scholarly journals Cross-Modal Sentiment Sensing with Visual-Augmented Representation and Diverse Decision Fusion

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 74
Author(s):  
Sun Zhang ◽  
Bo Li ◽  
Chunyong Yin
Keyword(s):  
Sentiment Analysis ◽  
Decision Fusion ◽  
Visual Features ◽  
Final Decision ◽  
The Public ◽  
Proposed Model ◽  
The Social ◽  
Interactive Relationship ◽  
Multimodal Sentiment Analysis ◽  
Hybrid Fusion

The rising use of online media has changed the social customs of the public. Users have become accustomed to sharing daily experiences and publishing personal opinions on social networks. Social data carrying emotion and attitude has provided significant decision support for numerous tasks in sentiment analysis. Conventional methods for sentiment classification only concern textual modality and are vulnerable to the multimodal scenario, while common multimodal approaches only focus on the interactive relationship among modalities without considering unique intra-modal information. A hybrid fusion network is proposed in this paper to capture both inter-modal and intra-modal features. Firstly, in the stage of representation fusion, a multi-head visual attention is proposed to extract accurate semantic and sentimental information from textual contents, with the guidance of visual features. Then, multiple base classifiers are trained to learn independent and diverse discriminative information from different modal representations in the stage of decision fusion. The final decision is determined based on fusing the decision supports from base classifiers via a decision fusion method. To improve the generalization of our hybrid fusion network, a similarity loss is employed to inject decision diversity into the whole model. Empiric results on five multimodal datasets have demonstrated that the proposed model achieves higher accuracy and better generalization capacity for multimodal sentiment analysis.

scholarly journals Multi-Interactive Memory Network for Aspect Based Multimodal Sentiment Analysis

2019 ◽  
Vol 33 ◽  
pp. 371-378 ◽  
Author(s):  
Nan Xu ◽  
Wenji Mao ◽  
Guandan Chen
Keyword(s):  
Sentiment Analysis ◽  
Visual Information ◽  
Specific Aspect ◽  
Text And Image ◽  
Research Attention ◽  
Multimodal Data ◽  
Proposed Model ◽  
Memory Network ◽  
Multimodal Sentiment Analysis ◽  
The Given

As a fundamental task of sentiment analysis, aspect-level sentiment analysis aims to identify the sentiment polarity of a specific aspect in the context. Previous work on aspect-level sentiment analysis is text-based. With the prevalence of multimodal user-generated content (e.g. text and image) on the Internet, multimodal sentiment analysis has attracted increasing research attention in recent years. In the context of aspect-level sentiment analysis, multimodal data are often more important than text-only data, and have various correlations including impacts that aspect brings to text and image as well as the interactions associated with text and image. However, there has not been any related work carried out so far at the intersection of aspect-level and multimodal sentiment analysis. To fill this gap, we are among the first to put forward the new task, aspect based multimodal sentiment analysis, and propose a novel Multi-Interactive Memory Network (MIMN) model for this task. Our model includes two interactive memory networks to supervise the textual and visual information with the given aspect, and learns not only the interactive influences between cross-modality data but also the self influences in single-modality data. We provide a new publicly available multimodal aspect-level sentiment dataset to evaluate our model, and the experimental results demonstrate the effectiveness of our proposed model for this new task.

scholarly journals Microblog Sentiment Orientation Detection Using User Interactive Relationship

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Liang Wang ◽  
Mei Wang ◽  
Xinying Guo ◽  
Xuebin Qin
Keyword(s):  
Sentiment Analysis ◽  
Classification Problem ◽  
Research Field ◽  
Message Content ◽  
Analysis Framework ◽  
Data Set ◽  
The Social ◽  
Relationship Model ◽  
Interactive Relationship ◽  
Sentiment Orientation

The development and popularity of microblog have made sentiment analysis of tweets and Weibo an important research field. However, the characteristics of microblog message pose challenge for the sentiment analysis and mining. The existing approaches mostly focus on the message content and context information. In this paper, we propose a novel microblog sentiment analysis framework by incorporating the social interactive relationship factor in the content-based approach. By exploring the interactive relationship on social network based on posted messages, we build social interactive model to represent the opposition or acceptation behavior. Based on the interactive relationship model, the sentiment of microblog message with sparse emotion terms can be deduced and identified, and the sentiment uncertainty can be alleviated to some extent. Afterwards, we transform the classification problem into an optimization problem. Experimental results on Weibo data set indicate that the proposed method can outperform the baseline methods.

Proportionality and Principled Balancing

2010 ◽  
Vol 4 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Aharon Barak
Keyword(s):  
Human Rights ◽  
Final Decision ◽  
Judicial Discretion ◽  
General Criterion ◽  
Levels Of Abstraction ◽  
Constitutional Amendments ◽  
The Public ◽  
Independent Judiciary ◽  
The Social ◽  
The Mind

This essay focuses on proportionality stricto sensu as a consequential test of balancing. The basic balancing rule establishes a general criterion for deciding between the marginal benefit to the public good and the marginal limit to human rights. Based on the Israeli constitutional jurisprudence, this essay supports the adoption of a principled balancing approach that translates the basic balancing rule into a series of principled balancing tests, taking into account the importance of the rights and the type of restriction. This approach provides better guidance to the balancer (legislator, administrator, judge), restricts wide discretion in balancing, and makes the act of balancing more transparent, more structured, and more foreseeable.The advantages of proportionality stricto sensu with its three levels of abstraction are several. It stresses the need to always look for a justification of a limit on human rights; it structures the mind of the balancer; it is transparent; it creates a proper dialog between the political brunches and the judiciary, and it adds to the objectivity of judicial discretion. Proportionality stricto sensu however has it critics: some claim that it attempts to balance incommensurable items; others that balancing is irrational. The answer to the critics is that it is a common base for comparison, namely the social marginal importance and that the balancing rules—basic, principled, concrete—supply a rational basis for balancing. A democracy must entrust the judiciary—the unelected independent judiciary—to be the final decision-maker—subject to constitutional amendments—about proper ends that cannot be achieved because they are not proportionality stricto sensu.

scholarly journals SRR-LGR: Local–Global Information-Reasoned Social Relation Recognition for Human-Oriented Observation

2021 ◽  
Vol 13 (11) ◽  
pp. 2038
Author(s):  
Linbo Qing ◽  
Lindong Li ◽  
Yuchen Wang ◽  
Yongqiang Cheng ◽  
Yonghong Peng
Keyword(s):  
Public Space ◽  
Social Relations ◽  
Social Relation ◽  
Global Information ◽  
Semantic Features ◽  
The Public ◽  
Proposed Model ◽  
The Social ◽  
Scene Information ◽  
High Level

People’s interactions with each other form the social relations in society. Understanding human social relations in the public space is of great importance for supporting the public administrations. Recognizing social relations through visual data captured by remote sensing cameras is one of the most efficient ways to observe human interactions in a public space. Generally speaking, persons in the same scene tend to know each other, and the relations between person pairs are strongly correlated. The scene information in which people interact is also one of the important cues for social relation recognition (SRR). The existing works have not explored the correlations between the scene information and people’s interactions. The scene information has only been extracted on a simple level and high level semantic features to support social relation understanding are lacking. To address this issue, we propose a social relation structure-aware local–global model for SRR to exploit the high-level semantic global information of the scene where the social relation structure is explored. In our proposed model, the graph neural networks (GNNs) are employed to reason through the interactions (local information) between social relations and the global contextual information contained in the constructed scene-relation graph. Experiments demonstrate that our proposed local–global information-reasoned social relation recognition model (SRR-LGR) can reason through the local–global information. Further, the results of the final model show that our method outperforms the state-of-the-art methods. In addition, we have further discussed whether the global information contributes equally to different social relations in the same scene, by exploiting an attention mechanism in our proposed model. Further applications of SRR for human-observation are also exploited.

Scale to estimate the aspect-oriented sentiment polarity under anaphors influence (SPAI)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudarshan S. Sonawane ◽  
Satish R. Kolhe
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Design Methodology ◽  
Predictive Analytics ◽  
Anaphora Resolution ◽  
Content Type ◽  
Open Platform ◽  
Proposed Model ◽  
The Social ◽  
Media Platform

PurposeThe purpose of this paper is to handle the anaphors through anaphora resolution in aspect-oriented sentiment analysis. Sentiment analysis is one of the predictive analytics of social media. In particular, the social media platform Twitter is an open platform to post the opinion by subscribers on contextual issues, events, products, individuals and organizations.Design/methodology/approachThe sentiment polarity assessment is not deterministic to conclude the opinion of the target audience unless the polarity is assessed under diversified aspects. Hence, the aspect-oriented sentiment polarity assessment is a crucial objective of the opinion assessment over social media. However, the aspect-oriented sentiment polarity assessment often influences by the curse of anaphora resolution.FindingsFocusing on these limitations, a scale to estimate the aspects oriented sentiment polarity under anaphors influence has been portrayed in this article. To assess the aspect-based sentiment polarity of the tweets, the anaphors of the tweets have been considered to assess the weightage of the tweets toward the sentiment polarity.Originality/valueThe experimental study presents the performance of the proposed model by comparing it with the contemporary models, which are estimating the sentiment polarity tweets under anaphors impact.

Impact of cultural-shift on multimodal sentiment analysis

2021 ◽  
pp. 1-10
Author(s):  
Tulika Banerjee ◽  
Niraj Yagnik ◽  
Anusha Hegde
Keyword(s):  
Spanish Text ◽  
Sentiment Analysis ◽  
Visual Cues ◽  
Visual Features ◽  
Human Communication ◽  
Verbal Cues ◽  
Facial Emotions ◽  
Primary Focus ◽  
Multimodal Sentiment Analysis ◽  
The Impact

Human communication is not limited to verbal speech but is infinitely more complex, involving many non-verbal cues such as facial emotions and body language. This paper aims to quantitatively show the impact of non-verbal cues, with primary focus on facial emotions, on the results of multi-modal sentiment analysis. The paper works with a dataset of Spanish video reviews. The audio is available as Spanish text and is translated to English while visual features are extracted from the videos. Multiple classification models are made to analyze the sentiments at each modal stage i.e. for the Spanish and English textual datasets as well as the datasets obtained upon coalescing the English and Spanish textual data with the corresponding visual cues. The results show that the analysis of Spanish textual features combined with the visual features outperforms its English counterpart with the highest accuracy difference, thereby indicating an inherent correlation between the Spanish visual cues and Spanish text which is lost upon translation to English text.

scholarly journals Sentiment Analysis of Social Media via Multimodal Feature Fusion

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2010
Author(s):  
Kang Zhang ◽  
Yushui Geng ◽  
Jing Zhao ◽  
Jianxin Liu ◽  
Wenxiao Li
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Feature Fusion ◽  
Image Features ◽  
Research Field ◽  
Sentiment Classification ◽  
Analysis Model ◽  
Noise Interference ◽  
Proposed Model ◽  
Multimodal Sentiment Analysis

In recent years, with the popularity of social media, users are increasingly keen to express their feelings and opinions in the form of pictures and text, which makes multimodal data with text and pictures the con tent type with the most growth. Most of the information posted by users on social media has obvious sentimental aspects, and multimodal sentiment analysis has become an important research field. Previous studies on multimodal sentiment analysis have primarily focused on extracting text and image features separately and then combining them for sentiment classification. These studies often ignore the interaction between text and images. Therefore, this paper proposes a new multimodal sentiment analysis model. The model first eliminates noise interference in textual data and extracts more important image features. Then, in the feature-fusion part based on the attention mechanism, the text and images learn the internal features from each other through symmetry. Then the fusion features are applied to sentiment classification tasks. The experimental results on two common multimodal sentiment datasets demonstrate the effectiveness of the proposed model.

scholarly journals Turning Words Into Consumer Preferences: How Sentiment Analysis Is Framed in Research and the News Media

2018 ◽  
Vol 4 (3) ◽  
pp. 205630511879772 ◽  
Author(s):  
Cornelius Puschmann ◽  
Alison Powell
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
News Media ◽  
Public Discourse ◽  
Information Overload ◽  
Consumer Preferences ◽  
The Public ◽  
Attention Economy ◽  
The Social

Sentiment analysis is an increasingly popular instrument for the analysis of social media discourse. Sentiment scores seemingly represent an objective means of assessing the mood of social media users, consumers, and the public at large. Similar to other computational tools, sentiment analysis promises to reduce complexity and mitigate information overload, and to inform the decisions of marketers, pollsters, and scholars with reliable data. This article argues that the assumptions encoded into sentiment analysis as a method are accompanied by a number of constraints, both regarding its technical limitations (in terms of what sentiment analysis can and cannot accomplish) and conceptually (in terms of what the notion of sentiment implicitly represents), constraints which are often de-emphasized in public discourse. After providing an overview of its history and development in computer science as well as psychology and the social sciences, we turn to the role of sentiment as a currency in the attention economy. We then present a brief study of common framing of sentiment analysis in the news media, highlighting the expectations that exist regarding its analytical capabilities. We close by discussing the kind of conceptual work that takes place around computational methods such as sentiment analysis in specific cultural environments, highlighting their influence on the public imaginary.

Pelaksanaan Jaminan Kesehatan Oleh BPJS Kesehatan Dalam Perspektif Syariah

Liquidity ◽  
2017 ◽  
Vol 6 (2) ◽  
pp. 110-118
Author(s):  
Iwan Subandi ◽  
Fathurrahman Djamil
Keyword(s):  
Health Care ◽  
Financial Institution ◽  
Social Supports ◽  
Religious Leaders ◽  
The Public ◽  
The People ◽  
The Social ◽  
Ministry Of Finance ◽  
Health Support ◽  
The Government

Health is the basic right for everybody, therefore every citizen is entitled to get the health care. In enforcing the regulation for Jaringan Kesehatan Nasional (National Health Supports), it is heavily influenced by the foreign interests. Economically, this program does not reduce the people’s burdens, on the contrary, it will increase them. This means the health supports in which should place the government as the guarantor of the public health, but the people themselves that should pay for the health care. In the realization of the health support the are elements against the Syariah principles. Indonesian Muslim Religious Leaders (MUI) only say that the BPJS Kesehatan (Sosial Support Institution for Health) does not conform with the syariah. The society is asked to register and continue the participation in the program of Social Supports Institution for Health. The best solution is to enforce the mechanism which is in accordance with the syariah principles. The establishment of BPJS based on syariah has to be carried out in cooperation from the elements of Social Supports Institution (BPJS), Indonesian Muslim Religious (MUI), Financial Institution Authorities, National Social Supports Council, Ministry of Health, and Ministry of Finance. Accordingly, the Social Supports Institution for Helath (BPJS Kesehatan) based on syariah principles could be obtained and could became the solution of the polemics in the society.

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