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].
Unsupervised Sentiment Analysis for Social Media Images
Authors: Yilin Wang, Suhang Wang, Jiliang Tang, Huan Liu, Baoxin Li
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With experiments on two large-scale datasets, we show that the proposed method is effective in addressing the two challenges. |
| Researcher Affiliation | Academia | Yilin Wang, Suhang Wang, Jiliang Tang, Huan Liu, Baoxin Li Arizona State University Tempe, Arizona EMAIL |
| Pseudocode | No | The provided text does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code or explicitly state that the code will be released. |
| Open Datasets | No | The paper mentions 'two large-scale datasets' and 'datasets from real-world social media image-sharing sites' but does not name them or provide concrete access information (e.g., specific links, DOIs, or formal citations) within the provided text. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) needed to reproduce data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (like exact GPU/CPU models or processor types) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values or training configurations. |