Improving Opinion Aspect Extraction Using Semantic Similarity and Aspect Associations
Authors: Qian Liu, Bing Liu, Yuanlin Zhang, Doo Soon Kim, Zhiqiang Gao
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results using eight review datasets show the effectiveness of the proposed approach. |
| Researcher Affiliation | Collaboration | 1Key Lab of Computer Network and Information Integration (Southeast University), Ministry of Education, China 2School of Computer Science and Engineering, Southeast University, China 3Department of Computer Science, University of Illinois at Chicago, USA 4Department of Computer Science, Texas Tech University, USA 5Bosch research lab, USA |
| Pseudocode | Yes | Algorithm 1 AER(Dt, R , R+, O); Algorithm 2 Sim-recom(T , T ); Algorithm 3 AR-recom(T , T ). |
| Open Source Code | No | The paper mentions using an "open source tool word2vec" but does not state that its own methodology's source code is publicly available. |
| Open Datasets | Yes | We thus use two publicly available aspect-annotated corpora2. One is from (Hu and Liu 2004)... The other is from (Liu et al. 2015). 2http://www.cs.uic.edu/ liub/FBS/sentiment-analysis.html |
| Dataset Splits | No | The paper mentions using "Test Datasets for Evaluation" and discusses training word vectors and mining association rules from other datasets, but it does not specify explicit training, validation, and test splits for its proposed AER model in the typical sense. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper mentions using "word2vec" and "Stanford Parser" but does not specify their version numbers. |
| Experiment Setup | Yes | We empirically set the parameters for aspect similarities as ϵ = 0.38 in Algorithm 2, set the parameters for generating association rules as minimum confidence = 0.5 and minimum support = 0.2. |