Context-Sensitive Twitter Sentiment Classification Using Neural Network
Authors: Yafeng Ren, Yue Zhang, Meishan Zhang, Donghong Ji
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both balanced and unbalanced datasets show that our proposed models outperform the current state-of-the-art. |
| Researcher Affiliation | Academia | 1. Computer School, Wuhan University, Wuhan, China 2. Singapore University of Technology and Design, Singapore 3. School of Computer Science and Technology, Heilongjiang University, Harbin, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using the 'word2vec' tool and provides a link to its Google Code project, but it does not state that the authors' own source code for their methodology is publicly available. |
| Open Datasets | No | The paper describes its data collection process using the Twitter Streaming API and states the final size of the dataset (18,000 tweets), but it does not provide concrete access information (e.g., a link, DOI, or formal citation for public access) to this collected dataset. |
| Dataset Splits | Yes | We perform ten-fold cross-validation experiments and report the overall performances. The whole dataset is split into ten equal sections, each decoded by the model trained from the remaining nine sections. We randomly choose one section from the nine training sections as the development dataset in order to tune hyper-parameters. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'word2vec' and 'Ada Grad' but does not specify version numbers for these or any other software components, which is required for reproducibility. |
| Experiment Setup | Yes | Type #parameter Network structure D = 50, Dc = 10, C = 100, Cc = 30, H = 50 Training λ = 10 8, α = 0.01 |