title: “SenticNet Is Not Aspect-Based Sentiment Analysis” collection: posts permalink: /post/senticnet-is-not-absa/ date: 2026-01-29 excerpt: “Why SenticNet is an affective knowledge base rather than an end-to-end ABSA solution.” tags:

• sentiment analysis
• SenticNet
• ABSA

• NLP

SenticNet Is Not Aspect-Based Sentiment Analysis

Introduction

When I first started using SenticNet, I assumed it could directly perform sentiment analysis on sentences and even support Aspect-Based Sentiment Analysis (ABSA). After extensive experimentation, I realised this assumption was fundamentally wrong.

This post explains what SenticNet actually is, what it is not, and how it should be used correctly.

What SenticNet Actually Is

SenticNet is an affective commonsense knowledge base. At its core, it is a mapping from concepts to affective information:

• polarity
• sentics (affective dimensions)
• mood tags
• semantic associations

In practice, SenticNet behaves like: concept → affective knowledge

It does not process raw text.

What SenticNet Does NOT Do

SenticNet does not:

• parse sentences
• extract concepts from text
• identify aspects
• link aspects to sentiment expressions

It only responds when a concept is explicitly provided.

Why SenticNet Is Not ABSA

Aspect-Based Sentiment Analysis requires three steps:

1. Aspect extraction
2. Sentiment detection
3. Aspect–sentiment linking

SenticNet only contributes to step 2.

The remaining steps must be handled using linguistic rules, parsers, or learning-based models.

A Practical Example

Sentence:

the battery life is terrible

• Aspect: battery life
• Sentiment expression: terrible

SenticNet understands terrible,
but it has no knowledge of battery life as an aspect in this sentence.

Correct Usage Pattern

A correct pipeline looks like this:

”"”bash Sentence → concept / aspect extraction → aspect–sentiment linking → SenticNet (affective enrichment) “””

SenticNet should be treated as a knowledge source, not an analyzer.

Lessons Learned

• Treat SenticNet as passive knowledge
• Do not expect end-to-end sentiment analysis
• Use it to improve interpretability and affective richness
• Combine it with NLP or neural models

Conclusion

SenticNet is powerful when used correctly, but misleading when treated as a sentiment analysis system.

Understanding its role makes sentiment pipelines clearer, more interpretable, and more robust.