Emotion Classification in Microblog Texts Using Class Sequential Rules
Authors: Shiyang Wen, Xiaojun Wan
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a Chinese benchmark dataset show the superior performance of the proposed approach. |
| Researcher Affiliation | Academia | Shiyang Wen and Xiaojun Wan* Institute of Computer Science and Technology, Peking University, Beijing 100871, China The MOE Key Laboratory of Computational Linguistics, Peking University, Beijing 100871, China {wenshiyang, wanxiaojun}@pku.edu.cn |
| Pseudocode | No | The paper describes the CSR mining algorithm from (Liu 2007) and states details are omitted due to page limit, but it does not provide its own structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We use the benchmark dataset from the 2013 Chinese Microblog Sentiment Analysis Evaluation (CMSAE)5. The task is to recognize the fine-grained emotion type of a Chinese microblog text. (Footnote 5: http://tcci.ccf.org.cn/conference/2013/pages/page04_eva.html) |
| Dataset Splits | Yes | In the experiments, the parameter values are set by a five-fold cross-validation process on the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | We use a Chinese segmentation tool ICTCLAS 3 (http://www.ictclas.org) and the LIBSVM toolkit 4 (http://www.csie.ntu.edu.tw/~cjlin/libsvm/). The paper mentions software names but does not specify their version numbers. |
| Experiment Setup | Yes | The parameters in our method are set as minconf = 0.01 and W = 0.05. |