Microblog Sentiment Classification via Recurrent Random Walk Network Learning
Authors: Zhou Zhao, Hanqing Lu, Deng Cai, Xiaofei He, Yueting Zhuang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experiments on the large-scale public datasets from Twitter show that our method achieves better performance than other state-of-the-art solutions to the problem. |
| Researcher Affiliation | Academia | Zhou Zhao1, Hanqing Lu1, Deng Cai2, Xiaofei He2, and Yueting Zhuang1 1College of Computer Science, Zhejiang University 2State Key Lab of CAD&CG, Zhejiang University {zhaozhou,lhq110,yzhuang}@zju.edu.cn, {dengcai,xiaofeihe}@gmail.com |
| Pseudocode | No | The paper provides mathematical equations for the model (e.g., LSTM equations, objective function) but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing source code or provide a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate the performance of our method using two public Twitter datasets for microblog sentiment classification: Stanford Twitter Sentiment (STS) [Go et al., 2009] and Obama Mc Cain Debate (OMD) [Shamma et al., 2009], which are composed of raw tweets with their ground truth sentiment classes. |
| Dataset Splits | Yes | Following the experimental setting in [Shamma et al., 2009; Hu et al., 2013], we take 80% of the dataset as training data, other 10% of the data for validation and the remaining 10% ones for testing. |
| Hardware Specification | No | The paper does not mention any specific hardware components (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'pre-trained word embeddings' and 'LSTMs' but does not specify version numbers for any software libraries, frameworks, or programming languages (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | To optimize the objective, we employ the stochastic gradient descent (SGD) with diagonal variant of Ada Grad in [Duchi et al., 2011]. The input words of our methods are initialized by pre-trained word embeddings [Mikolov et al., 2013] and the weights of LSTMs are randomly by a Gaussian distribution with zero mean. We vary the number of embedding dimensions from 32 to 521, the number of random-walk layers from 1 to 9, and the trade-off parameter α from 10 6 to 106 and 10 8 to 108, respectively. We observe that our method achieves the best performance when the number of embedding dimensions is set to 128, the number of random-walk layers is set to 3 and the trade-off parameter is set to 1 on both datasets. |