Opinion-aware Knowledge Graph for Political Ideology Detection
Authors: Wei Chen, Xiao Zhang, Tengjiao Wang, Bishan Yang, Yi Li
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our method achieves high accuracy in detecting ideologies compared to baselines including LR, SVM and RNN. |
| Researcher Affiliation | Academia | Key Lab of High Confidence Software Technologies (MOE), School of EECS, Peking University Computer Science Department, Carnegie Mellon University |
| Pseudocode | No | The paper describes the proposed framework and its components in text but does not include pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing open-source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | The U.S. Congressional floor debate (Convote) dataset [Thomas et al., 2006]: it consists of debate transcripts of the US Congress in 2005. The IBC dataset [Iyyer et al., 2014]: it includes sentences selected from the Ideological Books Corpus, a collection of books and magazine articles written between 2008 and 2012 by authors with well-known political leanings. The Twitter dataset: Bakshy et al. [2015] lists the media with their ideology leanings. We further find their corresponding twitter accounts. We collect tweets (provided by Sasa Petrovic1) posted by these twitter accounts from Jul., 2011 to Sep., 2011. We use DBPedia2 [Bizer et al., 2009] as a source of background knowledge graph. (Footnote 1: http://demeter.inf.ed.ac.uk/cross/docs/fsd_corpus.tar.gz, Footnote 2: http://wiki.dbpedia.org) |
| Dataset Splits | Yes | We perform 10-fold cross-validation on Convote and IBC datasets for each method except for the RNN-serial models4. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions tuning parameters with 'machine learning library scikit-learn3' but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions 'tuned the parameters of SVM and LR with cross-validation' but does not provide specific hyperparameter values or detailed training configurations for any of the models, including the proposed OKG. |