scholarly journals Design of Festival Sentiment Classifier Based on Social Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Huilin Yuan ◽  
Yufan Song ◽  
Jianlu Hu ◽  
Yatao Ma
Keyword(s):  
Chinese Text ◽  
Data Augmentation ◽  
Cultural Inheritance ◽  
Text Data ◽  
Business Decisions ◽  
Sina Weibo ◽  
The Public ◽  
Cultural Festivals ◽  
Text Information ◽  
Text Sentiment Analysis

With the development of society, more and more attention has been paid to cultural festivals. In addition to the government’s emphasis, the increasing consumption in festivals also proves that cultural festivals are playing increasingly important role in public life. Therefore, it is very vital to grasp the public festival sentiment. Text sentiment analysis is an important research content in the field of machine learning in recent years. However, at present, there are few studies on festival sentiment, and sentiment classifiers are also limited by domain or language. The Chinese text classifier is much less than the English version. This paper takes Sina Weibo as the text information carrier and Chinese festival microblogs as the research object. CHN-EDA is used to do Chinese text data augmentation, and then the traditional classifiers CNN, DNN, and naïve Bayes are compared to obtain a higher accuracy. The matching optimizer is selected, and relevant parameters are determined through experiments. This paper solves the problem of unbalanced Chinese sentiment data and establishes a more targeted festival text classifier. This festival sentiment classifier can collect public festival emotion effectively, which is beneficial for cultural inheritance and business decisions adjustment.

scholarly journals A Commodity Classification Framework Based on Machine Learning for Analysis of Trade Declaration

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 964
Author(s):  
Mingshu He ◽  
Xiaojuan Wang ◽  
Chundong Zou ◽  
Bingying Dai ◽  
Lei Jin
Keyword(s):  
Machine Learning ◽  
Chinese Text ◽  
Decision Fusion ◽  
High Rate ◽  
Text Recognition ◽  
Fusion Model ◽  
Commodity Trade ◽  
Classification Framework ◽  
Text Information ◽  
Application Fields

Text, voice, images and videos can express some intentions and facts in daily life. By understanding these contents, people can identify and analyze some behaviors. This paper focuses on the commodity trade declaration process and identifies the commodity categories based on text information on customs declarations. Although the technology of text recognition is mature in many application fields, there are few studies on the classification and recognition of customs declaration goods. In this paper, we proposed a classification framework based on machine learning (ML) models for commodity trade declaration that reaches a high rate of accuracy. This paper also proposed a symmetrical decision fusion method for this task based on convolutional neural network (CNN) and transformer. The experimental results show that the fusion model can make up for the shortcomings of the two original models and some improvements have been made. In the two datasets used in this paper, the accuracy can reach 88% and 99%, respectively. To promote the development of study of customs declaration business and Chinese text recognition, we also exposed the proprietary datasets used in this study.

Method for hiding text data in an image

2021 ◽  
Vol 83 (1) ◽  
pp. 72-79
Author(s):  
O.A. Kan ◽  
◽  
N.A. Mazhenov ◽  
K.B. Kopbalina ◽  
G.B. Turebaeva ◽  
...  
Keyword(s):  
Secret Message ◽  
Source Image ◽  
Hidden Information ◽  
Text Data ◽  
Code Table ◽  
Ascii Code ◽  
Text Information ◽  
Graphic File ◽  
Image Pixels ◽  
Digital Fingerprints

The main problem: The article deals with the issues of hiding text information in a graphic file. A formula for hiding text information in image pixels is proposed. A steganography scheme for embedding secret text in random image pixels has been developed. Random bytes are pre-embedded in each row of pixels in the source image. As a result of the operations performed, a key image is obtained. The text codes are embedded in random bytes of pixels of a given RGB channel. To form a secret message, the characters of the ASCII code table are used. Demo encryption and decryption programs have been developed in the Python 3.5.2 programming language. A graphic file is used as the decryption key. Purpose: To develop an algorithm for embedding text information in random pixels of an image. Methods: Among the methods of hiding information in graphic images, the LSB method of hiding information is widely used, in which the lower bits in the image bytes responsible for color encoding are replaced by the bits of the secret message. Analysis of methods of hiding information in graphic files and modeling of algorithms showed an increase in the level of protection of hidden information from detection. Results and their significance: Using the proposed steganography scheme and the algorithm for embedding bytes of a secret message in a graphic file, protection against detection of hidden information is significantly increased. The advantage of this steganography scheme is that for decryption, a key image is used, in which random bytes are pre-embedded. In addition, the entire pixel bits of the container image are used to display the color shades. It can also be noted that the developed steganography scheme allows not only to transmit secret information, but also to add digital fingerprints or hidden tags to the image.

scholarly journals Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

2021 ◽  
Vol 2083 (4) ◽  
pp. 042044
Author(s):  
Zuhua Dai ◽  
Yuanyuan Liu ◽  
Shilong Di ◽  
Qi Fan
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Structural Information ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Text Data ◽  
Short Text ◽  
Fine Grained ◽  
Syntactic Information ◽  
Text Information

Abstract Aspect level sentiment analysis belongs to fine-grained sentiment analysis, w hich has caused extensive research in academic circles in recent years. For this task, th e recurrent neural network (RNN) model is usually used for feature extraction, but the model cannot effectively obtain the structural information of the text. Recent studies h ave begun to use the graph convolutional network (GCN) to model the syntactic depen dency tree of the text to solve this problem. For short text data, the text information is not enough to accurately determine the emotional polarity of the aspect words, and the knowledge graph is not effectively used as external knowledge that can enrich the sem antic information. In order to solve the above problems, this paper proposes a graph co nvolutional neural network (GCN) model that can process syntactic information, know ledge graphs and text semantic information. The model works on the “syntax-knowled ge” graph to extract syntactic information and common sense information at the same t ime. Compared with the latest model, the model in this paper can effectively improve t he accuracy of aspect-level sentiment classification on two datasets.

scholarly journals O interesse público na sociedade de economia mista brasileira: aproximação com a benefit corporation do direito norte-americano

2020 ◽  
Vol 279 (1) ◽  
pp. 79
Author(s):  
Mario Engler Pinto Junior
Keyword(s):  
Corporate Governance ◽  
Public Interest ◽  
Institutional Design ◽  
Profit Maximization ◽  
Business Decisions ◽  
The Public ◽  
Benefit Corporation ◽  
The Us ◽  
Benefit Corporations ◽  
Shed Light

<p><span>The public interest of Brazilian mixed-capital company: approach to US benefit corporations</span></p><p><span><br /></span></p><p><span>RESUMO<br />O artigo faz um paralelo entre a figura da benefit corporation do direito norte-americano e a sociedade de economia mista brasileira, com o propósito de apontar semelhanças entre as duas estruturas societárias e lançar luzes sobre a racionalidade das soluções de governança adotadas em cada caso. A reflexão resgata inicialmente o conceito de interesse da companhia, destacando sua relevância como referencial jurídico para se aferir a legitimidade das decisões empresariais. Observa-se ainda que o entendimento sobre o tema varia conforme a abordagem teórica adotada, podendo se resumir na maximização dos lucros para partilha entre os sócios, ou combinar o atendimento a outros interesses não financeiros. Por sua vez, os desafios e soluções em matéria de governança corporativa também variam em função da amplitude do escopo atribuído à companhia. A benefit corporation procura combinar a consecução de algum objetivo de interesse público com a manutenção da finalidade lucrativa. A existência do escopo mais amplo permite questionar a adequação do desenho institucional para lidar com os conflitos inerentes ao novo tipo societário. Além disso, propicia uma análise comparativa com o modelo de sociedade de economia mista no direito brasileiro, que também está imbuída de uma missão pública, cuja consecução não afasta a necessidade de remunerar adequadamente o investimento acionário. Conclui-se que algumas medidas contidas na Lei nº 13.303/2016, para fortalecer o controle e gestão das empresas estatais brasileiras, guardam simetria com o tratamento aplicável às benefit corporation no direito norte-americano.</span></p><p><span><br /></span></p><p><span>ABSTRACT<br />The paper compares benefit corporations in the US with mixed-capital corporations in Brazil, in order to point the similarities and differences between both corporate structures. The paper also intends to shed light on the rationale of the governance solutions adopted in each case. The paper restates the concept of company’s interest and highlights it as a key legal reference for assessing the legitimacy of business decisions. Different readings of this concept are likely to translate into markedly different positions, from holding that the idea of interest refers solely to the purpose of profit maximization on behalf of shareholders to affirming the need to simultaneously accomplishing non-financial goals interests. The challenges and solutions concerning corporate governance also vary according to the extent of the corporation’s scope. Benefit corporations in the US seek to </span><span>simultaneously attain some goal of public interest and make profit for </span><span>its shareholders. The existence of a broader scope allows questioning </span><span>the suitability of their institutional design to deal with conflicts that are </span><span>inherent to this new corporate type. Their structure invites a comparison </span><span>to State owned enterprise (SOE) in Brazil. According to Brazilian Law, a </span><span>company controlled by the State is invested with a public mission while </span><span>needing to assure proper return to shareholders’ investment. The paper </span><span>concludes that some measures adopted by Brazilian Law No. 13.303/2016, </span><span>for strengthening the corporate governance of Brazilian SOE’s are similar </span><span>the U.S. Model Benefit Corporation Legislation (MBCL) concerning benefit </span><span>corporations.</span></p>

scholarly journals Sentiment of App with Word Vectors

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2156-2159
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Data ◽  
Vector Representations ◽  
Text Sentiment Analysis

Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features can achieve better results for text sentiment analysis of APP.

scholarly journals Text Data Augmentation for Deep Learning

2021 ◽  
Author(s):  
Connor Shorten ◽  
Taghi M. Khoshgoftaar ◽  
Borko Furht
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Data Augmentation ◽  
Early Stage ◽  
Practical Implementation ◽  
Text Data ◽  
Training Strategy ◽  
Local Decision ◽  
Decision Boundaries

Abstract Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data. Deep Learning generally struggles with the measurement of generalization and characterization of overfitting. We highlight studies that cover how augmentations can construct test sets for generalization. NLP is at an early stage in applying Data Augmentation compared to Computer Vision. We highlight the key differences and promising ideas that have yet to be tested in NLP. For the sake of practical implementation, we describe tools that facilitate Data Augmentation such as the use of consistency regularization, controllers, and offline and online augmentation pipelines, to preview a few. Finally, we discuss interesting topics around Data Augmentation in NLP such as task-specific augmentations, the use of prior knowledge in self-supervised learning versus Data Augmentation, intersections with transfer and multi-task learning, and ideas for AI-GAs (AI-Generating Algorithms). We hope this paper inspires further research interest in Text Data Augmentation.

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

Toward a Better Text Data Augmentation via Filtering and Transforming Augmented Instances

2021 ◽  
pp. 198-210
Author(s):  
Fei Xia ◽  
Shizhu He ◽  
Kang Liu ◽  
Shengping Liu ◽  
Jun Zhao
Keyword(s):  
Data Augmentation ◽  
Text Data
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