Aspect Based Sentiment Analysis: A Python Demo

Summary

This video introduces aspect-based sentiment analysis (ABSA), a method to extract structured data on aspects, opinions, and sentiments from reviews. It explains ABSA's advantages over traditional sentiment analysis and demonstrates a paper's model for extracting aspect sentiment triplets. The video includes a Python demo showcasing how to train a model on a specific dataset and evaluate its performance using F1 score.

Takeaways

• 🍽 Aspect-Based Sentiment Analysis (ABSA) focuses on identifying specific aspects of a product or service mentioned in reviews and the sentiment towards those aspects.
• 📊 ABSA provides structured data that includes aspects, opinions, and sentiments, offering more detailed insights compared to traditional sentiment analysis.
• 🔍 The example given in the script illustrates how ABSA can distinguish between positive sentiment towards 'waiters' and negative sentiment towards 'pasta' in a restaurant review.
• 📚 Terminology in ABSA includes 'aspects' or 'targets' (like 'waiters' and 'pasta'), and 'subjective opinions' (like 'friendly' and 'average').
• 📊 ABSA can quantify sentiments towards aspects by counting mentions and categorizing them as positive, negative, or neutral.
• 🔍 The script mentions clustering and dimension reduction as methods to group similar aspects when there are too many to analyze individually.
• 📖 The paper 'Learning Span-level Interactions for Aspect Sentiment Triplet Extraction' is highlighted, which introduces a model that outputs 'aspect sentiment triplets'.
• 🛠️ The model architecture involves tokenization, span enumeration, pruning, and classification to identify aspects, opinions, and their sentiment relations.
• 💻 The paper's authors have made their code publicly available on GitHub, allowing others to replicate their work and use their model.
• 🐍 A Python demo using the pre-trained model is provided, showing how to predict aspect sentiment triplets for new sentences.
• 📈 The model's performance varies depending on the dataset and encoding method used, with BERT showing better performance than LSTM.

Q & A

What is Aspect-Based Sentiment Analysis (ABSA)?

Aspect-Based Sentiment Analysis is a method used to analyze specific aspects or targets of a product or service mentioned in a review or text, identifying the sentiment expressed towards these aspects.

How does ABSA differ from traditional sentiment analysis?

ABSA provides more detailed insights by extracting information about specific aspects people are discussing, whether they like or dislike each aspect, and the reasons behind their sentiments, whereas traditional sentiment analysis typically focuses on overall positive, negative, or neutral sentiments.

What is an example of structured data output for ABSA?

Structured data output for ABSA might include aspects like 'waiters' and 'pasta' from a restaurant review, with opinions such as 'friendly' and 'average', and sentiments like 'positive' and 'negative' respectively.

What terminology is used in ABSA for specific parts of a review?

In ABSA, 'aspects' or 'targets' refer to specific parts of a product or service being reviewed, while 'subjective opinions' are the adjectives or phrases used to describe those aspects.

What does the output of ABSA look like when analyzing a large number of reviews?

The output of ABSA on a large dataset might show the frequency of mentions for each aspect, along with the associated opinions and sentiments, such as 'food' being mentioned 523 times with various opinions like 'weird', 'authentic', 'great', and 'dry'.

How can clustering and dimension reduction be applied in ABSA?

When there are too many aspects identified in the reviews, clustering and dimension reduction techniques can be used to group similar aspects together, simplifying the analysis and making it more manageable.

What is an 'aspect sentiment triplet' as mentioned in the paper walkthrough?

An 'aspect sentiment triplet' refers to a combination of an aspect, the opinion expressed about that aspect, and the sentiment associated with that opinion, such as 'windows 8' being the aspect, 'did not enjoy' the opinion, and 'negative' the sentiment.

What is the process for breaking down a sentence into tokens in ABSA?

In the model described, sentences are broken down into tokens, which are then combined to form spans. These spans represent phrases that could be aspects or opinions, like 'windows 8' or 'did not enjoy'.

How does the model handle the computational complexity of enumerating spans?

To manage computational complexity, the model performs pruning operations that remove invalid spans and keep only candidate aspects or targets and opinions.

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

The sentiment relation classifier determines whether each aspect-opinion pair conveys a positive, negative, or neutral sentiment, or if it is invalid due to being an objective fact without a subjective opinion.

How can one train a model from scratch using the provided ABSA framework?

The provided notebook demonstrates how to train a model from scratch using one's own labeled training data and evaluate its performance using metrics like the F1 score.