In today’s data-driven world, understanding customer opinions is as crucial as capturing sales. Aspect-Based Sentiment Analysis (ABSA) is a potent tool that dives deep into user sentiments about specific aspects of products or services rather than providing a blanket sentiment for an entire document. In this guide, we will delve into how to get started with ABSA, offering a user-friendly approach to implementing this powerful technique.

Understanding the Concept of Aspect-Based Sentiment Analysis

Think of ABSA as being akin to reviewing a restaurant. Instead of just saying, “The food is good,” you might say, “The food was delicious, but the service was awful.” This way, you’re offering nuanced feedback tailored to specific aspects rather than a general sentiment. ABSA performs this task automatically by analyzing long texts for sentiments on various aspects.

Quick Start with ABSA

Getting your hands on ABSA is a breeze! Below is a quick example of how to classify sentiments regarding specific aspects of a text using Python.

import aspect_based_sentiment_analysis as absa

nlp = absa.load()
text = "We are great fans of Slack, but we wish the subscriptions were more accessible to small startups."
slack, price = nlp(text, aspects=["slack", "price"])

assert price.sentiment == absa.Sentiment.negative
assert slack.sentiment == absa.Sentiment.positive

In this code snippet:

• We import the ABSA package.
• Load a pre-trained aspect-based sentiment analysis model.
• Define a sample text to analyze sentiments about Slack and its pricing.
• Lastly, the assertions confirm that the sentiment for “price” is negative while that for “slack” is positive.

The ABSA Pipeline: Keeping the Process in Shape

ABSA utilizes a well-structured pipeline that prepares inputs and interprets outputs efficiently. Here’s how:

name = absa.classifier-rest-0.2
model = absa.BertABSClassifier.from_pretrained(name)
tokenizer = absa.BertTokenizer.from_pretrained(name)
professor = absa.Professor(...)  # Explained in detail later on
text_splitter = absa.sentencizer()  # The English CNN model from SpaCy.
nlp = absa.Pipeline(model, tokenizer, professor, text_splitter)

task = nlp.preprocess(text=..., aspects=...)
tokenized_examples = nlp.tokenize(task.examples)
input_batch = nlp.encode(tokenized_examples)
output_batch = nlp.predict(input_batch)
predictions = nlp.review(tokenized_examples, output_batch)
completed_task = nlp.postprocess(task, predictions)

Think of the ABSA pipeline as a factory line for manufacturing sentiments:

• Each stage in the factory represents a step in processing the text, from receiving raw materials (input texts) to producing final products (predicted sentiments).
• The “professor” component supervises the model’s decisions, akin to a quality control manager ensuring that only reliable predictions proceed to the next stage.
• Input texts are meticulously split into smaller, manageable parts, enabling the analysis to be more accurate and context-aware.

Understanding Model Predictions

Just like a detective analyzing evidence to solve a case, explaining model reasoning is crucial to understanding its decisions. The Basic Pattern Recognizer enhances transparency by linking model predictions to recognized patterns:

import aspect_based_sentiment_analysis as absa

recognizer = absa.aux_models.BasicPatternRecognizer()
nlp = absa.load(pattern_recognizer=recognizer)

completed_task = nlp(text=..., aspects=["slack", "price"])
slack, price = completed_task.examples
absa.summary(slack)
absa.display(slack.review)

Troubleshooting Your ABSA Implementation

As you navigate the exciting world of aspect-based sentiment analysis, you might encounter some hiccups. Here are some troubleshooting tips to keep in mind:

• Ensure your Python environment is compatible (Python version 3.7 is recommended).
• If your model fails to predict correctly, check for errors in input formatting or model compatibility.
• For comprehensive insights into model behaviors and to validate your predictions, deepen your analysis using the Basic Pattern Recognizer.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Installation Guide

To get started with the aspect-based sentiment analysis package, install it using pip:

pip install aspect-based-sentiment-analysis

Alternatively, clone the repository and set up the environment using conda:

git clone git@github.com:ScalaConsultants/Aspect-Based-Sentiment-Analysis.git
conda env create -f=environment.yml
conda activate Aspect-Based-Sentiment-Analysis

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.