”„ Application of the sentiment analysis function to a dataset of reviews using pandas' apply and lambda functions.
  • ๐Ÿ› ๏ธ Adaptability of the sentiment analysis pipeline to different businesses or datasets by changing the source link.
  • ๐ŸŽ“ Comprehensive tutorial providing step-by-step guidance on performing sentiment analysis from model setup to data scoring.
Q & A
  • What is the main focus of the video?

    -The main focus of the video is to demonstrate how to perform sentiment analysis on text data using a pre-trained model called Vert from the Hugging Face Transformers library.

  • What library is used for performing sentiment analysis in the video?

    -The Transformers library from Hugging Face is used for performing sentiment analysis in the video.

  • How does the Vert model provide sentiment scores?

    -The Vert model provides sentiment scores between one and five, mimicking a star rating system, where a higher number indicates better sentiment.

  • What is the first step in setting up the sentiment analysis model?

    -The first step is to install the Transformers library, which is particularly useful for natural language processing tasks like sentiment analysis.

  • How are Yelp reviews collected for sentiment analysis?

    -Yelp reviews are collected by using the Requests library to make a request to the Yelp site, and then Beautiful Soup is used to extract the reviews from the HTML content.

  • What is the role of the Pandas library in this process?

    -The Pandas library is used to structure and store the collected Yelp reviews in a data frame, making it easier to process and analyze the data.

  • How is the sentiment score calculated from the model's output?

    -The sentiment score is calculated by using the torch.argmax function on the model's output (logits) to extract the highest value, which corresponds to the sentiment class.

  • What is the significance of the tokenization step in the sentiment analysis process?

    -Tokenization is significant because it converts the input text into a sequence of numbers that the model can understand and process for sentiment analysis.

  • How does the video demonstrate the practical application of the sentiment analysis pipeline?

    -The video demonstrates a practical application by scraping Yelp reviews, loading them into a data frame, and then running the sentiment analysis model on each review to score them.

  • What is the limitation of the NLP pipeline when processing text?

    -The limitation of the NLP pipeline is that it can only process up to 512 tokens at a time, which may affect the results if the reviews are longer.

  • How can the sentiment analysis process be applied to different types of reviews or businesses?

    -The sentiment analysis process can be applied to different types of reviews or businesses by changing the URL in the request to the desired business page on Yelp and following the same scraping and analysis steps.

๐Ÿค– Introduction to Sentiment Analysis with State-of-the-Art Model

This paragraph introduces the concept of sentiment analysis and outlines the plan to use a state-of-the-art model called Vert for the task. It highlights the ease of implementation using the Transformers package and sets the stage for exploring sentiment scoring on text data. The paragraph also mentions the exciting aspect of scraping Yelp reviews to apply the sentiment analysis pipeline, demonstrating its practical application.

๐Ÿ› ๏ธ Setting Up the Sentiment Analysis Model

The paragraph delves into the technical setup required for the sentiment analysis. It explains the installation of the Transformers library, which is crucial for NLP tasks like sentiment analysis. The process of performing sentiment scoring with a pre-trained Vert model is discussed, emphasizing the simplicity of the approach. The paragraph also touches on the plan to encode and calculate sentiment from text, leveraging the model for practical implementation, including web scraping with BeautifulSoup.

๐ŸŒ Multilingual Sentiment Analysis and Installation of Dependencies

This section highlights the versatility of the chosen model, which supports multiple languages for sentiment analysis. It provides an overview of the installation process for the necessary libraries, including transformers, requests, BeautifulSoup, pandas, and numpy. The paragraph explains the role of each library in the sentiment analysis pipeline and promotes an analytics tool called Mido for data manipulation in Jupyter notebooks.

๐Ÿ”ง Importing Dependencies and Model Instantiation

The paragraph focuses on the practical steps of importing the installed dependencies and setting up the pre-trained NLP model. It details the import statements for the tokenizer and model class from the Transformers library, as well as the necessary imports for PyTorch, requests, BeautifulSoup, and regex. The process of loading the model and tokenizer with specific URLs is also described, providing a clear guide for replication.

๐Ÿ“Š Testing the Sentiment Analysis Model

This part of the script describes the testing of the sentiment analysis model. It explains the process of tokenizing a string and passing it through the model to obtain a sentiment score. The paragraph demonstrates how to decode the tokens back to text, encode the prompt, and interpret the output as a sentiment score on a scale from one to five. It also shows how to extract the highest sentiment value using the argmax function from PyTorch.

๐ŸŒŸ Collecting Reviews and Applying Sentiment Analysis

The paragraph discusses the collection of reviews from Yelp using web scraping techniques with requests and BeautifulSoup. It explains how to extract specific review comments using regex and store them in a pandas DataFrame. The section also covers the process of looping through the reviews, applying the sentiment analysis model, and storing the sentiment scores in a new column within the DataFrame. The practical application of the sentiment analysis on real-world data is demonstrated, showcasing its potential for businesses.

๐ŸŽ‰ Conclusion and Further Applications

The final paragraph wraps up the tutorial by summarizing the steps taken, from installing dependencies and setting up the model to collecting reviews and performing sentiment analysis. It encourages viewers to engage with the content, seek help if needed, and explore further applications of the sentiment analysis pipeline on different datasets or businesses. The paragraph ends with a call to action for viewers to like, subscribe, and interact with the content.

๐Ÿ’กSentiment Analysis
Sentiment analysis is the process of determining the emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions, and emotions expressed within a text. In the video, sentiment analysis is the main focus, where the presenter uses a pre-trained model to analyze sentiments from text data, such as Yelp reviews, and assigns a rating scale from one to five based on the perceived sentiment.
๐Ÿ’กTransformers Package
The Transformers package is a Python library developed by Hugging Face, which provides a general-purpose framework for natural language processing (NLP). It includes a wide range of pre-trained models that can be fine-tuned for specific tasks, such as sentiment analysis. In the video, the presenter uses this package to easily implement and run a sentiment analysis model without extensive coding or model training.
๐Ÿ’กPre-trained Model
A pre-trained model is a machine learning model that has already been trained on a large dataset and can be used or fine-tuned for specific tasks. These models save time and computational resources as they do not requireไปŽๅคดๅผ€ๅง‹่ฎญ็ปƒ. In the context of the video, a pre-trained model for sentiment analysis is used to score sentiments of text inputs.
๐Ÿ’กYelp Reviews
Yelp reviews are customer-generated feedback and ratings posted on the Yelp platform, which is a popular business review site. These reviews are a valuable source of data for sentiment analysis as they contain subjective opinions and experiences of customers. In the video, Yelp reviews are used as a dataset for practicing and demonstrating sentiment analysis.
๐Ÿ’กPandas DataFrame
A Pandas DataFrame is a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns) in the Pandas library of Python. It is widely used for data manipulation and analysis. In the video, a DataFrame is used to store and process the collected Yelp reviews, making it easier to apply the sentiment analysis model to each review.
BeautifulSoup is a Python library used for parsing HTML and XML documents, which allows for easy navigation, searching, and modifying of the parse tree. It is commonly used for web scraping tasks, where it helps in extracting data from web pages. In the video, BeautifulSoup is used to extract review texts from the Yelp website's HTML structure.
๐Ÿ’กSentiment Score
A sentiment score is a numerical value assigned to a piece of text after sentiment analysis, representing the degree of positivity, neutrality, or negativity expressed in the text. Scores are often on a scale, with higher values indicating more positive sentiment and lower values indicating more negative sentiment. In the video, sentiment scores are given as integers between one and five, with five being the most positive.
๐Ÿ’กNatural Language Processing (NLP)
Natural Language Processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human (natural) languages. It involves the development of algorithms and computational models that can understand, interpret, and generate human language in a way that is both meaningful and useful. The video's main theme of sentiment analysis is an application of NLP.
๐Ÿ’กWeb Scraping
Web scraping is the process of extracting data from websites, typically by parsing HTML or JSON files. It involves using various tools and libraries to navigate through the structure of web pages, extract the desired information, and store it for further analysis or processing. In the video, web scraping is performed to collect Yelp reviews for sentiment analysis.
๐Ÿ’กData Transformation
Data transformation refers to the process of converting data from one format or structure into another to make it suitable for analysis or processing. This often involves cleaning, normalizing, or restructuring the data to fit the requirements of a specific task or system. In the video, data transformation is an essential step in preparing the Yelp reviews for sentiment analysis.

The video covers sentiment analysis using a state-of-the-art model called Vert.

The process is simplified using the Transformers package.

The video demonstrates how to perform sentiment scoring on text using a pre-trained Vert model.

The tutorial includes scraping data and reviews from Yelp.

The pre-trained Vert model is downloaded and installed from Hugging Face's Transformers library.

The model provides sentiment scores on a scale from one to five, mimicking star ratings.

The video outlines the steps to install and import necessary dependencies like PyTorch, Transformers, Requests, Beautiful Soup, Pandas, and NumPy.

The process of encoding and calculating sentiment from text is detailed, including handling the model's token limit.

A practical implementation is shown by scraping Yelp reviews and applying sentiment analysis to the collected data.

The video provides a method to convert sentiment scores into a binary value or an integer.

The tutorial demonstrates how to handle different languages for sentiment analysis using the multilingual capabilities of the Vert model.

The video includes a step-by-step guide on how to install PyTorch, including selecting the appropriate build for different operating systems.

The video explains the use of Beautiful Soup for web scraping and extracting needed data from web pages.

The tutorial shows how to structure and process data using Pandas for further analysis.

The video concludes by demonstrating how to apply the sentiment analysis pipeline on multiple reviews and businesses.

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