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].
SNNN: Promoting Word Sentiment and Negation in Neural Sentiment Classification
Authors: Qinmin Hu, Jie Zhou, Qin Chen, Liang He
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, the experiments conducting on the IMDB and Yelp data sets show that our approach is superior to the state-of-the-art methods. |
| Researcher Affiliation | Academia | Qinmin Hu, Jie Zhou, Qin Chen, Liang He Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Software Engineering East China Normal University, Shanghai, 200062, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model using mathematical equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code or include a link to a code repository for the described methodology. |
| Open Datasets | Yes | We conduct experiments to evaluate the effectiveness of our proposed approach on four datasets: Yelp 2013-2015 and IMDB, which are the same as (Tang, Qin, and Liu 2015a). The statistics of the datasets are summarized in Table 2. |
| Dataset Splits | Yes | For data training, development and testing purposes, we divide the data with the proportion of 8:1:1 and the NLTK 1 tool has been adopted on all datasets for tokenization and sentence splitting. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models or memory) used for running its experiments within the text. |
| Software Dependencies | No | The paper mentions 'NLTK tool' for tokenization and sentence splitting but does not provide specific version numbers for NLTK or any other software dependencies. |
| Experiment Setup | No | In order to better compare with the existing Chen s and Tang s work (Chen et al. 2016; Tang, Qin, and Liu 2015a), we train our data with the same settings as Chen and Tang. The details are referred to (Chen et al. 2016; Tang, Qin, and Liu 2015a) because of the page limit. |