Multimodal Sentiment Analysis Project(Persian-3classes)
Predicting sentiment from Persian Instagram posts by fusing VGG-16 image features with a bidirectional LSTM over the text.
Amirreza Vishteh· 1 min read
Overview
In this project, we explore multimodal sentiment analysis, which involves analyzing both text and image data together. Our goal is to predict sentiment labels (positive or negative) based on combined information from text and images.
Dataset Description
- We obtained a dataset from Instagram that includes both text and image content.
- The dataset consists of 1000 samples, each labeled as either positive or negative sentiment.
- Text data is provided in an Excel file, while image data is stored in a RAR file.
Model Architecture
Our multimodal sentiment analysis model combines features from both modalities:
- Image Modality:
- We use a pre-trained 16-layer VGG CNN to extract image features.
- We use a pre-trained 16-layer VGG CNN to extract image features.
- Text Modality:
- We employ a bidirectional LSTM (LSTM-BI) network to process text data.
- The features extracted from both modalities are concatenated to form a joint representation.
Results
- Our model achieved an accuracy of 69% on sentiment prediction.
- Additional performance metrics and findings can be included here.
