Unsupervised Sentiment Analysis with Signed Social Networks
Authors: Kewei Cheng, Jundong Li, Jiliang Tang, Huan Liu
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical experiments on two real-world datasets corroborate its effectiveness. |
| Researcher Affiliation | Academia | 1. Computer Science and Engineering, Arizona State University, Tempe, 85281, USA 2. Computer Science and Engineering, Michigan State University, East Lansing, 48824, USA |
| Pseudocode | Yes | Algorithm 1: Signed Senti Algorithm |
| Open Source Code | No | The paper does not provide any statement about making the source code available or include links to code repositories. |
| Open Datasets | Yes | We used two real-world datasets from Epinions and Slashdot which include both positive and negative links to perform unsupervised sentiment analysis. ... (Leskovec, Huttenlocher, and Kleinberg 2010b) |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits (e.g., percentages, sample counts, or specific methods for splitting). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using "Senti Word Net" as a sentiment lexicon but does not provide specific version numbers for any software dependencies or libraries used in the implementation or experimentation. |
| Experiment Setup | Yes | Noticed that in Sigend Senti, we have three regularization parameters α, β, γ. We empirically set these parameters as {α = 1, β = 0.5, γ = 0.7} in Epinions and {α = 1, β = 1, γ = 0.1} in Slashdot. |