Enhancing Aspect-based Sentiment Analysis through Supervised Contrastive Learning with Induced Tree Structures

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Karimi Zarandi Akram; Mirzaei Sayeh

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

Download Full-Text

View:

Download:

Information Seminar Paper

Title

Enhancing Aspect-based Sentiment Analysis through Supervised Contrastive Learning with Induced Tree Structures

Author(s)

Karimi Zarandi Akram | Mirzaei Sayeh

Keywords

Aspect Base Sentiment Analysis

Transformer

Contrastive Learning

Induced Tree

Abstract

A facet of sentiment analysis is Aspect-based Sentiment Analysis (ABSA), which entails identifying and assessing sentiments towards particular aspects or attributes within text data. In ABSA, an Induced Tree can illustrate the connections between various aspects of a text and the corresponding sentiments. By employing supervised Contrastive Learning, models for ABSA can be trained using both labeled and unlabeled data to boost performance. In this research, we fused Induced Tree structures from the pre-trained BERT model with supervised Contrastive Learning to empower the model to effectively distinguish between different aspects and sentiments. This method assists the model in grasping subtle sentiment variations towards various aspects of products or services, leading to more accurate sentiment analysis outcomes. The experimental results showcase the effectiveness of our integrated framework in delivering precise sentiment forecasts and profound insights into sentiment-attribute relationships, as evidenced by the enhanced classification accuracy on SemEval2014 benchmarks.

Video

Cites

No record.

References

No record.

Cite

APA: Copy

Karimi Zarandi, Akram, & Mirzaei, Sayeh. (2024). Enhancing Aspect-based Sentiment Analysis through Supervised Contrastive Learning with Induced Tree Structures. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147396/en

Vancouver: Copy

Karimi Zarandi Akram, Mirzaei Sayeh. Enhancing Aspect-based Sentiment Analysis through Supervised Contrastive Learning with Induced Tree Structures. 2024. Available from: https://sid.ir/paper/1147396/en

IEEE: Copy

Akram Karimi Zarandi, and Sayeh Mirzaei, “Enhancing Aspect-based Sentiment Analysis through Supervised Contrastive Learning with Induced Tree Structures,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147396/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops