🔥 🔥 🔥 Notice: This repository will no longer be maintained. Instead, we are are moving all our multimodal works to this new centralized repository: https://github.com/declare-lab/multimodal-deep-learning.

Context-Dependent Sentiment Analysis in User-Generated Videos

Code for the paper Context-Dependent Sentiment Analysis in User-Generated Videos (ACL 2017).

NOTE: Here is the updated version of the code - https://github.com/soujanyaporia/multimodal-sentiment-analysis

Requirements

Code is written in Python (2.7) and requires Keras (2.0.6) with Theano backend.

Description

In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process in multimodal sentiment analysis.

This repository contains the code for the mentioned paper. Each contextual LSTM (Figure 2 in the paper) is implemented as shown in above figure. For more details, please refer to the paper.
Note: Unlike the paper, we haven't used an SVM on the penultimate layer. This is in effort to keep the whole network differentiable at some performance cost.

Dataset

We provide results on the MOSI dataset
Please cite the creators

Preprocessing

As data is typically present in utterance format, we combine all the utterances belonging to a video using the following code

python create_data.py

Note: This will create speaker independent train and test splits

Running sc-lstm

Sample command:

python lstm.py --unimodal True
python lstm.py --unimodal False

Note: Keeping the unimodal flag as True (default False) shall train all unimodal lstms first (level 1 of the network mentioned in the paper)

Citation

If using this code, please cite our work using :

@inproceedings{soujanyaacl17,
  title={Context-dependent sentiment analysis in user-generated videos},
  author={Poria, Soujanya  and Cambria, Erik and Hazarika, Devamanyu and Mazumder, Navonil and Zadeh, Amir and Morency, Louis-Philippe},
  booktitle={Association for Computational Linguistics},
  year={2017}
}

Credits

Devamanyu Hazarika, Soujanya Poria