Combining Machine Learning and Crowdsourcing for Better Understanding Commodity Reviews
Authors: Heting Wu, Hailong Sun, Yili Fang, Kefan Hu, Yongqing Xie, Yangqiu Song, Xudong Liu
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we perform real experiments with practical review data to confirm the effectiveness of our method. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Beihang University, Beijing,China, 100191 2Department of Computer Science, University of Illinois at Urbana-Champaign, United States |
| Pseudocode | No | The paper includes 'Figure 2: The workflow of review analysis' which is a diagram, but it does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for their methodology is open-source or publicly available. |
| Open Datasets | No | The paper states 'We crawl 9,738 reviews of 9 mobile phones from JD.com' indicating a custom collected dataset, but it does not provide any link, citation, or statement about its public availability. |
| Dataset Splits | No | The paper mentions 'The dataset is split into one training set (80%) and one testing set (20%)', but it does not explicitly state the use of a validation set or its split percentage. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper lists the machine learning algorithms used (SVM, NB, KNN, AB, DT) but does not specify any software dependencies or their version numbers (e.g., Python, library versions). |
| Experiment Setup | No | The paper describes the general experimental setup and evaluation metric (F-measure) but does not provide specific details such as hyperparameters (e.g., learning rate, batch size, epochs) or specific optimizer settings for the machine learning algorithms. |