Brand User Attention Model Based on Online Text Reviews: An Empirical Study of New Energy Automobile Brands

Accurately grasping the rules of user behavior and market changes and timely adjusting decisions and strategies are the ways for brand development and innovation. In this paper, we proposed a brand user attention model based on online text review analysis. First of all, we collected and preprocessed the user comment text from the online forum. Secondly, through the LDA topic model and LDAvis visual analysis, the potential topics of user reviews were extracted, and a multi-dimensional feature analysis model was constructed to reveal the users\u27 attention features of brand products. Finally, took the new energy automobile brands as an example, the users\u27 attention features for the different new energy automobile brands were explored and the empirical study was carried out. This study found that the brand user attention model based on online text analysis can effectively extract the characteristics that brand users care about, obtain valuable business insight, and provide support for managers\u27 decision-making

Recherche d'Information Sociale et Recommandation: Etat d'art et travaux futurs

International audienceThe explosion of web 2.0 and social networks has created an enormous and rewarding source of information that has motivated researchers in different fields to exploit it. Our work revolves around the issue of access and identification of social information and their use in building a user profile enriched with a social dimension, and operating in a process of personalization and recommendation. We study several approaches of Social IR (Information Retrieval), distinguished by the type of incorporated social information. We also study various social recommendation approaches classified by the type of recommendation. We then present a study of techniques for modeling the social user profile dimension, followed by a critical discussion. Thus, we propose our social recommendation approach integrating an advanced social user profile model.L’explosion du web 2.0 et des réseaux sociaux a crée une source d’information énorme et enrichissante qui a motivé les chercheurs dans différents domaines à l’exploiter. Notre travail s’articule autour de la problématique d’accès et d’identification des informations sociales et leur exploitation dans la construction d’un profil utilisateur enrichi d’une dimension sociale, et son exploitation dans un processus de personnalisation et de recommandation. Nous étudions différentes approches sociales de RI (Recherche d’Information), distinguées par le type d’informations sociales incorporées. Nous étudions également diverses approches de recommandation sociale classées par le type de recommandation. Nous exposons ensuite une étude des techniques de modélisation de la dimension sociale du profil utilisateur, suivie par une discussion critique. Ainsi, nous présentons notre approche de recommandation sociale proposée intégrant un modèle avancé de profil utilisateur social

The influence of social media

This paper is about the various influences that social media has. It ranges from the way social media his impacted governments to the laws being made on how people interact with each other

Mining Twitter for crisis management: realtime floods detection in the Arabian Peninsula

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of doctor of Philosophy.In recent years, large amounts of data have been made available on microblog platforms such as Twitter, however, it is difficult to filter and extract information and knowledge from such data because of the high volume, including noisy data. On Twitter, the general public are able to report real-world events such as floods in real time, and act as social sensors. Consequently, it is beneficial to have a method that can detect flood events automatically in real time to help governmental authorities, such as crisis management authorities, to detect the event and make decisions during the early stages of the event. This thesis proposes a real time flood detection system by mining Arabic Tweets using machine learning and data mining techniques. The proposed system comprises five main components: data collection, pre-processing, flooding event extract, location inferring, location named entity link, and flooding event visualisation. An effective method of flood detection from Arabic tweets is presented and evaluated by using supervised learning techniques. Furthermore, this work presents a location named entity inferring method based on the Learning to Search method, the results show that the proposed method outperformed the existing systems with significantly higher accuracy in tasks of inferring flood locations from tweets which are written in colloquial Arabic. For the location named entity link, a method has been designed by utilising Google API services as a knowledge base to extract accurate geocode coordinates that are associated with location named entities mentioned in tweets. The results show that the proposed location link method locate 56.8% of tweets with a distance range of 0 – 10 km from the actual location. Further analysis has shown that the accuracy in locating tweets in an actual city and region are 78.9% and 84.2% respectively

Hot Topic Discovery in Online Community using Topic Labels and Hot Features

With huge volumes of information on Internet, how to extract user-concerned hot topics quickly and effectively has become a fundamental task for information processing on Internet. Generally, hot topic detection includes two tasks, the first one is topic discovery and the other is its hotness evaluation. In this paper, we propose a hot topic detection method. For topic discovery, topics are identified by clustering based on extracted topic labels. For hotness evaluation, the proposed model has fully considered the internal and external dual features and combined them together. The experimental results over TianYa BBS demonstrate the efficiency of the proposed method: compared with topic discovery based on latent semantic indexing, the improved vector space model based on topic labels gets better results and the identified topics are more accurate. Moreover, the proposed hotness features could reflect the popularity of a topic, and hence have obtained better hot topic results finally

Online customer satisfaction about sharing bike market in China

Sharing bike, which makes the replacement of private car travel possible, solved the “last mile” problem and changed people’s travel idea from individual ownership to sharing service. It is an environmentally friendly way of travel, which is quite popular in China. Users' satisfaction with sharing bicycles is essential in determining whether users continue to use these bicycles. The Internet is an important place for users to evaluate sharing bicycles. It is significant for developing public transportation in China to study sharing bike satisfaction by collecting online public opinion data. This paper takes the satisfaction of sharing bicycles in China as the research object, collects the public opinion information about sharing bicycles on the Internet, and obtains the satisfaction data of sharing bicycles by using text mining. In addition, the descriptive statistical analysis and comparative study were carried out on four major brands in China (Didi, Meituan, Hello, and Ofo). It is found that Didi's satisfaction with sharing bicycles is the highest, and Ofo is the lowest. The price increase of sharing bicycles does not affect its satisfaction in most cases, and only a few cases will reduce the satisfaction. This paper also studies the correlation between weather and sharing bicycles satisfaction using descriptive statistics, correlation analysis and factor analysis. Chengdu and Beijing were the two cities that selected to analyze the correlation between the weather conditions and sharing bikes satisfaction. In the research on the satisfaction of sharing bicycles in Beijing, it is found that there is a specific correlation between weather conditions, air temperature, and air pressure and the satisfaction. The satisfaction on sunny days is higher than that on cloudy days. In the research on the satisfaction of shared bicycles in Chengdu, it is found that the weather conditions do not affect user satisfaction of sharing bikes, and the public opinions, maximum temperature, and wind speed are related to the satisfaction.Bicicletas compartilhadas, que representam uma substituição da viagem em carro privado, resolveram o problema da "última milha" e mudou meios de transporte de propriedade individual para serviço de compartilhamento. É uma forma de viajar amigável ao ambiente, bastante popular na China. A satisfação dos usuários com bicicletas compartilhadas é essencial para determinar se os usuários continuam a usar essas bicicletas. A Internet trata-se de um lugar onde os usuários avaliam bicicletas compartilhadas. É significativo para o desenvolvimento do transporte público na China estudar a satisfação com bicicletas compartilhadas por meio da coleta de dados online da opinião pública. Esta dissertação tem como objeto de investigação a satisfação de bicicletas compartilhadas na China, coletando informações da opinião pública sobre bicicletas compartilhadas na Internet e usando mineração de texto. Além disso, a análise estatística descritiva e o estudo comparativo foram realizados em quatro grandes marcas na China (“Didi”, “Meituan”, “Hello” e “Ofo”). Verificou-se que a satisfação para a Didi é a mais alta e para Ofo é a mais baixa. O aumento do preço de bicicletas compartilhadas não afeta o nível de satisfação na sua maioria, e apenas em alguns casos reduziram a satisfação. A dissertação também estuda a correlação entre o clima e a satisfação com bicicletas compartilhadas usando estatística descritiva, análise de correlação e análise fatorial. As cidades de Chengdu e de Pequim foram escolhidas para analisar a correlação. Na pesquisa sobre a satisfação de bicicletas compartilhadas em Pequim, verificou-se que existe uma correlação específica entre as condições meteorológicas, a temperatura do ar e a pressão do ar e a satisfação. A satisfação em dias de sol é maior do que em dias nublados. Na pesquisa em Chengdu, foi descobrido que as condições climáticas não afetam a satisfação do usuário em compartilhar bicicletas, e a opinião pública, temperatura máxima e velocidade do vento relacionam-se à satisfação

Sistema de recomendação dos amigos na rede social online baseado em máquinas de vetores suporte

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2014.O rápido desenvolvimento da tecnologia da Internet trouxe-nos para a era da explosão de informações, enquanto a massa de informações por um lado, torna difícil selecionar as mais interessantes. Por outro lado, também muitas delas são perdidas na rede de informação, pois existem "informações secretas", não permitindo o acesso aos usuários em geral. O Sistema de Recomendação(RS) é atualmente um esquema mais eficiente para resolver o problema recente de sobrecarga de informações. A recomendação é amplamente utilizada em Redes Sociais Online(como Twitter, Weibo e outros Microblogs), neste trabalho é utilizado o método de Máquina de Vetores de Suporte(SVM) para aplicar recomendação de amigos. A dissertação propõe uma idéia que combina a teoria e os atributos de Microblog SVM para realizar a recomendação de amigos. Além disso implementá-lo como um sistema recomendado para aumentar a aceitação do usuário no microblog. Os experimentos mostraram que o modelo SVM proposto apresenta um desempenho eficiente e boa exatidão na recomendação de amigos nas redes sociais. O resultado do SVM é 72\% melhor que os métodos usados para comparação: os algoritmos Naïve Bayes e Random Forest, tendo sido considerados diferentes tamanhos de amostras para testar a eficiência e o desempenho destes modelos. O resultado mostrou que o algoritmo SVM é melhor para amostras de diversos tamanhos. _________________________________________________________________________________ ABSTRACTThe rapid growth of internet technology brought us to the era of the rapid difusionof information. Nevertheless, the large quantity of information makes it dificult to find interesting information and therefore, much of it is lost in the information networkdue to secret information, not permitting the access to the general public. Recommendationsystems (RS) are nowadays the most eficient tools to solve the recent problemof information overload. RS is already widely used in Online Social Networks(such asTwitter,Weibo and other Microblogs). In this research, Support Vector Machines (SVM)method is applied in the recommendation of friends.The dissertation proposes an idea which combining the SVM theory and attributesof Microblog to realize the recommendation of friends. Furthemore implement it as arecommended system to increase the acceptance of user no microblog.The experiments showed that the proposed SVM model presents an eficient performanceand good accuracy on the recommendation of friends in social networks. Theresult of the SVM is 72% better than the methods used for comparison: the algorithmsRandom Forest and Naive Bayes. Diferent sample sizes were considered separately totest the eficiency and performance of these models. These results showed that the SVMalgorithm is better for samples of diferent sizes

Tagging amongst friends: an exploration of social media exchange on mobile devices

Mobile social software tools have great potential in transforming the way users communicate on the move, by augmenting their everyday environment with pertinent information from their online social networks. A fundamental aspect to the success of these tools is in developing an understanding of their emergent real-world use and also the aspirations of users; this thesis focuses on investigating one facet of this: the exchange of social media. To facilitate this investigation, three mobile social tools have been developed for use on locationaware smartphone handsets. The first is an exploratory social game, 'Gophers' that utilises task oriented gameplay, social agents and GSM cell positioning to create an engaging ecosystem in which users create and exchange geotagged social media. Supplementing this is a pair of social awareness and tagging services that integrate with a user's existing online social network; the 'ItchyFeet' service uses GPS positioning to allow the user and their social network peers to collaboratively build a landscape of socially important geotagged locations, which are used as indicators of a user's context on their Facebook profile; likewise 'MobiClouds' revisits this concept by exploring the novel concept of Bluetooth 'people tagging' to facilitate the creation of tags that are more indicative of users' social surroundings. The thesis reports on findings from formal trials of these technologies, using groups of volunteer social network users based around the city of Lincoln, UK, where the incorporation of daily diaries, interviews and automated logging precisely monitored application use. Through analysis of trial data, a guide for designers of future mobile social tools has been devised and the factors that typically influence users when creating tags are identified. The thesis makes a number of further contributions to the area. Firstly, it identifies the natural desire of users to update their status whilst mobile; a practice recently popularised by commercial 'check in' services. It also explores the overarching narratives that developed over time, which formed an integral part of the tagging process and augmented social media with a higher level meaning. Finally, it reveals how social media is affected by the tag positioning method selected and also by personal circumstances, such as the proximity of social peers

Employing Topological Data Analysis On Social Networks Data To Improve Information Diffusion

For the past decade, the number of users on social networks has grown tremendously from thousands in 2004 to billions by the end of 2015. On social networks, users create and propagate billions of pieces of information every day. The data can be in many forms (such as text, images, or videos). Due to the massive usage of social networks and availability of data, the field of social network analysis and mining has attracted many researchers from academia and industry to analyze social network data and explore various research opportunities (including information diffusion and influence measurement). Information diffusion is defined as the way that information is spread on social networks; this can occur due to social influence. Influence is the ability affect others without direct commands. Influence on social networks can be observed through social interactions between users (such as retweet on Twitter, like on Instagram, or favorite on Flickr). In order to improve information diffusion, we measure the influence of users on social networks to predict influential users. The ability to predict the popularity of posts can improve information diffusion as well; posts become popular when they diffuse on social networks. However, measuring influence and predicting posts popularity can be challenging due to unstructured, big, noisy data. Therefore, social network mining and analysis techniques are essential for extracting meaningful information about influential users and popular posts. For measuring the influence of users, we proposed a novel influence measurement that integrates both users’ structural locations and characteristics on social networks, which then can be used to predict influential users on social networks. centrality analysis techniques are adapted to identify the users’ structural locations. Centrality is used to identify the most important nodes within a graph; social networks can be represented as graphs (where nodes represent users and edges represent interactions between users), and centrality analysis can be adopted. The second part of the work focuses on predicting the popularity of images on social networks over time. The effect of social context, image content and early popularity on image popularity using machine learning algorithms are analyzed. A new approach for image content is developed to represent the semantics of an image using its captions, called keyword vector. This approach is based on Word2vec (an unsupervised two-layer neural network that generates distributed numerical vectors to represent words in the vector space to detect similarity) and k-means (a popular clustering algorithm). However, machine learning algorithms do not address issues arising from the nature of social network data, noise and high dimensionality in data. Therefore, topological data analysis is adopted. It is a noble approach to extract meaningful information from high-dimensional data and is robust to noise. It is based on topology, which aims to study the geometric shape of data. In this thesis, we explore the feasibility of topological data analysis for mining social network data by addressing the problem of image popularity. The proposed techniques are employed to datasets crawled from real-world social networks to examine the performance of each approach. The results for predicting the influential users outperforms existing measurements in terms of correlation. As for predicting the popularity of images on social networks, the results indicate that the proposed features provides a promising opportunity and exceeds the related work in terms of accuracy. Further exploration of these research topics can be used for a variety of real-world applications (including improving viral marketing, public awareness, political standings and charity work)