Multimodal Sentiment Dataset

Key Features:

• Multimodal Data: The dataset combines both images and textual descriptions with sentiment labels (positive, negative, or neutral), facilitating the training of models for multimodal sentiment analysis.
• Diverse Classes: It features 100 distinct categories of animals and objects, enabling robust testing across varied domains.
• Sentiment Annotations: Each image-text pair is annotated with sentiment, allowing for exploration in emotion recognition, opinion mining, and subjectivity analysis. This is particularly useful for tasks where the goal is to gauge sentiment from both visual and textual cues.

Potential Applications:

• Image Classification: The dataset is suitable for building and fine-tuning models that can classify images into one of the 100 classes.
• Sentiment Analysis: Researchers can use the dataset to perform sentiment analysis, identifying the sentiment expressed not only in the text but also as inferred from the image.
• Image Captioning: The text associated with each image allows for experiments in automatic image captioning, where models generate text descriptions for images based on learned features.
• Multimodal Fusion: The dataset is valuable for tasks involving multimodal fusion, where both textual and visual data are combined to predict or classify sentiment, offering challenges in integrating diverse data types.

Dataset Composition:

• Image Data: High-quality images representing various animals and objects across 100 classes.
• Textual Data: Descriptive sentences or phrases related to the images, providing context and enhancing the sentiment prediction process.
• Sentiment Labels: Sentiment annotations (positive, negative, neutral) associated with each image-text pair, ensuring that the data is ready for supervised learning tasks.

Use Cases:

• Emotion Detection in Media: This dataset can be applied to sentiment analysis of social media posts, reviews, or news articles that feature both images and text, helping models better understand user emotions across multiple modalities.
• Multimodal Chatbots: Improve chatbot performance by integrating both image and text recognition, enabling more personalized and sentiment-aware responses.
• Content Moderation: Automatically detect inappropriate or harmful content by combining textual sentiment with visual sentiment cues.