3,075 research outputs found
Large language models for aspect-based sentiment analysis
Large language models (LLMs) offer unprecedented text completion
capabilities. As general models, they can fulfill a wide range of roles,
including those of more specialized models. We assess the performance of GPT-4
and GPT-3.5 in zero shot, few shot and fine-tuned settings on the aspect-based
sentiment analysis (ABSA) task. Fine-tuned GPT-3.5 achieves a state-of-the-art
F1 score of 83.8 on the joint aspect term extraction and polarity
classification task of the SemEval-2014 Task 4, improving upon InstructABSA
[@scaria_instructabsa_2023] by 5.7%. However, this comes at the price of 1000
times more model parameters and thus increased inference cost. We discuss the
the cost-performance trade-offs of different models, and analyze the typical
errors that they make. Our results also indicate that detailed prompts improve
performance in zero-shot and few-shot settings but are not necessary for
fine-tuned models. This evidence is relevant for practioners that are faced
with the choice of prompt engineering versus fine-tuning when using LLMs for
ABSA
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
The World Wide Web holds a wealth of information in the form of unstructured
texts such as customer reviews for products, events and more. By extracting and
analyzing the expressed opinions in customer reviews in a fine-grained way,
valuable opportunities and insights for customers and businesses can be gained.
We propose a neural network based system to address the task of Aspect-Based
Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic
Sentiment Analysis. Our proposed architecture divides the task in two subtasks:
aspect term extraction and aspect-specific sentiment extraction. This approach
is flexible in that it allows to address each subtask independently. As a first
step, a recurrent neural network is used to extract aspects from a text by
framing the problem as a sequence labeling task. In a second step, a recurrent
network processes each extracted aspect with respect to its context and
predicts a sentiment label. The system uses pretrained semantic word embedding
features which we experimentally enhance with semantic knowledge extracted from
WordNet. Further features extracted from SenticNet prove to be beneficial for
the extraction of sentiment labels. As the best performing system in its
category, our proposed system proves to be an effective approach for the
Aspect-Based Sentiment Analysis
Rude waiter but mouthwatering pastries! An exploratory study into Dutch aspect-based sentiment analysis
The fine-grained task of automatically detecting all sentiment expressions within a given document and the aspects to which they refer is known as aspect-based sentiment analysis. In this paper we present the first full aspect-based sentiment analysis pipeline for Dutch
and apply it to customer reviews. To this purpose, we collected reviews from two different domains, i.e. restaurant and smartphone reviews. Both corpora have been manually annotated using newly developed guidelines that comply to standard practices in the field. For our experimental pipeline we perceive aspect-based sentiment analysis as a task consisting of three main subtasks which have to be tackled incrementally: aspect term extraction, aspect category classification and polarity classification. First experiments on our Dutch restaurant corpus reveal that this is indeed a feasible approach that yields promising results
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