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].
Learning Latent Opinions for Aspect-level Sentiment Classification
Authors: Bailin Wang, Wei Lu
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our model achieves the state-of-the-art performance while extracting interpretable sentiment expressions. |
| Researcher Affiliation | Academia | Bailin Wang College of Information and Computer Sciences University of Massachusetts Amherst, MA 01003, USA EMAIL Wei Lu Singapore University of Technology and Design 8 Somapah Road Singapore, 487372 EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is available at https://github.com/berlino/SA-Sent |
| Open Datasets | Yes | We perform sentiment classification tasks on publicly available datasets on online reviews across different languages from Sem Eval tasks and social comments from Twitter. The first group consists of review datasets from Sem Eval2014 task 4 (Pontiki et al. 2014) and Twitter comments collected by (Dong et al. 2014). |
| Dataset Splits | Yes | One-sixth of training data is left out as the validation set for tuning hyperparameters and doing model selection. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like "Glo Ve" and "Adam" but does not specify their version numbers. |
| Experiment Setup | Yes | The dimension of target s binary indicator embedding is 30. We fixed the word embeddings in all the experiments. Dropout is also used after the input layer and it is tuned for each dataset. λ1 is tuned between 0 and 1, λ2 is chosen from [0, 0.2] with step size 0.04. For LSTM, we set the hidden dimension size to 300. |