Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unsupervised Sentiment Analysis with Signed Social Networks
Authors: Kewei Cheng, Jundong Li, Jiliang Tang, Huan Liu
AAAI 2017 | Venue PDF | 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. |