In this project we analysed how much semantic information images carry, and how much value image data can add to sentiment analysis of the text associated with the images. To better understand the contribution from images, we compared models which only made use of image data, models which only made use of text data, and models which combined both data types. We also analysed if this approach could help sentiment classifiers generalize to unknown sentiments.
The most comparable set of experiments to ours were from @cite_0 . Their methodology for gathering images was very similar, applying filters to exclude examples with no image or text for example. Their dataset was also the most comparable size compared to ours, 250k datapoints, split almost equally between positive and negative classes. Other research that utilized the same dataset, @cite_2 for example, used a much larger set of 0.5 million images. We therefore use results of @cite_0 as benchmarks for our image only, text only, and combined models. We also include @cite_2 's results for image sentiment classification as a state of the art comparison for this task. Finally, whilst we experimented with both two (positive, negative), and three (positive, negative, neutral) sentiment classifiers, @cite_2 and @cite_0 only provide results for two classes. Consequently, we have no benchmarks for our three class sentiment classifiers.
@cite_0 @cite_2
2527200148 2253891449
Sentiment analysis is crucial for extracting social signals from social media content. Due to huge variation in social media, the performance of sentiment classifiers using single modality (visual or textual) still lags behind satisfaction. In this paper, we propose a new framework that integrates textual and visual information for robust sentiment analysis. Different from previous work, we believe visual and textual information should be treated jointly in a structural fashion. Our system first builds a semantic tree structure based on sentence parsing, aimed at aligning textual words and image regions for accurate analysis. Next, our system learns a robust joint visual-textual semantic representation by incorporating 1) an attention mechanism with LSTM (long short term memory) and 2) an auxiliary semantic learning task. Extensive experimental results on several known data sets show that our method outperforms existing the state-of-the-art joint models in sentiment analysis. We also investigate different tree-structured LSTM (T-LSTM) variants and analyze the effect of the attention mechanism in order to provide deeper insight on how the attention mechanism helps the learning of the joint visual-textual sentiment classifier. Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.