SMACk: An Argumentation Framework for Opinion Mining
Authors: Mauro Dragoni, Célia da Costa Pereira, Andrea G.B. Tettamanzi, Serena Villata
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this demo, we present our opinion summary application built on top of an argumentation framework, a standard AI framework whose value is to exchange, communicate and resolve possibly conflicting viewpoints in distributed scenarios. We show how our application is able to extract relevant and debated opinions from a set of documents containing user-generated content from online commercial websites. |
| Researcher Affiliation | Academia | Mauro Dragoni1, C elia da Costa Pereira2, Andrea G.B. Tettamanzi2, Serena Villata3 1Fondazione Bruno Kessler, Trento, Italy 2Universit e Nice Sophia Antipolis, I3S, UMR 7271, Sophia Antipolis, France 3CNRS, I3S, UMR 7271, Sophia Antipolis, France |
| Pseudocode | No | The paper describes the construction of the argumentation graph and the computation of argument acceptability in narrative text, but it does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the framework or providing a link to a code repository. |
| Open Datasets | Yes | We analyzed a set of 50, 000 reviews extracted from the Dranziera1 dataset. 1http://goo.gl/7j K4Rp |
| Dataset Splits | No | The paper mentions analyzing "a set of 50,000 reviews extracted from the Dranziera1 dataset" but does not specify any training, validation, or test splits for this dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the described application or demonstrations. |
| Software Dependencies | No | The paper describes the SMACk framework and the use of abstract bipolar argumentation theory, but it does not list any specific software dependencies or library versions (e.g., Python, PyTorch, specific solver versions) that were used. |
| Experiment Setup | No | The paper describes the conceptual steps of the demonstration, such as graph construction and acceptability computation, but it does not provide specific experimental setup details, hyperparameters (e.g., learning rates, batch sizes), or training configurations. |