1,400 research outputs found
A survey on deep learning in image polarity detection: Balancing generalization performances and computational costs
Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost
Sensing Human Sentiment via Social Media Images: Methodologies and Applications
abstract: Social media refers computer-based technology that allows the sharing of information and building the virtual networks and communities. With the development of internet based services and applications, user can engage with social media via computer and smart mobile devices. In recent years, social media has taken the form of different activities such as social network, business network, text sharing, photo sharing, blogging, etc. With the increasing popularity of social media, it has accumulated a large amount of data which enables understanding the human behavior possible. Compared with traditional survey based methods, the analysis of social media provides us a golden opportunity to understand individuals at scale and in turn allows us to design better services that can tailor to individuals’ needs. From this perspective, we can view social media as sensors, which provides online signals from a virtual world that has no geographical boundaries for the real world individual's activity.
One of the key features for social media is social, where social media users actively interact to each via generating content and expressing the opinions, such as post and comment in Facebook. As a result, sentiment analysis, which refers a computational model to identify, extract or characterize subjective information expressed in a given piece of text, has successfully employs user signals and brings many real world applications in different domains such as e-commerce, politics, marketing, etc. The goal of sentiment analysis is to classify a user’s attitude towards various topics into positive, negative or neutral categories based on textual data in social media. However, recently, there is an increasing number of people start to use photos to express their daily life on social media platforms like Flickr and Instagram. Therefore, analyzing the sentiment from visual data is poise to have great improvement for user understanding.
In this dissertation, I study the problem of understanding human sentiments from large scale collection of social images based on both image features and contextual social network features. We show that neither
visual features nor the textual features are by themselves sufficient for accurate sentiment prediction. Therefore, we provide a way of using both of them, and formulate sentiment prediction problem in two scenarios: supervised and unsupervised. We first show that the proposed framework has flexibility to incorporate multiple modalities of information and has the capability to learn from heterogeneous features jointly with sufficient training data. Secondly, we observe that negative sentiment may related to human mental health issues. Based on this observation, we aim to understand the negative social media posts, especially the post related to depression e.g., self-harm content. Our analysis, the first of its kind, reveals a number of important findings. Thirdly, we extend the proposed sentiment prediction task to a general multi-label visual recognition task to demonstrate the methodology flexibility behind our sentiment analysis model.Dissertation/ThesisDoctoral Dissertation Computer Science 201
Automatic domain ontology extraction for context-sensitive opinion mining
Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline
A Deep Multi-Level Attentive network for Multimodal Sentiment Analysis
Multimodal sentiment analysis has attracted increasing attention with broad
application prospects. The existing methods focuses on single modality, which
fails to capture the social media content for multiple modalities. Moreover, in
multi-modal learning, most of the works have focused on simply combining the
two modalities, without exploring the complicated correlations between them.
This resulted in dissatisfying performance for multimodal sentiment
classification. Motivated by the status quo, we propose a Deep Multi-Level
Attentive network, which exploits the correlation between image and text
modalities to improve multimodal learning. Specifically, we generate the
bi-attentive visual map along the spatial and channel dimensions to magnify
CNNs representation power. Then we model the correlation between the image
regions and semantics of the word by extracting the textual features related to
the bi-attentive visual features by applying semantic attention. Finally,
self-attention is employed to automatically fetch the sentiment-rich multimodal
features for the classification. We conduct extensive evaluations on four
real-world datasets, namely, MVSA-Single, MVSA-Multiple, Flickr, and Getty
Images, which verifies the superiority of our method.Comment: 11 pages, 7 figure
Affective Image Content Analysis: Two Decades Review and New Perspectives
Images can convey rich semantics and induce various emotions in viewers.
Recently, with the rapid advancement of emotional intelligence and the
explosive growth of visual data, extensive research efforts have been dedicated
to affective image content analysis (AICA). In this survey, we will
comprehensively review the development of AICA in the recent two decades,
especially focusing on the state-of-the-art methods with respect to three main
challenges -- the affective gap, perception subjectivity, and label noise and
absence. We begin with an introduction to the key emotion representation models
that have been widely employed in AICA and description of available datasets
for performing evaluation with quantitative comparison of label noise and
dataset bias. We then summarize and compare the representative approaches on
(1) emotion feature extraction, including both handcrafted and deep features,
(2) learning methods on dominant emotion recognition, personalized emotion
prediction, emotion distribution learning, and learning from noisy data or few
labels, and (3) AICA based applications. Finally, we discuss some challenges
and promising research directions in the future, such as image content and
context understanding, group emotion clustering, and viewer-image interaction.Comment: Accepted by IEEE TPAM
Multimodal Grounding for Language Processing
This survey discusses how recent developments in multimodal processing
facilitate conceptual grounding of language. We categorize the information flow
in multimodal processing with respect to cognitive models of human information
processing and analyze different methods for combining multimodal
representations. Based on this methodological inventory, we discuss the benefit
of multimodal grounding for a variety of language processing tasks and the
challenges that arise. We particularly focus on multimodal grounding of verbs
which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference
of Computational Linguistics. Please refer to this version for citations:
https://www.aclweb.org/anthology/papers/C/C18/C18-1197
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