scholarly journals Capturing Associations and Sustainable Competitiveness of Brands from Social Tags

2019 ◽  
Vol 11 (6) ◽  
pp. 1529 ◽  
Author(s):  
Xuan Gong ◽  
Yunchan Zhu ◽  
Rizwan Ali ◽  
Ruijin Guo
Keyword(s):  
Social Media ◽  
Real Time ◽  
Survey Data ◽  
Automobile Industry ◽  
Traditional Method ◽  
Predictive Power ◽  
Determination Method ◽  
Brand Associations ◽  
Social Tags ◽  
Brand Competitiveness

With the explosion of social media, consumers’ minds have become important assets in brand competitions. Determining a brand’s competitive structure based on consumers’ desires is particularly important to effectively establish a brand and maintain sustainable competitiveness. The traditional methods of determining brand competitiveness are costly and time-consuming. In this study, we propose an efficient, systematical, highly automated, and real-time method to determine brand competitiveness based on consumers’ brand associations with the brand’s social tags. Using a set of 45 brands in the automobile industry and around 50,000 social tags, we compared our brand competitiveness determination method with data provided by Interbrand and directly elicited survey data, finding a significant correlation and a better predictive power in consumers’ perceived brand competitiveness than the traditional method. Our proposed method enables managers to create and maintain sustainable brand advantages in consumers’ minds.

scholarly journals A new method of multi-beam real-time attitude compensation data processing

2020 ◽  
Vol 206 ◽  
pp. 01014
Author(s):  
Zhang Yonghou ◽  
Xiao Fumin ◽  
Jin Shaohua ◽  
Bian Gang
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Traditional Method ◽  
Correction Method ◽  
New Method ◽  
Determination Method ◽  
Error Correction Method ◽  
Compensation Error ◽  
Specific Determination ◽  
Processing Accuracy

The traditional method for processing multi-beam real-time attitude compensation data did not consider the influence of attitude compensation error, which left attitude residual in data. So a new method for processing multi-beam data of real-time attitude compensation was proposed. By studying the nature of attitude compensation error, the calculation method of systematic attitude compensation error was put forward. The concept of correction threshold was introduced and the specific determination method was given. Finally, combining with the principle of real-time attitude compensation, a systematic attitude compensation error correction method, which makes use of a tracking algorithm considering attitude to make secondary attitude correction, was proposed, and the specific processing flow was given. By comparing the data processing accuracy of the tradition method and the new method, the results showed that the new method can effectively reduce the influence of systematic attitude compensation error, and significantly improve the data processing accuracy.

Techniques of Viral Marketing

2019 ◽  
Vol 118 (6) ◽  
pp. 97-99
Author(s):  
Arockia Jeyasheela A ◽  
Dr.S. Chandramohan
Keyword(s):  
Social Media ◽  
Real Time ◽  
Word Of Mouth ◽  
Viral Marketing ◽  
Consumer Perception ◽  
Key Word ◽  
The Right ◽  
A Company

This study is discussed about the viral marketing. It is a one of the key success of marketing. This paper gave the techniques of viral marketing. It can be delivered word of mouth. It can be created by both the representatives of a company and consumer (individuals or communities). The right viral message with go to right consumer to the right time. Viral marketing is easy to attract the consumer. It is most important advertising to consumer. It involves consumer perception, organization contribution, blogs, SMO (Social Media Optimize), SEO (Social Engine Optimize). Principles of viral marketing are social profile gathering, Proximity Market, Real time Key word density.

Social Listening to Create Bespoke Customer Experiences: Best Practices for Hospitality Operators

2021 ◽  
pp. 193896552199308
Author(s):  
Kathryn A. LaTour ◽  
Ana Brant
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Real Time ◽  
Brand Image ◽  
Customer Experience ◽  
Current Experience ◽  
Social Listening

Most hospitality operators use social media in their communications as a means to communicate brand image and provide information to customers. Our focus is on a two-way exchange whereby a customer’s social posting is reacted to in real-time by the provider to enhance the customer’s current experience. Using social media in this way is new, and the provider needs to carefully balance privacy and personalization. We describe the process by which the Dorchester Collection Customer Experience (CX) Team approached its social listening program and share lessons to identify best practices for hospitality operators wanting to delight their customers through insights gained from social listening.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals Real-time clinician text feeds from electronic health records

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
James T. H. Teo ◽  
Vlad Dinu ◽  
William Bernal ◽  
Phil Davidson ◽  
Vitaliy Oliynyk ◽  
...  
Keyword(s):  
Social Media ◽  
Electronic Health Records ◽  
Real Time ◽  
Capacity Planning ◽  
Low Cost ◽  
Disease Outbreaks ◽  
Record System ◽  
Health Records ◽  
Keywords And Phrases ◽  
Electronic Health

AbstractAnalyses of search engine and social media feeds have been attempted for infectious disease outbreaks, but have been found to be susceptible to artefactual distortions from health scares or keyword spamming in social media or the public internet. We describe an approach using real-time aggregation of keywords and phrases of freetext from real-time clinician-generated documentation in electronic health records to produce a customisable real-time viral pneumonia signal providing up to 4 days warning for secondary care capacity planning. This low-cost approach is open-source, is locally customisable, is not dependent on any specific electronic health record system and can provide an ensemble of signals if deployed at multiple organisational scales.

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Disaster Response ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Situational Awareness ◽  
High Performing ◽  
Latent Topics ◽  
Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

Advanced Driver Assists and Safety Management System

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Dipti Chavan ◽  
Aniket Kamble ◽  
Aditya Khadsare ◽  
Vaibhav Chougule ◽  
Vaibhav Chougule
Keyword(s):  
Real Time ◽  
Automobile Industry ◽  
Safety Management ◽  
Public Utilities ◽  
Sensor Platform ◽  
Key Points ◽  
Different Types ◽  
Important Field ◽  
And Control ◽  
Web Platform

Electronics and communication is the most important field. In this paper, we can describe how much safety is in the Automobile industry. In this paper, we are using uno-Arduino. The different types of sensors facilities are also provided using key points. The different sensors are provided to check visitor count. In this system, we can monitor and control all the safety precautions their one IoT web platform. This helps in the proper utilization of drivers and helps in avoiding accidents. This paper can be implemented in any two-wheelers, heavily loaded trucks, small SUVs, compact cars. In our paper, the electronics machine/components will be automatically working with using of Arduino program. The proposed wireless sensor platform is an attempt to develop more safety devices that can be used in multiple areas such as homes, schools, and public utilities to reduce accidents. This Advanced Driver Assists system will provide real-time accident detections and monitoring usage information that helps in real-time by using GSM, GPS, and sensors.

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