• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.2
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• pp.244-255
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• 2023

Patterns abstractly depicting flowing water with Chinese characters such as gong, wan(man), or wang continued endlessly and curved water patterns began appearing on textiles during the Song Dynasty. Though Song curved water patterns encompassed poetic sentiments such as "falling flowers and flowing water," the meaning faded with time, and these patterns were depicted in backgrounds with flowers added to brocade (Geum-sang-cheom-hwa). During the Ming and Qing Dynasties, combinations of diverse patterns, including flowers, butterflies, dragons, and auspicious treasures became fashionable, rather than the gong- and wan-shaped curved water patterns. Likewise, during the Joseon Dynasty, curved water patterns were preferred as background rather than as primary patterns. They were overlaid with flowers and clouds. The overlaid flower patterns included four-season flower patterns (17th-18th centuries), round flower patterns (19th century), and large flower patterns (20th century), which were identical to flower patterns fashionable at the time and arranged at intervals on complex curved water pattern backgrounds. In contrast, simple Ruyi types were more numerous than the four-Ruyi types fashionable at the time with regard to cloud patterns. Added here were Taiji (great ultimate symbol) or crane patterns, thus seeking to depict diverse auspicious Ruyi such as wish fulfillment and longevity.

• Analyses & Alternatives
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• v.7 no.1
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• pp.201-227
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• 2023

The purpose of this study is to examine how France became a representative country for far-right European populism, despite its tradition of tolerance. To achieve this goal, we examine, first, how the concept of tolerance developed in France after the 16th century. Through this process, we find that within the political system, the tolerance of the liberal tradition met with universalism, a republican value, and developed into an 'institutional tolerance' that allowed 'differences' from an authoritarian perspective rather than on an equal level. This 'assimilation' policy, reflecting a 'patriarchal' and 'oppressive' institutional tolerance, formed the keynote of the immigration policy of the 20th century, which continued until the 1980s, and shows that the French government did not take practical steps for the social integration of immigrant groups under the republican universal value that does not allow 'differences.' The government came up with an 'integration' immigration policy that embraces cultural 'differences' only after encountering problems with immigrant groups. However, this was not enough to calm the antipathy towards immigrants in French society and the discontent of immigrants in French society. Also, universalism, a republican value with deep roots in France, prevented the French immigration policy from escaping its assimilationist nature even in the 21st century. In the midst of this, far-right parties have gained power by promoting xenophobic sentiments centered on immigration problems. Finally, this study also looks at how far-right populism is currently changing the French political environment.

• Journal of Distribution Science
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• v.21 no.5
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• pp.53-61
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• 2023

Purpose: The purpose of this study is to consolidate research trends about the distribution of 'other customer perspective' on 'brand love' and 'brand anthropomorphism', as well as to identify prospective research topics and provide managers with suggestions. Design, data, and technique of research: The purpose of this article is to examine the distribution relationship between brand love and brand anthropomorphism using a systematic review and bibliographic mapping analysis (VOS viewer) using 23 documents from 2014 to 2023. Results: This will be a step in the correct path if brand managers can have a great interaction with their clients by using common anthropomorphism. Yet, a second challenge will be how to anthropomorphize the brand. Moreover, there is nothing simpler than discovering oneself in a brand when there are several pictures, life ethics, sentiments, and experiences that coincide. From a different perspective, the brand sometimes looks to be the ideal model for consumers to identify with, and even fall in love with since it makes them feel close to their significant other. Conclusion: The findings may help companies create a long-term brand strategy and anticipate additional consumer rewards and value. They may also enhance brand-customer theory.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.112-117
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• 2023

This study aims to find out what tourists' interests and perceptions are like through online big data. Big data for a total of five years from 2018 to 2022 were collected using the Textom program. Sentiment analysis was performed with the collected data. Sentiment analysis expresses the necessity and emotions of city tours in online reviews written by tourists using city tours. The purpose of this study is to extract and analyze keywords representing satisfaction. The sentiment analysis program provided by the big data analysis platform "TEXTOM" was used to study positives and negatives based on sentiment analysis of tourists' online reviews. Sentiment analysis was conducted by collecting reviews related to the city tour. The degree of positive and negative emotions for the city tour was investigated and what emotional words were analyzed for each item. As a result of big data sentiment analysis to examine the emotions and sentiments of tourists about the city tour, 93.8% positive and 6.2% negative, indicating that more than half of the tourists are positively aware. This paper collects tourists' opinions based on the analyzed sentiment analysis, understands the quality characteristics of city tours based on the analysis using the collected data, and sentiment analysis provides important information to the city tour platform for each region.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• Smart Media Journal
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• v.12 no.10
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• pp.38-46
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• 2023

Farming activities are transforming from traditional skill-based agriculture into knowledge-based and technology-driven digital agriculture. The use of intelligent information and communication technology introduces the idea of smart farming that enables farmers to collect weather data, monitor crop growth remotely and detect crop diseases easily. The introduction of Plantix, a pest and disease management tool in the form of a mobile application has allowed farmers to identify pests and diseases of the crop using their mobile devices. Hence, this study collected the reviews of Plantix to explore the response of the users on the Google Play Store towards the application through Latent Dirichlet Allocation (LDA) topic modeling. Results indicate four latent topics in the reviews: two positive evaluations (compliments, appreciation) and two suggestions (plant options, recommendations). We found the users suggested the application to additional plant options and additional features that might help the farmers with their difficulties. In addition, the application is expected to benefit the farmer more by having an early alert of diseases to farmers and providing various substitutes and a list of components for the remedial measures.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.5
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• pp.873-890
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• 2023

This study analyzed users' emotional responses to VI character design through YouTube comments. The researchers applied text-mining to analyze 116,375 comments, focusing on terms related to character design and characteristics of VI. Using the BERT model in sentiment analysis, we classified comments into extremely negative, negative, neutral, positive, or extremely positive sentiments. Next, we conducted a co-occurrence frequency analysis on comments with extremely negative and extremely positive responses to examine the semantic relationships between character design and emotional characteristic terms. We also performed a content analysis of comments about Miquela and Shudu to analyze the perception differences regarding the two character designs. The results indicate that form elements (e.g., voice, face, and skin) and behavioral elements (e.g., speaking, interviewing, and reacting) are vital in eliciting users' emotional responses. Notably, in the negative responses, users focused on the humanization aspect of voice and the authenticity aspect of behavior in speaking, interviewing, and reacting. Furthermore, we found differences in the character design elements and characteristics that users expect based on the VI's field of activity. As a result, this study suggests applications to character design to accommodate these variations.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.842-857
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• 2023

Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• Journal of Information Science Theory and Practice
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• v.12 no.2
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• pp.64-78
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• 2024

Sentiment analysis is one of the promising approaches for developing a point of interest (POI) recommendation system. It uses natural language processing techniques that deploy expert insights from user-generated content such as reviews and feedback. By applying sentiment polarities (positive, negative, or neutral) associated with each POI, the recommendation system can suggest the most suitable POIs for specific users. The proposed study combines two models for POI recommendation. The first model uses bidirectional long short-term memory (BiLSTM) to predict sentiments and is trained on an election dataset. It is observed that the proposed model outperforms existing models in terms of accuracy (99.52%), precision (99.53%), recall (99.51%), and F1-score (99.52%). Then, this model is used on the Foursquare dataset to predict the class labels. Following this, user and POI embeddings are generated. The next model recommends the top POIs and corresponding coordinates to the user using the LSTM model. Filtered user interest and locations are used to recommend POIs from the Foursquare dataset. The results of our proposed model for the POI recommendation system using sentiment analysis are compared to several state-of-the-art approaches and are found quite affirmative regarding recall (48.5%) and precision (85%). The proposed system can be used for trip advice, group recommendations, and interesting place recommendations to specific users.