ad5c9772c273eabe06401bb0d4375b345ea81993
Multi-Modal Urdu Sentiment Analysis: Enhancing Accuracy through Audio-Visual Cues
Current Urdu sentiment analysis models are limited to text-based classification, ignoring crucial audio-visual cues present in spoken Urdu that often convey important sentiment information. This limitation leads to incomplete or inaccurate sentiment analysis, particularly in cases where tone, facial expressions, and gestures play a significant role in conveying sentiment.
Existing approaches to Urdu sentiment analysis rely solely on textual data, which fails to capture the full spectrum of sentiment expression in Urdu communication. Incorporating multi-modal information can significantly enhance the accuracy of sentiment analysis by capturing subtle cues expressed through tone, facial expressions, and gestures that are particularly important in Urdu communication. This approach aligns with the natural way humans interpret sentiment in face-to-face interactions, potentially leading to more nuanced and accurate sentiment classification.
We propose a novel multi-modal Urdu sentiment analysis framework that integrates textual, audio, and visual information. The architecture consists of three main components: 1) A text encoder using a Urdu-specific transformer model to process textual input. 2) An audio encoder employing a 1D convolutional network to extract prosodic features from speech. 3) A visual encoder utilizing a 3D convolutional network to capture facial expressions and gestures. We introduce a cross-modal attention mechanism that allows each modality to attend to relevant features in other modalities. The final sentiment classification is performed using a fusion layer that dynamically weights the contribution of each modality based on their reliability for a given input.
Step 1: Data Collection and Preprocessing
Collect a large-scale dataset of Urdu video clips with sentiment annotations. Ensure diversity in speakers, topics, and sentiment expressions. Preprocess the data by extracting text transcripts, audio features, and visual frames from the videos.
Step 2: Model Architecture Implementation
Implement the three-branch architecture using PyTorch. Use a pre-trained Urdu-specific BERT model for the text encoder, a 1D CNN for the audio encoder, and a 3D CNN (e.g., I3D) for the visual encoder. Implement the cross-modal attention mechanism and fusion layer.
Step 3: Training Setup
Split the dataset into training, validation, and test sets. Implement data loaders for efficient multi-modal data handling. Set up the training loop with appropriate loss function (e.g., cross-entropy for sentiment classification) and optimizer (e.g., Adam).
Step 4: Model Training and Validation
Train the model on the training set, monitoring performance on the validation set. Experiment with different hyperparameters, including learning rate, batch size, and fusion strategies. Implement early stopping based on validation performance.
Step 5: Baseline Comparisons
Implement and train unimodal baselines (text-only, audio-only, video-only) and simpler fusion techniques (e.g., late fusion) for comparison. Use the same dataset splits for fair comparison.
Step 6: Evaluation on Test Set
Evaluate the best performing multi-modal model and baselines on the held-out test set. Use standard classification metrics such as accuracy, F1-score, and confusion matrix. Implement additional metrics to measure the model's ability to exploit multi-modal cues effectively.
Step 7: Analysis and Ablation Studies
Conduct ablation studies to understand the contribution of each modality and the effectiveness of the cross-modal attention mechanism. Analyze cases where the multi-modal model outperforms unimodal baselines and vice versa.
Step 8: Generalization Tests
Test the model on existing text-only Urdu sentiment datasets to ensure backward compatibility and assess generalization capabilities.
Baseline Prompt Input (Text-Only Model)
یہ فلم بہت اچھی تھی، لیکن اس کا اختتام تھوڑا مایوس کن تھا۔
Baseline Prompt Expected Output (Text-Only Model)
Neutral
Proposed Prompt Input (Multi-Modal Model)
[Video clip of a person saying: 'یہ فلم بہت اچھی تھی، لیکن اس کا اختتام تھوڑا مایوس کن تھا۔' with a disappointed facial expression and a slightly frustrated tone]
Proposed Prompt Expected Output (Multi-Modal Model)
Negative
Explanation
The text-only model fails to capture the negative sentiment conveyed through the speaker's tone and facial expression, classifying the sentiment as neutral based solely on the balanced textual content. In contrast, the multi-modal model correctly identifies the overall negative sentiment by incorporating audio-visual cues.
If the proposed multi-modal approach does not significantly outperform the text-only baseline, we will conduct a thorough error analysis to understand the limitations. This may involve examining cases where audio-visual cues contradict textual sentiment, or where one modality dominates the others. We could then explore alternative fusion techniques, such as hierarchical attention or dynamic weighting mechanisms. Additionally, we might investigate the quality and representativeness of our multi-modal dataset, potentially expanding or refining it to better capture the nuances of Urdu sentiment expression. Another direction could be to focus on specific sub-tasks where multi-modal cues are particularly important, such as sarcasm detection or emotion intensity prediction in Urdu. This could lead to valuable insights into the challenges of multi-modal sentiment analysis in Urdu and potentially inform future research directions in this area.