scholarly journals Social Media Posts Popularity Prediction During Long-Running Live Events A case study on Fashion Week

2021 ◽  
Author(s):  
Alireza Javadian Sabet
Keyword(s):  
Social Media ◽  
Influential Factors ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Regression Methods ◽  
Social Events ◽  
Content Popularity ◽  
Popularity Prediction ◽  
Support Vector Regressor

In the last few years, social media has dominated various aspects of people’s life including social events. Users participate more and more in long-running periodical events in social media, by sharing their experiences and preferences. This information provides unprecedented opportunities allowing businesses to promote their brands coverage by using word-of-mouth (WOM), that is enabled by the user generated contents (UGCs). Studying social media content popularity by considering the societies’ behavioral patterns is, therefore, paramount. In this thesis, we inspect users’ engagement motives in long-running events by means of a comprehensive statistical analysis of fashion week events on Instagram. Additionally, we develop a multi-modal approach to solve the problem of post popularity prediction that exploits potentially influential factors and apply it on fashion week events. We employ two metrics for implementing a filter feature selection technique, together with an automated grid search for optimizing hyper-parameters in four regression methods: ridge, support vector regressor, gradient tree boosting and neural networks.

scholarly journals Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events

2018 ◽  
Vol 8 (1) ◽  
pp. 15 ◽  
Author(s):  
Ruoxin Zhu ◽  
Diao Lin ◽  
Michael Jendryke ◽  
Chenyu Zuo ◽  
Linfang Ding ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
City Management ◽  
Social Media Data ◽  
Social Events ◽  
Time Space ◽  
The Impact ◽  
Media Data

Studying the impact of social events is important for the sustainable development of society. Given the growing popularity of social media applications, social sensing networks with users acting as smart social sensors provide a unique channel for understanding social events. Current research on social events through geo-tagged social media is mainly focused on the extraction of information about when, where, and what happened, i.e., event detection. There is a trend towards the machine learning of more complex events from even larger input data. This research work will undoubtedly lead to a better understanding of big geo-data. In this study, however, we start from known or detected events, raising further questions on how they happened, how they affect people’s lives, and for how long. By combining machine learning, natural language processing, and visualization methods in a generic analytical framework, we attempt to interpret the impact of known social events from the dimensions of time, space, and semantics based on geo-tagged social media data. The whole analysis process consists of four parts: (1) preprocessing; (2) extraction of event-related information; (3) analysis of event impact; and (4) visualization. We conducted a case study on the “2014 Shanghai Stampede” event on the basis of Chinese Sina Weibo data. The results are visualized in various ways, thus ensuring the feasibility and effectiveness of our proposed framework. Both the methods and the case study can serve as decision references for situational awareness and city management.

scholarly journals Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

2019 ◽  
Vol 8 (9S4) ◽  
pp. 39-47
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Cuckoo Search ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Selection Technique ◽  
Performance Factors

Sentiment analysis is an area of natural language processing (NLP) and machine learning where the text is to be categorized into predefined classes i.e. positive and negative. As the field of internet and social media, both are increasing day by day, the product of these two nowadays is having many more feedbacks from the customer than before. Text generated through social media, blogs, post, review on any product, etc. has become the bested suited cases for consumer sentiment, providing a best-suited idea for that particular product. Features are an important source for the classification task as more the features are optimized, the more accurate are results. Therefore, this research paper proposes a hybrid feature selection which is a combination of Particle swarm optimization (PSO) and cuckoo search. Due to the subjective nature of social media reviews, hybrid feature selection technique outperforms the traditional technique. The performance factors like f-measure, recall, precision, and accuracy tested on twitter dataset using Support Vector Machine (SVM) classifier and compared with convolution neural network. Experimental results of this paper on the basis of different parameters show that the proposed work outperforms the existing work

Daily and hourly mood pattern discovery of Turkish twitter users

2015 ◽  
Vol 5 (2) ◽  
pp. 90
Author(s):  
Mete Celik ◽  
Ahmet Sakir Dokuz
Keyword(s):  
Social Media ◽  
Big Data ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Social Media Data ◽  
Web Technologies ◽  
Document Frequency ◽  
Social Media Text ◽  
Twitter Users ◽  
Media Data

<p>Massive amount of data-related applications and widespread usage of web technologies has started big data era. Social media data is one of the big data sources. Mining social media data provides useful insights for companies and organizations for developing their services, products or organizations. This study aims to analyze Turkish Twitter users based on daily and hourly social media sharings. By this way, daily and hourly mood patterns of Turkish social media users could be revealed in positive or negative manner. For this purpose, Support Vector Machines (SVM) classification algorithm and Term Frequency – Inverse Document Frequency (TF-IDF) feature selection technique was used. As far as our knowledge, this is the first attempt to analyze people’s all sharings on social media and generate results for temporal-based indicators like macro and micro levels.</p><p> </p><p>Keywords: big data, social media, text classification, svm, tf-idf term weighting, daily and hourly mood patterns.</p>

Pricing Method for Service-Oriented Manufacturing With Support Vector Machine

2017 ◽  
Author(s):  
Qiunan Meng ◽  
Jian Lou ◽  
Xun Xu ◽  
Shiqiang Yu
Keyword(s):  
Support Vector Machine ◽  
Value Chain ◽  
Back Propagation ◽  
Influential Factors ◽  
Support Vector ◽  
Small Scale ◽  
Product Price ◽  
Price Prediction ◽  
Service Oriented

To evaluate the effects of customers’ participation levels in various business activities on pricing in service-oriented manufacturing, the indices of pricing are proposed through extracting the influential factors in the four stages (i.e., design, manufacturing, production and services) from the whole value chain to comprehensively reflect customers’ demands. A new pricing model based on these indices is formulated by Support Vector Machine (SVM). It can predict a more accurate product price regarding the products’ similarity by the values of the influential factors that are determined in terms of business activities participated by customers. Finally, a case study from a molding company in China is conducted to verify the effectiveness of this pricing methodology. The results indicate that the model by SVM fares better in comparison with that by Back Propagation Neural Networks in small scale samples, especially in the performances of generalization and robustness. The outcomes also testify that this price prediction methodology can increase the accuracy of a product’s price as well as the customer’s satisfaction.

scholarly journals Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

2019 ◽  
Vol 11 (23) ◽  
pp. 2731 ◽  
Author(s):  
Mirzaei ◽  
Verrelst ◽  
Marofi ◽  
Abbasi ◽  
Azadi
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Heavy Metal Stress ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Regression Methods ◽  
Field Spectroscopy ◽  
Metal Contents ◽  
Metal Monitoring

Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants’ physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350–2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R2 rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage.

scholarly journals Social media for informal science learning in China: A case study

2014 ◽  
pp. 262-280
Keyword(s):  
Social Media ◽  
Learning Communities ◽  
Learning Community ◽  
Social Networking Sites ◽  
Science Learning ◽  
Future Research ◽  
Informal Science ◽  
Informal Science Learning ◽  
Social Events

This article reports a case study on a popular informal science learning community via social media in China, named GuoKr (meaning “nutshell” in English). Data were collected through a variety of Chinese social media and social networking sites, web-based community portals, and discussion boards. Content analyses and data mining were conducted to investigate how GuoKr successfully attracted and engaged public in informal learning on scientific topics in particular. The study found three key characteristics that contributed to the success of such learning communities: (a) utilizing a variety of social media to empower participants with just-in-time, accidental learning opportunities; (b) daily tweets related to emerging or ongoing social events or hot topics to provide brief but intriguing knowledge “bites”, which often leads to extended readings and related resources; and (c) the integration of social media and traditional face-to-face local events to engage the public in science-related learning and knowledge sharing. Practical and research implications are discussed with suggestions for future research as related to ubiquitous learning communities for informal science learning.

Demand Forecasting Model of Port Critical Spare Parts Based on PSO-LSSVM

2013 ◽  
Vol 433-435 ◽  
pp. 545-549
Author(s):  
Zhi Jie Song ◽  
Zan Fu ◽  
Han Wang ◽  
Gui Bin Hou
Keyword(s):  
Historical Data ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Influential Factors ◽  
Support Vector ◽  
Proposed Model ◽  
Svm Model ◽  
Vector Machines ◽  
Demand Forecasting Model

Demand forecasting for port critical spare parts (CSP) is notoriously difficult as it is expensive, lumpy and intermittent with high variability. In this paper, some influential factors which have an effect on CSP consumption were proposed according to port CSP characteristics and historical data. Combined with the influential factors, a least squares support vector machines (LS-SVM) model optimized by particle swarm optimization (PSO) was developed to forecast the demand. And the effectiveness of the model is demonstrated through a real case study, which shows that the proposed model can forecast the demand of port CSP more accurately, and effectively reduce inventory backlog.

scholarly journals FORECASTING HAZARDOUS WASTE GENERATION USING SHORT DATA SETS: CASE STUDY OF LITHUANIA / PAVOJINGŲJŲ ATLIEKŲ SUSIDARYMO PROGNOZAVIMAS NAUDOJANT TRUMPAS DUOMENŲ IMTIS: LIETUVOS ATVEJIS

2016 ◽  
Vol 8 (4) ◽  
pp. 357-364 ◽  
Author(s):  
Aistė Karpušenkaitė ◽  
Gintaras Denafas ◽  
Tomas Ruzgas
Keyword(s):  
Hazardous Waste ◽  
Treatment Plant ◽  
Support Vector ◽  
Data Sets ◽  
Waste Generation ◽  
Regression Methods ◽  
Vector Machines ◽  
Waste Flow ◽  
Modelling Methods

Due to inefficient waste sorting in primary and secondary waste generation sources Lithuania fails in trying to meet EU requirements for waste management sector regarding the amount of waste flow that reaches landfills. Especially sensitive situation is with hazardous waste, which often are disposed along with municipal solid waste and with it reaches landfills and due to the fact that mechanical and biological treatment plant are only now being established in the biggest cities of Lithuania, landfills becomes a big issue. The main purpose of this research is to find out which mathematical modelling methods could be fitted and if it is possible to forecast annual hazardous waste generation by using automotive, medical and daylight lamps waste generation statistical data. This is part of a research of medical, automotive and daylight lamps waste generation forecasting possibilities. Tests on the performance of artificial neural networks, multiple linear regression, partial least squares, support vector machines and four nonparametric regression methods were conducted on two developed data sets. The best and most promising results in both cases were demonstrated by generalized additives method (R2 = 0.99) and kernel regression (R2 = 0.99). Dėl nepakankamai efektyvaus pirminio ir antrinio atliekų rūšiavimo jų susidarymo šaltiniuose, Lietuva neatitinka ES atliekų tvarkymui keliamų reikalavimų, kurie apibrėžia į sąvartynus patenkančių atliekų srauto procentinę dalį. Pavojingosios atliekos yra ypač daug dėmesio reikalaujantis atliekų tvarkymo sektoriaus aspektas, nes didelė dalis pavojingųjų atliekų kartu su komunalinių atliekų srautu patenka į sąvartynus. Mechaninio ir biologinio apdorojimo įrenginiai, kurie padėtų spręsti šią ir sąvartynų pavojingumo aplinkai problemą, daugelyje didžiųjų šalies miestų tik dabar baigiami įrengti ar pradėti statyti. Pagrindinis šio tyrimo tikslas yra išsiaiškinti, kurie matematinio modeliavimo metodai galėtų būti pritaikyti prognozuojant metinį susidarančių pavojingųjų atliekų kiekį remiantis medicininių, automobilinių ir dienos šviesos lempų atliekų susidarymo duomenimis. Tai yra medicininių, automobilinių ir dienos šviesos lempų atliekų susidarymo prognozavimo galimybių tyrimo dalis. Atliekant tyrimą su dviem duomenų imtimis buvo išbandyti dirbtinių neuronų tinklų, daugialypės tiesinės regresijos, dalinių mažiausių kvadratų, atraminių vektorių, neparametrinės regresijos ir laiko eilučių metodai. Abiejų duomenų imčių atvejais geriausi rezultatai buvo pasiekti taikant bendrųjų adityvų (R2 = 0,99) ir branduolinės regresijos (R2 = 0,99) metodus.

STRATEGI KOMUNIKASI CROWDFUNDING PADA KOLASE.COM

2019 ◽  
Vol 2 (2) ◽  
pp. 177-187
Author(s):  
Venessa Agusta Gogali ◽  
Fajar Muharam ◽  
Syarif Fitri
Keyword(s):  
Social Media ◽  
Communication Strategies ◽  
Communication Strategy ◽  
New Method ◽  
Media Communication ◽  
Social Activists ◽  
Social Media Communication

Crowdfunding is a new method in fundraising activities based online. Moreover, the level of penetration of social media to the community is increasingly high. This makes social activists and academics realize that it is important to study social media communication strategies in crowdfunding activities. There is encouragement to provide an overview of crowdfunding activities. So the author conducted a research on "Crowdfunding Communication Strategy Through Kolase.com Through Case Study on the #BikinNyata Program Through the Kolase.com Website that successfully achieved the target. Keywords: Strategic of Communication, Crowdfunding, Social Media.

Marketing communication objectives through digital content marketing on social media

2020 ◽  
pp. 79-104
Author(s):  
Janice J. Nieves-Casasnovas ◽  
Frank Lozada-Contreras
Keyword(s):  
Social Media ◽  
Marketing Strategies ◽  
Brand Awareness ◽  
Digital Content ◽  
Media Content ◽  
Marketing Communication ◽  
Case Study Design ◽  
Multiple Case ◽  
Content Marketing

The purpose of this study was to determine what type of marketing communication objectives are present in the digital content marketing developed by luxury auto brands with social media presence in Puerto Rico, particularly Facebook. A longitudinal multiple-case study design was used to analyze five luxury auto brands using content analysis on Facebook posts. This analysis included identification of marketing communication objectives through social media content marketing strategies, type of media content and social media metrics. Our results showed that the most used objectives are brand awareness, brand personality, and brand salience. Another significant result is that digital content marketing used by brands in social media are focused towards becoming more visible and recognized; also, reflecting human-like traits and attitudes in their social media.

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