Violence Rating Prediction from Movie Scripts
Authors: Victor R. Martinez, Krishna Somandepalli, Karan Singla, Anil Ramakrishna, Yalda T. Uhls, Shrikanth Narayanan671-678
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 victorrm@usc.edu Krishna Somandepalli University of Southern California Los Angeles, CA somandep@usc.edu Karan Singla University of Southern California Los Angeles, CA singlak@usc.edu Anil Ramakrishna University of Southern California Los Angeles, CA akramakr@usc.edu Yalda T. Uhls University of California Los Angeles Los Angeles, CA yaldatuhls@gmail.com Shrikanth Narayanan University of Southern California Los Angeles, CA shri@sipi.usc.edu |
| 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]. |