Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis
Authors: Fangtao Li, Sheng Wang, Shenghua Liu, Ming Zhang
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on two datasets: review dataset and microblog dataset. The results demonstrate the advantages of our model. It shows significant improvement compared with supervised topic models and collaborative filtering methods. |
| Researcher Affiliation | Collaboration | Fangtao Li1, Sheng Wang2, Shenghua Liu3 and Ming Zhang4 1Google Inc. 1600 Amphitheatre Pkwy, Mountain View, CA 2Department of Computer Science, University of Illinois Urbana Champaign, Urbana, IL 3Institute of Computing Technology, Chinese Academy of Sciences, Beijing 4School of Electronics Engineering and Computer Science, Peking University, Beijing |
| Pseudocode | No | The paper describes the generative process of the model in numbered textual steps but does not present it as a formal 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | Review dataset The first dataset is a collection of movie reviews. A subset of this collection has been used in (Pang and Lee 2005). |
| Dataset Splits | No | The paper mentions evaluating on a 'test set' but does not provide specific details on training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using Lib SVM for classification but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Figure 3 shows the accuracy of our model in comparison with all the baselines in different numbers of latent factors. ... The proposed SUIT model achieves best result when the number is equal to 40. |