Sentiment Analysis for Images on Microblogging by Integrating Textual Information with Multiple Kernel Learning

Tan, Junxin; Xu, Mengting; Shang, Lin; Jia, Xiuyi

doi:10.1007/978-3-319-42911-3_41

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Single textual model: A single textual model is a sentiment analysis method based on textual features. Tan et al [67] proposed a model using multikernel learning to extract text features as the input of a support vector machine to analyze sentiment polarity. Le and Mikolov [68] proposed an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts.…”

Section: B Evaluation Metrics and Baselinesmentioning

confidence: 99%

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Transfer Correlation Between Textual Content to Images for Sentiment Analysis

et al. 2020

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In social media, images and texts are used to convey individuals' attitudes and feelings; thus, social media has become an indispensable part of people's lives. To understand social behavior and provide better recommendations, sentiment analysis on social media is helpful. One sentiment analysis task is polarity prediction. Although current research on visual or textual sentiment analysis has achieved quite good progress, multimodal and cross-modal analysis combining visual and textual correlation is still in the exploration stage. To capture a semantic connection between images and captions, this paper proposes a cross-modal approach that considers both images and captions in classifying image sentiment polarity. This method transfers the correlation between textual content to images. First, the image and its corresponding caption are sent into an inner-class mapping model, where they are transformed into vectors in Hilbert space to obtain their labels by calculating the inner-class maximum mean discrepancy (MMD). Then, a class-aware sentence representation (CASR) model assigns the distributed representation to the labels with a class-aware attention-based gated recurrent unit (GRU). Finally, an inner-class dependency LSTM (IDLSTM) classifies the sentiment polarity. Experiments carried out on the Getty Images dataset and Twitter 1269 dataset demonstrate the effectiveness of our approach. Moreover, extensive experimental results show that our model outperforms baseline solutions. INDEX TERMS Correlation, cross-modal, transfer, sentiment analysis.

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Section: B Evaluation Metrics and Baselinesmentioning

confidence: 99%

“…As shown in Table 4, the recall of single text feature models [67], [68] is generally lower than that of other models, and the recall of the single visual model Siersdorfer et al [69] proposed is the highest (84.0%). The cross-modal model You et al [71] proposed has the highest precision of 84.6%.…”

Section: Performance On Getty Imagesmentioning

confidence: 99%

Transfer Correlation Between Textual Content to Images for Sentiment Analysis

et al. 2020

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“…A picture contains a lot of information, which can reflect the user's emotional tendencies. Reference [7] proposes a Cross-modality Consistent Regression (CCR), which trains paragraph vector model and multi-modal regression model of text sentiment analysis on the basis of fine-tuning convolution neural network in order to achieve consistency among different modal features; Reference [8] proposed an emotional analysis method based on Simple Multiple Kernel Learning (SimpleMK). The main idea is to use text information to improve the ability of image classification through SimpleMKL.…”

Section: Image Sentiment Analysismentioning

confidence: 99%

“…The study in document [8] used two data sets, one from Flickr (including pictures and text descriptions) and the other from Twitter. Two sets of experiments were set up.…”

Section: Image Preprocessingmentioning

confidence: 99%

The Multimedia Sentiment Model Based on Online Homestay Reviews

Wenguang¹,

Li²,

Yu³

et al. 2020

IJEM

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Aiming at the fact that traditional sentiment analysis is based on text, without considering the factors such as special symbols and emoticon images, which can't fully extract the user's emotions, this paper proposes a sentiment analysis method of online homestay reviews based on image-text fusion. For text datasets, first use Word2vec to build a topic clustering model, then find the corresponding topic attribute dictionary through the topic center words, use Bayesian classifier is used for sentiment analysis, compared with SVM and decision tree methods, to evaluate the effect; For the picture dataset, Convolutional Neural Network (CNN) model is initialized by parameter migration, and image sentiment classification model is obtained by fine-tuning training of CNN model after parameter migration; Finally, the fusion method is designed to calculate the emotional probability of image-text, then judge the emotional polarity and compare it with the user's score, The accuracy rate is 88.6%, which is higher than that of text sentiment analysis model or image sentiment analysis model. The experimental results show that the sentiment analysis of image-text fusion has better classification effect on image-text reviews and more effectively avoid the problem of inconsistency between user ratings and the emotion expressed in comments.

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Harnessing the Power of Data Science to Grasp Insights About Human Behaviour, Thinking, and Feeling from Social Media Images

Dudău

2021

Computational Social Sciences

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Sentiment Analysis for Images on Microblogging by Integrating Textual Information with Multiple Kernel Learning

Cited by 6 publications

References 16 publications

Transfer Correlation Between Textual Content to Images for Sentiment Analysis

Transfer Correlation Between Textual Content to Images for Sentiment Analysis

The Multimedia Sentiment Model Based on Online Homestay Reviews

Harnessing the Power of Data Science to Grasp Insights About Human Behaviour, Thinking, and Feeling from Social Media Images

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