Summary of New Directions in Text Classification Research: Maximizing the Performance Of Sentiment Classification From Limited Data, by Surya Agustian et al.
New Directions in Text Classification Research: Maximizing The Performance of Sentiment Classification from Limited Data
by Surya Agustian, Muhammad Irfan Syah, Nurul Fatiara, Rahmad Abdillah
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Information Theory (cs.IT); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of sentiment analysis on limited training data, a crucial issue in various applications. It proposes a benchmark dataset for classifying text into positive, negative, and neutral categories using a limited amount of data (300-600 samples). The study provides external datasets for aggregation and augmentation purposes, focusing on Covid Vaccination sentiment and an open topic. The F1-score is used as the evaluation metric, balancing precision and recall among the three classes. A baseline score is offered as a reference for unoptimized classification methods, while an optimized score serves as a target to be achieved by proposed methods. Both scores are achieved using the SVM method, which is widely recognized as a state-of-the-art approach in conventional machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to do better sentiment analysis when you don’t have much training data. This is important because sometimes we only have a little bit of information about how people feel about something. The researchers provide a special dataset for testing and training, along with some extra datasets that can be used to make the model better. They use a specific way of measuring how well the model does, called the F1-score. This score looks at both how accurate the model is and how good it is at finding all the different kinds of sentiment (positive, negative, or neutral). The researchers also provide two scores: one for when you don’t optimize the model, and one for when you do. Both scores use a special kind of machine learning method called SVM. |
Keywords
» Artificial intelligence » Classification » F1 score » Machine learning » Precision » Recall
