Sentiment Analysis: Is there a way to extract positive and negative aspects in reviews?

Question

Currently, I'm working on a project where I need to extract the relevant aspects used in positive and negative reviews in real time.

For the notions of more negative and positive, it will be a question of contextualizing the word. Distinguish between a word that sounds positive in a negative context (consider irony).

Here is an example: Very nice welcome!!! We ate very well with traditional dishes as at home, the quality but also the quantity are in appointment!!!*

Positive aspects: welcome, traditional dishes, quality, quantity

Can anyone suggest to me some tutorials, papers or ideas about this topic?

Thank you in advance.

Answer 1

This task is called Aspect Based Sentiment Analysis (ABSA). Most popular is the format and dataset specified in the 2014 Semantic Evaluation Workshop (Task 5) and its updated versions in the following years.

Overview of model efficiencies over the years:

https://paperswithcode.com/sota/aspect-based-sentiment-analysis-on-semeval

Good source for ressources and repositories on the topic (some are very advanced but there are some more starter friendly ressources in there too):

https://github.com/ZhengZixiang/ABSAPapers

Just from my general experience in this topic a very powerful starting point that doesn't require advanced knowledge in machine learning model design is to prepare a Dataset (such as the one provided for the SemEval2014 Task) that is in a Token Classification Format and use it to fine-tune a pretrained transformer model such as BERT, RoBERTa or similar. Check out any tutorial on how to do fine-tuning on a token classification model like this one in huggingface. They usually use the popular task of Named Entity Recognition (NER) as the example task but for the ABSA-Task you basically do the same thing but with other labels and a different dataset.

Obviously an even easier approach would be to take more rule-based approaches or combine a rule-based approach with a trained sentiment analysis model/negation detection etc., but I think generally with a rule-based approach you can expect a much inferior performance compared to using state-of-the-art models as transformers.

If you want to go even more advanced than just fine-tuning the pretrained transformer models then check out the second and third link I provided and look at some of the machine learning model designs specifically designed for Aspect Based Sentiment Analysis.