Aspect Based Sentiment Analysis: A Python Demo

Decision Analytics
23 May 202209:26
EducationalLearning
32 Likes 10 Comments

TLDRThis educational video dives into the fascinating world of Aspect-Based Sentiment Analysis (ABSA), a nuanced approach to understanding opinions within text. Starting with an engaging example, it demystifies ABSA by illustrating how it discerns aspects, opinions, and sentiments from a restaurant review. The presentation progresses to explain ABSA's terminology, its advantages over traditional sentiment analysis, and showcases expected outputs using real-world data. A detailed walkthrough of a seminal paper on ABSA introduces viewers to model architecture, data processing, and the extraction of sentiment triplets. The video concludes with a Python demo on Google Colab, demonstrating the model's application to different examples, underscoring its domain-specific effectiveness, and guiding viewers on training models with custom datasets.

Takeaways
  • 📝 Aspect-Based Sentiment Analysis (ABSA) is a method to analyze sentiments directed towards specific aspects of a product or service.
  • 🔍 ABSA provides more detailed insights compared to traditional sentiment analysis by identifying particular aspects people like or dislike and the reasons behind their sentiments.
  • 🍴 In the given example, 'waiters' and 'pasta' are aspects, while 'friendly' and 'average' represent subjective opinions tied to these aspects with corresponding sentiments.
  • 📊 ABSA outputs structured data that can be visualized to show the frequency of mentions and associated sentiments for different aspects.
  • 🤖 The model 'learns' to identify aspects (targets), opinions, and sentiments by breaking sentences into tokens and forming relevant spans.
  • 🌳 The process includes aspect term extraction, opinion term extraction, and classification to reduce computational complexity and focus on valid aspects and opinions.
  • 🔗 Aspects and opinions are paired and classified to determine the sentiment relationship, which can be positive, negative, or neutral.
  • 📈 The paper 'Learning Span Level Interactions for Aspect Sentiment Triplet Extraction' demonstrates a model that significantly improves performance in ABSA tasks.
  • 🏗️ The model's architecture includes sentence tokenization, span formation, pruning, aspect and opinion classification, and sentiment relation classification.
  • 💻 The authors have made the code for their model publicly available on GitHub, and a Python demo is provided on Google Colab for practical understanding.
  • 📚 ABSA models can be trained from scratch using labeled training data and evaluated using metrics like the F1 score.
Q & A

  • What is Aspect-Based Sentiment Analysis (ABSA)?

    -Aspect-Based Sentiment Analysis (ABSA) is a method of analyzing sentiments that focuses on identifying specific aspects or targets within a given product or service and determining the sentiment associated with each aspect. It provides more detailed insights compared to traditional sentiment analysis by extracting what aspects people are discussing and whether they like or dislike each aspect and the reasons behind it.

  • What are the components of an ABSA output?

    -An ABSA output typically consists of structured data that includes aspects (also known as targets), opinions (subjective opinions about the aspects), and sentiments (positive, negative, or neutral feelings expressed towards the aspects).

  • How does ABSA handle multiple aspects in a single review?

    -If a review contains too many aspects, ABSA can employ clustering and dimensionality reduction techniques to group similar aspects together. This helps in managing and understanding the data more effectively.

  • What is the significance of aspect term extraction and opinion term extraction in ABSA?

    -Aspect term extraction and opinion term extraction are crucial steps in the ABSA process. They involve identifying and classifying spans (phrases) as either aspects, opinions, or invalid. This pruning operation helps in filtering out irrelevant information and retaining only the candidate aspects or targets and opinions, which are essential for determining sentiment relationships.

  • How does the model architecture in the discussed paper handle sentence analysis?

    -The model architecture breaks a sentence into tokens, combines these tokens to form spans (phrases), and then classifies each span as invalid, an opinion, or a target (aspect). This process helps in identifying aspect-opinion pairs and determining the sentiment associated with each pair.

  • What is the role of the sentiment relation classifier in ABSA?

    -The sentiment relation classifier in ABSA is responsible for determining whether each aspect-opinion pair conveys a positive, negative, or neutral sentiment. It can also identify if a pair is invalid, such as when the aspect is a fact rather than a subjective opinion, and therefore, no sentiment relationship exists.

  • How does the model handle computational complexity?

    -The model controls computational complexity by performing aspect term extraction and opinion term extraction, which prunes the number of possible spans. This is necessary because the number of potential spans grows exponentially with the length of the sentence.

  • What are the different training and testing sets used in the paper?

    -The paper uses four different training and testing sets, namely Rest 14 (restaurant reviews from 2014) and Lab 14 (laptop reviews from 2014). These datasets were originally released by the Semantic Evaluation Workshop.

  • How can one evaluate the performance of an ABSA model?

    -The performance of an ABSA model can be evaluated using metrics such as F1 score, which measures the model's precision and recall in identifying aspect-opinion pairs and their associated sentiments accurately.

  • Is it possible to retrain the ABSA model on new data?

    -Yes, the ABSA model can be retrained from scratch using your own labeled training data. This allows for the model to be adapted to specific domains or datasets, improving its ability to detect sentiment aspect triplets in the new context.

  • Where can one find the code and resources for the ABSA model discussed in the paper?

    -The authors of the paper have made their code publicly available on GitHub, and there is a Python demo available on Google Colab that demonstrates how to use the model for predicting sentiment aspect triplets.

Outlines
00:00
📚 Introduction to Aspect-Based Sentiment Analysis (ABSA)

This paragraph introduces aspect-based sentiment analysis (ABSA), a method for extracting sentiment from reviews and opinions. It explains how ABSA can identify specific aspects (like 'waiters' and 'pasta' from a restaurant review) and the associated sentiments (positive or negative). The paragraph also outlines the process of ABSA, starting with an example of a restaurant review and discussing the terminology used in the field. It emphasizes the benefits of ABSA over traditional sentiment analysis, such as providing more detailed insights into customer opinions on specific aspects of a product or service.

05:00
📈 Understanding ABSA Outputs and Model Architecture

This paragraph delves into the expected outputs of ABSA and the architecture of a specific model designed for aspect sentiment triplet extraction. It describes how ABSA can process a large number of reviews to identify mentions and sentiments associated with different aspects. The paragraph walks through a hypothetical example of analyzing restaurant reviews and introduces the concept of clustering and dimension reduction for managing numerous aspects. It also explains the model's architecture, including tokenization, span formation, aspect and opinion term extraction, and sentiment relation classification. The performance of the model is discussed, along with its training and testing on various datasets.

Mindmap
Keywords
💡Aspect-Based Sentiment Analysis (ABSA)
Aspect-Based Sentiment Analysis, also known as ABSA, is a method used in natural language processing and text analytics to identify and extract subjective information from a given text. It focuses on analyzing specific aspects or targets, such as 'waiters' or 'pasta' in a restaurant review, and associates them with corresponding opinions and sentiments. ABSA provides more detailed insights compared to traditional sentiment analysis by pinpointing exactly which aspects of a product or service people like or dislike and the reasons behind their sentiments.
💡Structured Data
Structured data refers to information that is organized in a specific format, making it easier to process and analyze. In the context of ABSA, structured data would be the output that shows the aspects, opinions, and sentiments in an organized manner, often in tabular form. This allows for a clear visualization of the sentiments associated with different aspects of a product or service, facilitating further analysis and decision-making.
💡Aspects or Targets
Aspects or targets are specific elements or features of a product, service, or topic that are being discussed or reviewed. In sentiment analysis, these aspects are the focus of the sentiment expressed by the reviewer. Identifying aspects is crucial for understanding the detailed sentiment towards different parts of a product or service. For example, in a restaurant review, 'waiters' and 'pasta' are aspects that customers may express opinions about.
💡Opinions
Opinions are the subjective evaluations or views expressed by individuals about various aspects of a product, service, or topic. In the context of ABSA, opinions are the descriptive phrases or words that provide insight into the sentiment held towards an aspect. They form the basis for determining whether the sentiment is positive, negative, or neutral.
💡Sentiment
Sentiment refers to the emotional response or attitude expressed within a piece of text towards a particular aspect or target. It is typically categorized as positive, negative, or neutral. Sentiment analysis aims to quantify this emotional response in order to understand the overall opinion or feeling towards the subject at hand.
💡Span
In the context of natural language processing, a span is a sequence of words or tokens within a sentence that represent a particular aspect or opinion. Spans are used in ABSA to identify phrases that correspond to aspects and opinions, which can be multiple words long and must be considered as a single unit for accurate sentiment analysis.
💡Tokenization
Tokenization is the process of breaking down a sentence into individual units called tokens, which are usually words, phrases, or punctuation marks. This is a fundamental step in many natural language processing tasks, including ABSA, as it allows the algorithm to analyze and process the text at a more granular level.
💡Aspect Term Extraction
Aspect term extraction is the process of identifying and extracting specific terms from a text that represent aspects or targets of the sentiment analysis. This is a crucial step in ABSA as it narrows down the focus to the relevant parts of the text that carry the most information about the sentiment being expressed.
💡Opinion Term Extraction
Opinion term extraction is the process of identifying and extracting terms that express the sentiment or opinion about the aspects in the text. These terms provide the evaluative language that indicates whether the sentiment is positive, negative, or neutral towards the aspects.
💡Sentiment Relation Classifier
A sentiment relation classifier is a component of an ABSA model that determines the sentiment polarity (positive, negative, or neutral) associated with each aspect-opinion pair. It plays a critical role in understanding the sentiment expressed towards different aspects by analyzing the relationship between the aspect and the opinion expressed about it.
💡Model Architecture
Model architecture refers to the structural design and organization of a machine learning model, including the way data flows through the system and the types of operations or computations performed. In the context of ABSA, model architecture involves the methods and processes used to break down text into tokens, form spans, extract aspects and opinions, and classify sentiment relationships.
💡Data Sets
Data sets are collections of data used for training and testing machine learning models. In the context of ABSA, data sets often consist of reviews or comments about products or services, labeled with the aspects, opinions, and sentiments they contain. These data sets help the model learn to accurately identify and analyze sentiment in new, unseen text.
Highlights

Introduction to Aspect-Based Sentiment Analysis (ABSA), a method for detailed sentiment analysis.

ABSA provides more detailed insights compared to traditional sentiment analysis by identifying specific aspects people like or dislike.

An example of ABSA output shows structured data that identifies aspects (waiters, pasta), opinions (friendly, average), and sentiments (positive, negative).

Terminology clarification: aspects are also called targets, and opinions are subjective views on these aspects.

Overview of ABSA outputs, which can include counts of mentions and associations with sentiments (e.g., food mentioned 523 times with various sentiments).

Clustering and dimension reduction can be used to group similar aspects together when there are too many aspects.

Walkthrough of a paper titled 'Learning Span Level Interactions for Aspect Sentiment Triplet Extraction', focusing on a model that identifies aspect-sentiment relationships.

The model breaks sentences into tokens and forms spans to identify aspects and opinions within consumer reviews.

The process includes aspect term extraction, opinion term extraction, and pruning to manage computational complexity.

Each aspect is paired with each opinion, and a classifier determines the sentiment relationship between them (positive, negative, or neutral).

The model's performance is significantly improved compared to previous papers, with variations depending on the model used (LSTM or BERT).

Training and testing datasets include restaurant reviews from 2014 (Rest 14) and laptop reviews from 2014 (Lab 14).

Authors have made their code publicly available on GitHub for further exploration and application.

A Python demo is available on Google Colab, showing the process of downloading packages and loading pre-trained models.

Demonstration of how the model works with a training example, identifying the target, opinion, and sentiment in a given sentence.

The model's sensitivity to the training data is highlighted, as it struggles with examples outside its domain of training (e.g., medical domain).

The notebook also shows how to train a model from scratch using your own labeled training data and how to evaluate the model using F1 score.

The video concludes with a call to action for viewers to like and subscribe if they found the content useful.

Transcripts

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ABSASentimentAnalysisOpinionMiningPythonDemoReviewAnalysisDataExtractionModelArchitectureSpanInteractionsAspectSentimentTripletExtraction