Multimodal Sentiment Analysis Based on Deep Learning: Recent Progress

Multimodal sentiment analysis is an important research topic in the field of NLP, aiming to analyze speakers\u27 sentiment tendencies through features extracted from textual, visual, and acoustic modalities. Its main methods are based on machine learning and deep learning. Machine learning-based methods rely heavily on labeled data. But deep learning-based methods can overcome this shortcoming and capture the in-depth semantic information and modal characteristics of the data, as well as the interactive information between multimodal data. In this paper, we survey the deep learning-based methods, including fusion of text and image and fusion of text, image, audio, and video. Specifically, we discuss the main problems of these methods and the future directions. Finally, we review the work of multimodal sentiment analysis in conversation

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

The application of a sentiment analysis approach to explore public understandings of animal agriculture

Ideas about farming are important components of consumers' value judgements about the foods they purchase and consume. Nevertheless, a divide exists between public perceptions and the realities of agricultural practices. We take a novel approach, using sentiment analysis, amongst other methods, to explore what consumers think about farming and how the visual elements of agricultural images might contribute to these perceptions. Data were drawn from responses to questions about three photographs of contemporary UK dairy farms, part of an online survey (n = 521), exploring public perceptions of food and farming. Sentiment and content analysis, descriptive statistics and Spearman's rank correlations were used to analyse the data. Participants thought good animal farming involves an evaluation of both farmers' skill and the relative ethical correctness of certain farming practices. Dirt and untidiness were linked with an increased likelihood of animal disease, and cleanliness and tidiness with a decreased likelihood. According to respondents, keeping cattle inside was problematic, whereas keeping animals outside is more appropriate, linked to their ability to graze in fields and the perceived goodness of a grass-based diet. Respondents discussed the need for farmers to be qualified, passionate and care for their animals. The paper concludes by reflecting on the use of images and sentiment analysis in this type of research, suggesting that along with certain benefits there are limitations to these methods

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

[EN] In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from the increasing user interaction when navigating. For this reason, in this survey we explore works in the line of prevention of risks that can arise from social interaction in online environments, focusing on works using Multi-Agent System (MAS) technologies. For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed. We review with special attention works using MAS technologies for user recommendation and guiding. Through the analysis of previous approaches on detection of the user state and risk prevention in SNSs we elaborate potential future lines of work that might lead to future applications where users can navigate and interact between each other in a more safe way.This work was funded by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A.; Espinosa Minguet, AR. (2020). A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis. Applied Sciences. 10(19):1-29. https://doi.org/10.3390/app10196746S1291019Vanderhoven, E., Schellens, T., Vanderlinde, R., & Valcke, M. (2015). Developing educational materials about risks on social network sites: a design based research approach. Educational Technology Research and Development, 64(3), 459-480. doi:10.1007/s11423-015-9415-4Teens and ICT: Risks and Opportunities. 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Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran