EXPLORING BRAND POSITIONING AND HOTEL PERSONA TROUGH WOM AND CONTENT BY TEXT ANALYSIS

Hotel management will concentrate on addressing difficult challenges and finding new ways for hotels to succeed in today's changing market world. Online customer reviews can be used to reflect the degree of differentiation between hotel brands and understanding the hotel industry's market structure through text analytics. The purpose of this study is to discover and demonstrate how customer online reviews (WOM and content), in the hospitality industry using text analysis can be used to explore brand positioning of hotel persona. This study gathered 4.215 online reviews one of hotel at Bandung city from year of 2015 – 2019, the methodology used the approach of text classification, quantitative analysis of text. This study found the category of visitors who stay the most are comes from family category and romantic vacation category, while the target visitors expected by the hotel are from business traveler’s category. Most customer state the word "family" when mentioning the hotel. Children, family, pool, zoo are the words most often discussed in customer reviews. This study findings can be used as an insight into what are the things that generate a satisfying experience and strengthen brand positioning of the hotel. In the future, it would be interesting to gathered data from multiple e-commerce application, and combining ontology-learning-based text mining and psychometric techniques to translate online hotel’s reviews into a hotel’s positioning map, capturing the relationship between product of hotel and reviewer effectively. Keywords: online review, brand positioning, text analytics, customer perceived value, hotel person

The applications of social media in sports marketing

n the era of big data, sports consumer's activities in social media become valuable assets to sports marketers. In this paper, the authors review extant literature regarding how to effectively use social media to promote sports as well as how to effectively analyze social media data to support business decisions. Methods: The literature review method. Results: Our findings suggest that sports marketers can use social media to achieve the following goals, such as facilitating marketing communication campaigns, adding values to sports products and services, creating a two-way communication between sports brands and consumers, supporting sports sponsorship program, and forging brand communities. As to how to effectively analyze social media data to support business decisions, extent literature suggests that sports marketers to undertake traffic and engagement analysis on their social media sites as well as to conduct sentiment analysis to probe customer's opinions. These insights can support various aspects of business decisions, such as marketing communication management, consumer's voice probing, and sales predictions. Conclusion: Social media are ubiquitous in the sports marketing and consumption practices. In the era of big data, these ＂footprints＂ can now be effectively analyzed to generate insights to support business decisions. Recommendations to both the sports marketing practices and research are also addressed

Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry

Abstract. Insights gained from social media platforms are pivotal for businesses to understand their products’ present position. While it is possible to use consulting services focusing on surveys about a product or brand, such methods may yield limited insights. By contrast, on social media, people frequently express their individual and unique feelings about products openly and informally. With this in mind, we aim to provide rigorous methodologies to enable businesses to gain significant insights on their brands and products in terms of representations on social media. This study employs conjoint analysis to lay the analytical groundwork for developing positive and negative sentiment frameworks to evaluate the brands of three prominent emerging automotive companies in Indonesia, anonymized as “HMI,” “YMI,” and “SMI.” We conducted a survey with a sample size of n=67 to analyze the phrasings of importance for our wording dictionary construction. A series of data processing operations were carried out, including the collection, capture, formatting, cleansing, and transformation of data. Our study’s findings indicate a distinct ranking of the most positively and negatively perceived companies among social media users. As a direct management-related implication, our proposed data analysis methods could assist the industry in applying the same rigor to evaluating companies’ products and brands directly from social media users’ perspective. Keywords:  Brand image, social media, data analytics, sentiment analysis, conjoint analysi

Understanding destination brand love using machine learning and content analysis method

This study aims to apply the concept of brand love in tourist destinations in order to identify the core-elements that could have influential impacts on generating destination brand love. This has been carried out by using a mixed-method of machine learning and content analysis. We have discovered that the topics have been generated for historical landmarks and destinations by analyzing the visitors’ on-line reviews are architecture, historical sites, tradition and shrine places, which could be similar to other tourist historical destinations in different part of the world. However, this study has the potential to be a model for other researches related to different destinations with possible different topics emerged. Our study contributes by providing both researchers and managers a novel method to understand what attributes of destination brand love they need to posit more emphasize to attract more visitors based on the destination type

Sentiment Analysis of Textual Content in Social Networks. From Hand-Crafted to Deep Learning-Based Models

Aquesta tesi proposa diversos mètodes avançats per analitzar automàticament el contingut textual compartit a les xarxes socials i identificar les opinions, emocions i sentiments a diferents nivells d’anàlisi i en diferents idiomes. Comencem proposant un sistema d’anàlisi de sentiments, anomenat SentiRich, basat en un conjunt ric d’atributs, inclosa la informació extreta de lèxics de sentiments i models de word embedding pre-entrenats. A continuació, proposem un sistema basat en Xarxes Neurals Convolucionals i regressors XGboost per resoldre una sèrie de tasques d’anàlisi de sentiments i emocions a Twitter. Aquestes tasques van des de les tasques típiques d’anàlisi de sentiments fins a determinar automàticament la intensitat d’una emoció (com ara alegria, por, ira, etc.) i la intensitat del sentiment dels autors a partir dels seus tweets. També proposem un nou sistema basat en Deep Learning per solucionar el problema de classificació de les emocions múltiples a Twitter. A més, es va considerar el problema de l’anàlisi del sentiment depenent de l’objectiu. Per a aquest propòsit, proposem un sistema basat en Deep Learning que identifica i extreu l'objectiu dels tweets. Tot i que alguns idiomes, com l’anglès, disposen d’una àmplia gamma de recursos per permetre l’anàlisi del sentiment, a la majoria de llenguatges els hi manca. Per tant, utilitzem la tècnica d'anàlisi de sentiments entre idiomes per desenvolupar un sistema nou, multilingüe i basat en Deep Learning per a llenguatges amb pocs recursos lingüístics. Proposem combinar l’ajuda a la presa de decisions multi-criteri i anàlisis de sentiments per desenvolupar un sistema que permeti als usuaris la possibilitat d’explotar tant les opinions com les seves preferències en el procés de classificació d’alternatives. Finalment, vam aplicar els sistemes desenvolupats al camp de la comunicació de les marques de destinació a través de les xarxes socials. Amb aquesta finalitat, hem recollit tweets de persones locals, visitants i els gabinets oficials de Turisme de diferents destinacions turístiques i es van analitzar les opinions i les emocions compartides en ells. En general, els mètodes proposats en aquesta tesi milloren el rendiment dels enfocaments d’última generació i mostren troballes apassionants.Esta tesis propone varios métodos avanzados para analizar automáticamente el contenido textual compartido en las redes sociales e identificar opiniones, emociones y sentimientos, en diferentes niveles de análisis y en diferentes idiomas. Comenzamos proponiendo un sistema de análisis de sentimientos, llamado SentiRich, que está basado en un conjunto rico de características, que incluyen la información extraída de léxicos de sentimientos y modelos de word embedding previamente entrenados. Luego, proponemos un sistema basado en redes neuronales convolucionales y regresores XGboost para resolver una variedad de tareas de análisis de sentimientos y emociones en Twitter. Estas tareas van desde las típicas tareas de análisis de sentimientos hasta la determinación automática de la intensidad de una emoción (como alegría, miedo, ira, etc.) y la intensidad del sentimiento de los autores de los tweets. También proponemos un novedoso sistema basado en Deep Learning para abordar el problema de clasificación de emociones múltiples en Twitter. Además, consideramos el problema del análisis de sentimientos dependiente del objetivo. Para este propósito, proponemos un sistema basado en Deep Learning que identifica y extrae el objetivo de los tweets. Si bien algunos idiomas, como el inglés, tienen una amplia gama de recursos para permitir el análisis de sentimientos, la mayoría de los idiomas carecen de ellos. Por lo tanto, utilizamos la técnica de Análisis de Sentimiento Inter-lingual para desarrollar un sistema novedoso, multilingüe y basado en Deep Learning para los lenguajes con pocos recursos lingüísticos. Proponemos combinar la Ayuda a la Toma de Decisiones Multi-criterio y el análisis de sentimientos para desarrollar un sistema que brinde a los usuarios la capacidad de explotar las opiniones junto con sus preferencias en el proceso de clasificación de alternativas. Finalmente, aplicamos los sistemas desarrollados al campo de la comunicación de las marcas de destino a través de las redes sociales. Con este fin, recopilamos tweets de personas locales, visitantes, y gabinetes oficiales de Turismo de diferentes destinos turísticos y analizamos las opiniones y las emociones compartidas en ellos. En general, los métodos propuestos en esta tesis mejoran el rendimiento de los enfoques de vanguardia y muestran hallazgos interesa.This thesis proposes several advanced methods to automatically analyse textual content shared on social networks and identify people’ opinions, emotions and feelings at a different level of analysis and in different languages. We start by proposing a sentiment analysis system, called SentiRich, based on a set of rich features, including the information extracted from sentiment lexicons and pre-trained word embedding models. Then, we propose an ensemble system based on Convolutional Neural Networks and XGboost regressors to solve an array of sentiment and emotion analysis tasks on Twitter. These tasks range from the typical sentiment analysis tasks, to automatically determining the intensity of an emotion (such as joy, fear, anger, etc.) and the intensity of sentiment (aka valence) of the authors from their tweets. We also propose a novel Deep Learning-based system to address the multiple emotion classification problem on Twitter. Moreover, we considered the problem of target-dependent sentiment analysis. For this purpose, we propose a Deep Learning-based system that identifies and extracts the target of the tweets. While some languages, such as English, have a vast array of resources to enable sentiment analysis, most low-resource languages lack them. So, we utilise the Cross-lingual Sentiment Analysis technique to develop a novel, multi-lingual and Deep Learning-based system for low resource languages. We propose to combine Multi-Criteria Decision Aid and sentiment analysis to develop a system that gives users the ability to exploit reviews alongside their preferences in the process of alternatives ranking. Finally, we applied the developed systems to the field of communication of destination brands through social networks. To this end, we collected tweets of local people, visitors, and official brand destination offices from different tourist destinations and analysed the opinions and the emotions shared in these tweets

A taxonomy for deriving business insights from user-generated content

Deriving business insights from user-generated content (UGC) is a widely investigated phenomenon in information systems (IS) research. Due to its unstructured nature and technical constraints, UGC is still underutilized as a data source in research and practice. Using recent advancements in machine learning research, especially large language models (LLMs), IS researchers can possibly derive these insights more effectively. To guide and further understand the usage of these techniques, we develop a taxonomy that provides an overview of business insights derived from UGC. The taxonomy helps both practitioners and researchers identify, design, compare and evaluate the use of UGC in this IS context. Finally, we showcase an LLM-supported demo application that derives novel business insights and apply the taxonomy to it. In doing so, we show exemplary how LLMs can be used to develop new or extend existing NLP applications in the realm of IS

Inside the Black Box of Dictionary Building for Text Analytics: A Design Science Approach

The purpose of this paper is to develop and demonstrate a dictionary building process model for text analytics projects following the design science methodology. Using inductive consensus-building, we examined prior research to develop an initial process model. The model is subsequently demonstrated and validated by using data to develop an environmental sustainability dictionary for the IT industry. To our knowledge, this is an initial attempt to provide a normalized dictionary building process for text analytics projects. The resulting process model can provide a road map for researchers who want to use automated approaches to text analysis but are currently prevented by the lack of applicable domain dictionaries. Having a normalized design process model will assist researchers by legitimizing their work requiring dictionary building and help academic reviewers by providing an evaluation framework. The resulting environmental sustainability dictionary for IT industry can be used as a starting point for future research on Green IT and sustainability management

Destination image online analyzed through user generated content: a systematic literature review

Destination Image is a concept that has been studied for a long time in tourism research. The question of how a destination is perceived by tourists and potential new guests is an important insight, especially for local tourism managers, in order to evaluate the implemented strategies and to plan further tactics. Since the last two decades, due to a drastic digitalization, tourism research is now increasingly examining the Destination Image online. This creates new challenges in the selection of sources, methods, and in data collection. The aim of the present study was to systematically capture the approach to analyze the online Destination Image through User Generated Content using studies from the last ten years. Therefore, a Systematic Literature Review on primary research from academic databases was conducted. As a summary of the findings, a conceptual model was developed, based on the insights of the studies in the dataset, to contribute a guidance for the preparation phase of future online Destination Image research. In short, the main findings are: TripAdvisor.com is the main source for online Destination Image analysis. Researchers recommend using the help of software and programming languages to collect and analyzed the data. Equally to earlier Destination Image studies, the main methods applied in online Destination Image analysis are quantitative content analysis, qualitative content analysis and sentiment analysis. In combination with the examination of cognitive and affective factors, co-occurrence analysis, and correlation analysis. The present study has several limitations, which are: the loss of detail information due to reducing the studies to comparable key parameters, the absence of Anglo-American studies, due to the database selection as well as the lack of quality testing of the studies included.A Destination Image é um conceito que tem sido estudado há muito tempo na investigação turística. A questão de como o destino é visto pelos turistas e pelos potenciais novos hóspedes é uma perspectiva importante, especialmente para os gestores de turismo da região, a fim de avaliar as estratégias implementadas e de planear novas tácticas. Desde as últimas duas décadas, ocorreu uma digitalização drástica, a investigação turística adaptou-se a este fenómeno e está agora a estudar cada vez mais a imagem do destino online. Esta alteração criou novos desafios na selecção de fontes, métodos, e na recolha de dados. O objetivo do presente trabalho foi o de captar, de forma sistemática, as abordagens consideradas para analisar a imagem do destino online utilizando estudos dos últimos dez anos. Para este efeito, os estudos primários dos anos 2010-2020 das bases de dados académicos Web of Science, ProQuest e b-on, foram recolhidos utilizando palavras-chave de pesquisa pré-definidas. O grupo de artigos obtidos como resultado foram subsequentemente sujeitos a avaliação de eligibilidade, como recomendado por Moher et al. (2009). Isto significa que os estudos que não cumpriam os critérios pré-definidos foram excluídos. Os critérios de inclusão foram: O trabalho académico tinha de ser uma referência primária de uma revista científica, escrita em inglês e a amostra analisada tinha de ter uma origem associada à comunicação nas social media online. Posteriormente, os restantes 35 artigos foram transferidos para uma base de dados utilizando uma matriz de codificação. A matriz de codificação foi concebida para capturar os parâmetros-chave de cada estudo primário de uma forma padronizada e, portanto, comparável. Foi considerada informação geral, como o ano, localização e revista publicada, bem como informação temática específica, como o campo do turismo pesquisado e os meios analisados, juntamente com as categorias referentes à metodologia considerada, as ferramentas utilizadas e os resultados obtidos. A base de dados resultante foi então utilizada para obter declarações sobre a abordagem metodológica utilizada na análise da imagem de destinos online. Como resumo dos resultados, foi desenvolvido um modelo conceptual, baseado nos conhecimentos obtidos a partir do grupo de artigos, que constituiu o conjunto de dados para análise, para contribuir com um guião para a fase de preparação de uma futura investigação sobre imagem dos destinos online. Em resumo, as principais conclusões são: TripAdvisor.com é a principal fonte para a análise da imagem de destinos online. Os investigadores recomendam a utilização da ajuda de software e linguagens de programação para a recolha e análise dos dados. À semelhança de estudos anteriores de Destination Image, os principais métodos aplicados na análise imagem dos destinos online são a análise quantitativa do conteúdo, a análise qualitativa do conteúdo e a análise dos sentimentos. Em combinação com a análise dos fatores cognitivos e afectivos, análise de co-ocorrência, e análise de correlação. O presente estudo tem várias limitações. Que são: a perda de informação detalhada devido à redução dos estudos a parâmetros-chave comparáveis, a ausência de estudos anglo-americanos, devido à selecção do banco de dados, bem como a falta de testes de qualidade dos estudos incluídos.(TurExperience - Tourist experiences' impacts on the destination image: searching for new opportunities to the Algarve”)

Stripping customers' feedback on hotels evaluation through data mining

Com a constante evolução tecnológica e a consequente afluência de partilha de informação entre os consumidores, as plataformas online, como é o caso do TripAdvisor, começaram a ser usadas para análise, principalmente na indústria hoteleira. Estas plataformas permitem aos clientes a partilha de opiniões e a respectiva atribuição de uma avaliação quantitativa aos hotéis visitados. Os estudos publicados têm-se focado, fundamentalmente, na análise dos comentários; contudo, estudos relacionados com a avaliação quantitativa são mais escassos. Este estudo foi desenvolvido através de técnicas de data mining por forma a modelar a pontuação atribuída no TripAdvisor. Foram recolhidos dois comentários por cada mês do ano de 2015 referentes a 21 hotéis localizados na avenida mais emblemática de Las Vegas, a Strip, num total de 504 comentários. A localização foi seleccionada por ser um destino de elevado impato turístico já que a cidade persiste devido à hotelaria e aos casinos. Foram seleccionadas 19 variáveis que representam o utilizador, o hotel e as suas características para alimentarem uma máquina de vectores de suporte objectivando a modelação da avaliação quantitativa para extração de conhecimento. Os resultados atestaram a utilidade do modelo na sua capacidade preditiva. Após esta validação foi aplicada uma análise de sensibilidade ao modelo para compreender a relevância das variáveis. Os resultados revelaram que as variáveis diretamente relacionadas com o utilizador e a sua experiência na utilização do TripAdvisor têm maior influência na atribuição das pontuações, comparativamente com as variáveis relacionadas com o hotel.The emergence of online reviews’ platforms such as TripAdvisor provided tools for tourists to write their opinions and rate hotels with a quantitative score. While numerous studies are found based on textual comments of users, research on the score is rather scarce. This study presents a data mining approach for modeling TripAdvisor score using 504 reviews published in 2015 for the 21 hotels located in the Strip, Las Vegas. Nineteen features characterizing the reviews, hotels and the users were prepared and used for feeding a support vector machine for modeling the score. The results achieved reveal the model is a good approximation for predicting the score. Therefore, a sensitivity analysis was applied over the model for extracting useful knowledge translated into features’ relevance for the score. The findings unveiled user features related to TripAdvisor membership experience play a key role in influencing the scores granted, clearly surpassing hotel features