Unlock Aspect-Based Sentiment Analysis' Potential for Informed Decision Making with GPT-4 and other LLMs
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Table of Contents
Understanding public opinion and sentiment is crucial for businesses, researchers, and policymakers in today’s digital world. Sentiment analysis has long been a popular tool to gauge the general idea of text data.
However, traditional sentiment analysis often falls short of capturing people’s nuanced opinions about different aspects of a subject.
This is where aspect-based sentiment analysis comes into play, offering more comprehensive and targeted research.
This blog post explores aspect-based sentiment analysis, its history, differences from general sentiment analysis, potential use cases, and five real-world examples.
What is Aspect-Based Sentiment Analysis?
Aspect-based sentiment analysis (ABSA) is an intermediate-to-advanced NLP task that identifies and determines sentiment towards specific aspects or features mentioned in a text.
It goes beyond the scope of traditional sentiment analysis, which provides an overall sentiment by capturing the varied opinions people express about different aspects of a subject, product, or service.
History of Aspect-Based Sentiment Analysis
ABSA has its roots in the early days of sentiment analysis research, dating back to the early 2000s.
As researchers and practitioners recognized the limitations of traditional sentiment analysis, they began developing methods to extract more detailed and contextualized sentiment information from text.
Initial approaches relied on rule-based systems and lexicons, while more recent techniques incorporate machine learning and deep learning models to capture the nuances of language and context.
Difference Between Aspect-Based Sentiment Analysis and Sentiment Analysis:
The primary difference between ABSA and general sentiment analysis lies in their granularity.
Traditional sentiment analysis provides a given text’s overall sentiment score, such as positive, negative, or neutral.
In contrast, ABSA identifies and evaluates sentiment towards specific aspects or features within the text, offering a more targeted and comprehensive understanding of opinions.
How Aspect-Based Sentiment Analysis Can Be Used
ABSA has numerous practical applications, including:
- Product reviews analysis: Companies can gain insights into customers’ opinions about various aspects of their products, enabling them to make informed decisions about product improvements and marketing strategies.
- Social media monitoring: Brands can monitor social media platforms to understand how their audience feels about specific aspects of their products, services, or brand image, allowing them to address concerns and capitalize on positive feedback.
- Customer support: ABSA can help identify common issues and praise in customer feedback, enabling companies to prioritize and address concerns more effectively.
- Market research: Researchers can use ABSA to analyze opinions on specific aspects of competitors’ products or services, providing valuable insights for product development and marketing strategies.
- Public opinion analysis: Policymakers and organizations can use ABSA to understand public sentiment towards policies, programs, or events, helping them make informed decisions.
5 Examples of Aspect-Based Sentiment Analysis
- A restaurant chain analyses customer reviews to identify strengths and weaknesses in service, food quality, and ambiance.
- A smartphone manufacturer using ABSA to understand consumer opinions on battery life, camera quality, and user interface in product reviews.
- A political campaign analyses social media posts to gauge public sentiment on specific policy proposals and campaign messaging.
- A hotel chain using ABSA to evaluate customer feedback on room cleanliness, staff friendliness, and location to improve guest experiences.
- A streaming service analyses user reviews to understand preferences for content quality, platform usability, and pricing, guiding future development and marketing strategies.
ChatGPT prompt example prompt
Aspect-based Sentiment Analysis Prompt
Aspect-based Sentiment Analysis for: Fill mask, also known as masked language modeling or predicting missing words, is an advanced NLP task. It requires a deeper understanding of language, context, and semantic relationships between words in a sentence.
In fill mask tasks, a model is trained to predict a missing word (or token) in a sentence given the surrounding context. This type of task is often used for pretraining large-scale language models like GPT-4 or BERT. These models can capture complex patterns and relationships within the language, which allows them to generate highly contextualized and coherent word predictions.
Since fill mask tasks involve predicting missing words based on context and semantics, they go beyond the scope of core and intermediate NLP tasks, making them advanced NLP tasks.
Result as Text
Here’s an aspect-based sentiment analysis for the given text:
Aspect: Fill mask (masked language modeling)
Sentiment: Neutral
Aspect: Advanced NLP task
Sentiment: Positive (The text describes fill mask tasks as advanced and requiring a deeper understanding of language, context, and semantics.)
Aspect: Model training
Sentiment: Neutral
Aspect: Large-scale language models (GPT-4 or BERT)
Sentiment: Positive (The text highlights their ability to capture complex patterns and relationships within the language and generate highly contextualized and coherent word predictions.)
Aspect: Core and intermediate NLP tasks
Sentiment: Neutral (The text mentions that fill mask tasks go beyond the scope of core and intermediate NLP tasks, but it does not express any sentiment towards them.)
Note that aspect-based sentiment analysis aims to determine the sentiment towards specific aspects mentioned in a text, rather than providing an overall sentiment.
Result as table
| Aspect | Sentiment | Evidence |
|---|---|---|
| Advanced NLP task | Positive | “advanced NLP task,” “requires a deeper understanding,” “capture complex patterns” |
| Fill mask task | Neutral to Positive | “predicting a missing word (or token),” “pretraining large-scale language models” |
| GPT-4 or BERT | Positive | “large-scale language models like GPT-4 or BERT” |
| Capturing patterns and relationships | Positive | “capture complex patterns and relationships within the language,” “generate highly contextualized and coherent word predictions” |
| Beyond core and intermediate tasks | Positive | “go beyond the scope of core and intermediate NLP tasks” |
Result as Json
{
"Aspect-based Sentiment Analysis": {
"Fill Mask Tasks": {
"Sentiment": "Positive",
"Reviews": [
{
"Text": "Fill mask tasks are advanced NLP tasks that require a deeper understanding of language, context, and semantic relationships between words in a sentence.",
"Sentiment": "Positive",
"Evidence": ["advanced NLP tasks", "deeper understanding of language", "context", "semantic relationships"]
},
{
"Text": "In fill mask tasks, a model is trained to predict a missing word in a sentence given the surrounding context, making them go beyond the scope of core and intermediate NLP tasks.",
"Sentiment": "Positive",
"Evidence": ["trained to predict a missing word", "given the surrounding context", "beyond the scope of core and intermediate NLP tasks"]
},
{
"Text": "These models can capture complex patterns and relationships within the language, which allows them to generate highly contextualized and coherent word predictions.",
"Sentiment": "Positive",
"Evidence": ["capture complex patterns and relationships", "generate highly contextualized and coherent word predictions"]
}
]
}
}
}
Conclusions
Aspect-based sentiment analysis is a powerful and granular method for analyzing sentiment in text data.
ABSA provides deeper insights and allows for more targeted action by focusing on specific aspects or features.
ABSA’s potential applications will only grow as NLP techniques evolve, making it an indispensable tool for businesses and researchers.