Various Approaches to Aspect-based Sentiment Analysis

TL;DR

This paper explores various methods, features, and preprocessing techniques for aspect-based sentiment analysis, focusing on classifying sentiments for specific aspects within sentences with conflicting sentiments.

Contribution

It systematically reviews different approaches and techniques to improve aspect-specific sentiment classification in complex sentences.

Findings

Preprocessing steps significantly impact classification accuracy.

Feature selection tailored to aspects enhances sentiment detection.

Different machine learning and deep learning methods show varied effectiveness.

Abstract

The problem of aspect-based sentiment analysis deals with classifying sentiments (negative, neutral, positive) for a given aspect in a sentence. A traditional sentiment classification task involves treating the entire sentence as a text document and classifying sentiments based on all the words. Let us assume, we have a sentence such as "the acceleration of this car is fast, but the reliability is horrible". This can be a difficult sentence because it has two aspects with conflicting sentiments about the same entity. Considering machine learning techniques (or deep learning), how do we encode the information that we are interested in one aspect and its sentiment but not the other? Let us explore various pre-processing steps, features, and methods used to facilitate in solving this task.