Multimodal Sentimental Analysis for Tweets
Publication Date : 15/05/2020
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Social media users are increasingly using both images and text to express their opinions and share their experiences, instead of only using text in the conventional social media. Consequently, the conventional text-based sentiment analysis has evolved into more complicated studies of multimodal sentiment analysis. To tackle the challenge of how to effectively exploit the information from both visual content and textual content from image-text posts. The proposed approach explores the correlation between the image and the text, followed by a multimodal sentiment analysis method. To be more specific, visual features extracted by the convolutional neural network algorithm is used to represent visual concepts, to develop a machine learning sentiment analysis approach. Extensive experiments are conducted to demonstrate the superior performance of the proposed approach. The reviews are classified as positive or negative sentiments by fusion of results from each mode.
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