Enhancing Hotel Management: a Sentiment Analysis Approach to Assessing Customer Impressions on Environment-Based Reviews

Purpose: This study aims to examine hotel reviews, with a specific emphasis on environmental aspects. Employing advanced sentiment analysis techniques, we delve into user sentiments expressed in evaluations.   Theoretical Framework: The theoretical framework utilizes sentiment analysis to delve into hotel reviews, emphasizing the environment. Focused on key areas such as staff service (professionalism and friendliness), hotel environment and facilities (beautiful décor and cleanliness), and affordability with a friendly pricing strategy.   Design/Methodology/Approach: This study involves the manual collection and analysis of 3,475 hotel review from Centara Hotel & Convention Center Udon Thani. The methodology includes systematic steps: Preprocessing (removing irrelevant charcters and stopword), Feature Extraction (identifying key elements), Classification (using Logistic Regression for binary sentiment analysis), and Prediction (applying the model to categorize new reviews).   Findings: In our findings, Logistic Regression effectively categorized reviews into positive or negative sentiments, boasting a robust macro precision of 0.80. Notably, positive evaluations showed superior prediction results, with a high recall value of 0.84 percent, contributing to an impressive overall accuracy of 86 percent. These results highlight the efficacy of Logistic Regression in distinguishing sentiment categories, affirming its suitability for this analysis.   Research, Practical & Social Implications: In summary, focusing on enhancing staff service, maintaining a pristine hotel environment, and offering friendly, affordable services has the potential to greatly boost customer satisfaction and overall business performance. These strategic efforts not only generate positive feedback but also lead to increased competitive advantages and expanded market share in the fiercely competitive hotel industry.   Originality/Value: The findings have broader implications by shaping public perceptions, providing valuable insights for strategic decision-making, and positioning hotels for success in the highly competitive hospitality landscape

Detailing Sentiment Analysis to Consider Entity Aspects: An Approach for Portuguese Short Texts

Sentiment analysis is useful for identifying trends, or for discovering user preferences, which can later be applied to campaign targeting or recommendations. In this paper, we describe an approach to classify the sentiment polarity regarding aspects, and how this technique was used in a previous system, for short texts in Portuguese, giving it greater sensitivity to detail. Aspect extraction is done by locating candidates for aspect as expressions having a relationship with the entity and possibly some polarized term, through rules based on POS tags. For each aspect, the sentiment polarity is determined by a Maximum Entropy classifier, whose features depend on the entity mention, on the aspect and its support text, including negation detection, bigrams, POS tags, and sentiment lexiconbased polarity clues. For aspect sentiment, our classifier evaluation indicated a precision of 68% for the positive class and 73% for the negative class, with the dataset used in our research.SmartSeg project, which is co-funded through Portugal 2020’s "R&D Incentive System - Individual Projects" program, grant number "POCI-01-0247-FEDER-011192

Improving sentiment analysis on PeduliLindungi comments: a comparative study with CNN-Word2Vec and integrated negation handling

This study investigates sentiment analysis in Google Play reviews of the PeduliLindungi application, focusing on the integration of negation handling into text preprocessing and comparing the effectiveness of two prominent methods: CNN-Word2Vec CBOW and CNN-Word2Vec SkipGram. Through a meticulous methodology, negation handling is incorporated into the preprocessing phase to enhance sentiment analysis. The results demonstrate a noteworthy improvement in accuracy for both methods with the inclusion of negation handling, with CNN-Word2Vec SkipGram emerging as the superior performer, achieving an impressive 76.2% accuracy rate. Leveraging a dataset comprising 13,567 comments, this research introduces a novel approach by emphasizing the significance of negation handling in sentiment analysis. The study not only contributes valuable insights into the optimization of sentiment analysis processes but also provides practical considerations for refining methodologies, particularly in the context of mobile application reviews

A META-ANALYTIC REVIEW OF SOCIAL MEDIA STUDIES

Social media such as social networking sites, blogs, micro-blogs, Wikis, are increasingly and widely used in our daily lives. In the information system (IS) discipline, social media have become a hot research area and draw the attention of many scholars. The paper systematically reviewed social media studies published in Association for Information Systems (AIS) listed top 20 journals from 2009 to 2013. The publication time, journal preferences, research objects and research topics are discussed. Generally, the current social media studies including four areas, namely user, management, technology and information. Each area has distinct focuses and topics. By thoroughly analyzing the research topics, the authors formulate our projections and recommendations for future social media studies

Using sentiment analysis to predict Amazon ratings : a comparative study using dictionaries approaches

This dissertation delves into the domain of sentiment analysis, a computational approach to detect and extract human sentiments from textual data. With the ever-increasing growth of online textual content, especially in the form of reviews, the need to accurately determine customer sentiment has never been more imperative. To explore the efficacy of lexicon-based sentiment analysis models, this study implements 9 models: VADER, TextBlob, NRC Lexicon, SentiWordNet, Pattern, AFINN, Opinion Lexicon, LabMT, and ANEW. These models are tested on an Amazon reviews dataset, which is uniquely accompanied by a rating system in which the accuracy of the sentiment extraction can be assessed. The study then further delves into a comparative analysis, collecting the performance of these models to discern their strengths, weaknesses, and overall utility.Esta dissertação aborda o tema de Sentiment Analysis, uma técnica que permite detetar e extrair sentimentos humanos a partir de texto. Com o crescimento exponencial de dados sob a forma de texto online, particularmente nas avaliações dos consumidores, a necessidade de determinar com precisão os sentimentos destes nunca foi tão imperativo. Esta técnica é essencial para converter os dados textuais em informação que pode ser efetivamente utilizada. Para explorar a eficácia dos modelos de Sentiment Analysis na categoria de abordagem por Dicionário, este estudo implementa nove modelos: VADER, TextBlob, NRC Lexicon, SentiWordNet, Pattern, AFINN, Opinion Lexicon, LabMT e ANEW. Estes modelos são testados numa base de dados que contém avaliações da Amazon e classificações através das quais a precisão da extração de sentimento pode ser avaliada. O estudo aprofunda-se numa análise comparativa, avaliando o desempenho destes modelos para identificar os seus pontos fortes, fracos e a sua utilidade

How is the review helpfulness evaluated?

A user-generated review that is perceived as helpful is valuable for both customer and the retailer, and that is why online markets such as Amazon.com collect public opinion on reviews that are perceived more helpful. Review platforms allow customers to vote for reviews they deem helpful. While prior literature has examined what drives the helpfulness of reviews, many of these studies have looked at drivers of perceived helpfulness of reviews in isolation. Using the lens of dual process theory, this research examines how consumers evaluate the helpfulness of a review. We propose a framework and provide empirical evidence for the evaluation of the review helpfulness process. We find that extreme reviews have a higher effect on review helpfulness compared to moderate reviews, and this effect is mediated by the depth and sentiment of the review content

How social media usage by managers affects corporate value : the case of Elon Musk

The emergence of social media has revolutionized the world where this innovation has erased boundaries, created a new reality and brought color to people’s lives. Online platforms have an effect similar to an epidemic, since almost everyone who uses the Internet is present on social networks. Initially, this worldwide phenomenon started by providing freedom of expression to its users, allowing them to be architects and creators of content, promoting themselves on a large scale, having greater visibility and more exposure. Everybody is interconnected through this virtual world. So, social networks have become a privilege not only for the CEOs, who can use them to make announcements about companies and new events, to promote actions of solidarity and Corporate Social Responsibility, but it also gave organizations the opportunity to be closer to their customers and receiving their feedback more easily, publicizing their products and services more efficiently. The constant use of social media has led to an increase of free information, which has driven investors to analyze the sentiment and opinions expressed in these platforms, and determine whether there is any relationship between the emotion intrinsic to the message broadcasted and the stock price changes. This dissertation aims to demonstrate that CEOs’ online messages on social media can influence not only their reputation, integrity and credibility, but also affect stock prices. Elon Musk's tweets are used as a reference to verify whether online posts on Twitter have an impact on investors’ opinions and trigger movements on the stock market or not.O aparecimento das redes sociais transformou o mundo onde vivemos, trouxe novos mundos ao mundo, criando uma realidade paralela àquela em que vivemos. As redes sociais tiveram um efeito semelhante ao de uma epidemia, porque grande parte das pessoas que usam a internet têm redes sociais. Inicialmente, este fenómeno mundial começou por promover liberdade de expressão aos seus utilizadores e permitiu-lhes serem os arquitetos, criadores de conteúdo, divulgando-o a uma larga escala, tendo maior visibilidade e exposição. Como se pode constatar, neste mundo virtual, estamos todos interligados, pelo que estas plataformas sociais tornaram-se um privilégio, não só para os CEOs, onde estes podem comunicar novidades relativamente às empresas e a novos eventos, demonstrar responsabilidade social e promover ações de solidariedade, como também para as empresas, já que lhes permite estar mais perto dos seus clientes, receber feedback quase imediato e promover os seus produtos e serviços. O uso constante das redes sociais conduziu ao aumento de informação disponível, levando os investidores a analisarem os sentimentos e opiniões expressos e a constatar se há alguma relação entre a emoção intrínseca à mensagem nas plataformas sociais e as alterações nos preços das ações. Esta tese pretende mostrar que as publicações nas redes sociais, feitas pelos CEOs, podem influenciar não só a sua reputação e credibilidade, como também os preços das ações. Nesta tese, usam-se como referência os tweets de Elon Musk para verificar se as mensagens online partilhadas na rede social Twitter podem ter um impacto no mercado de ações ou não