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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Violence Rating Prediction from Movie Scripts
Authors: Victor R. Martinez, Krishna Somandepalli, Karan Singla, Anil Ramakrishna, Yalda T. Uhls, Shrikanth Narayanan671-678
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We tested our models on a dataset of 732 Hollywood scripts annotated by experts for violent content. Our performance evaluation suggests that linguistic features are a good indicator for violent content. Furthermore, our ablation studies show that semantic and sentiment features are the most important predictors of violence in this data. |
| Researcher Affiliation | Academia | Victor R. Martinez University of Southern California Los Angeles, CA EMAIL Krishna Somandepalli University of Southern California Los Angeles, CA EMAIL Karan Singla University of Southern California Los Angeles, CA EMAIL Anil Ramakrishna University of Southern California Los Angeles, CA EMAIL Yalda T. Uhls University of California Los Angeles Los Angeles, CA EMAIL Shrikanth Narayanan University of Southern California Los Angeles, CA EMAIL |
| Pseudocode | No | The paper includes a figure illustrating the neural network architecture (Figure 1), but it does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code to replicate all experiments is publicly available4. (footnote 4: https://github.com/usc-sail/mica-violence-ratings-predictions-from-movie-scripts) |
| Open Datasets | Yes | We use the movie screenplays collected by (Ramakrishna et al. 2017), an extension to Movie-Di C (Banchs 2012). |
| Dataset Splits | Yes | We estimated model s performance and optimal penalty parameter C [0.01, 1, 10, 100, 1000] through nested 5-fold cross validation (CV). Albeit uncommon in most deep-learning approaches, we opted for 5-fold CV to estimate our model s performance. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | Linear SVC was implemented using scikit-learn (Pedregosa et al. 2011). RNN models were implemented in Keras (Chollet and others 2015). While specific libraries are mentioned, no version numbers for these software dependencies are provided. |
| Experiment Setup | Yes | We used the Adam optimizer with mini-batch size of 16 and learning rate of 0.001. To prevent over-fitting, we use drop-out of 0.5, and train until convergence (i.e., consecutive loss with less than 10 8 difference). Both models were trained with number of hidden units H [4, 8, 16, 32]. |