The Impact of Sentiment Analysis Output on Decision Outcomes: An Empirical Evaluation

User-generated online content serves as a source of product- and service-related information that reduces the uncertainty in consumer decision making, yet the abundance of such content makes it prohibitively costly to use all relevant information. Dealing with this (big data) problem requires a consumer to decide what subset of information to focus on. Peer-generated star ratings are excellent tools for one to decide what subset of information to focus on as they indicate a review’s “tone”. However, star ratings are not available for all user-generated content and not detailed enough in other cases. Sentiment analysis, a text-analytic technique that automatically detects the polarity of text, provides sentiment scores that are comparable to, and potentially more refined than, star ratings. Despite its popularity as an active topic in analytics research, sentiment analysis outcomes have not been evaluated through rigorous user studies. We fill that gap by investigating the impact of sentiment scores on purchase decisions through a controlled experiment using 100 participants. The results suggest that, consistent with the effort-accuracy trade off and effort-minimization concepts, sentiment scores on review documents improve the efficiency (speed) of purchase decisions without significantly affecting decision effectiveness (confidence)

A Review System Based On Product Features In A Mobile Environment

With the rapid growth of the mobile commerce, firms have been trying to get their online channels optimized for the mobile devices. However, many contents on online shopping sites are still focused on a desktop PC environment. Especially, consumer reviews are difficult to browse and grasp via a mobile device. Usually, it is not helpful to simply reduce the size of fonts or photos to fit to mobile devices without a fundamental transformation of the review presentation. In this study, we suggest a feature-based summarization process of consumer reviews in mobile environment. Further, we illustrate an implementation of the process by applying opinion mining techniques to product reviews crawled from a major shopping site in Korean. Finally, a plan for a controlled laboratory experiment is proposed to validate the effectiveness of the suggested review framework in this study

INVESTIGATING THE EFFECTS OF SELFPRESENTATION AT SOCIAL NETWORK SITES ON PURCHASE BEHAVIOR: A TEXT MINING AND ECONOMETRIC APPROACH

With advances in information and communication technologies (ICT), companies and platforms look to use the increasing volume and diversity of user-generated content (UGC) to predict consumer behavior, but with mixed results. In this study, we propose a text mining technique to find support for self-presentation in online social media and show that this is correlated with the content producer’s offline purchase behaviour. We use unique datasets from a social network site (SNS) and an offline fashion retailer to find that: 1) while public and private volume and sentiment metrics leads to non-significant predictions, the sentiment divergence can significantly explain offline purchases, 2) users who engage in SNS for self-presentation spend less money and buy less quantities, and 3) however, they spend more when exposed to specific site features that inspire self-presentation, like brand pages. Marketers and platform owners can benefit from our results by designing appropriate features to target such users

A Context-Dependent Sentiment Analysis of Online Product Reviews based on Dependency Relationships

Consumers often view online consumer product review as a main channel for obtaining product quality information. Existing studies on product review sentiment analysis usually focus on identifying sentiments of individual reviews as a whole, which may not be effective and helpful for consumers when purchase decisions depend on specific features of products. This study proposes a new feature-level sentiment analysis approach for online product reviews. The proposed method uses an extended PageRank algorithm to extract product features and construct expandable context-dependent sentiment lexicons. Moreover, consumers’ sentiment inclinations toward product features expressed in each review can be derived based on term dependency relationships. The empirical evaluation using consumer reviews of two different products shows a higher level of effectiveness of the proposed method for sentiment analysis in comparison to two existing methods. This study provides new research and practical insights on the analysis of online consumer product reviews

Sentiment analysis of electronic word of mouth (E-WoM) on e-learning.

The proliferation of social media and the internet has given people many opportunities to air their views and to be at liberty to say what they feel without hindrance. This is beneficial to commercial organizations and the general well-being of the populace. However, the cost of this freedom is that spamming is practiced with little or no control. This chapter focuses on the electronic word of mouth (eWOM) of opinion holders and the sentiments expressed in eWOM. One of the areas of life impacted by sentiment is electronic learning because it has become a prevalent mode of learning. The study aims to analyze eWOM on e-learning which can help in identifying learners' sentiments. Findings from three thousand tweets show more neutral sentiments, followed by positive sentiments. Suggestions and recommendations as well as the future directions for sentiment analysis of eWOM on e-learning are also discussed in this chapter

Dynamic Characteristic of Consumer Attention in Online Reviews —Empirical Research Based on Mobile Store Reviews

Nowadays consumer online reviews are becoming more and more important for enterprise decision-making. While the existing research seldom discussed review data from a dynamic perspective, especially ignored consumers\u27 attention change during the product life cycle. To study whether there are dynamic changes and the characteristics of changes in the attention degree of consumers in each phase of the product life cycle, this paper coded a specific node program to collect the online reviews data of the four mobile phones in the entire product life cycle and used python\u27s Chinese automatic word segmentation tool library to segment each word and count word frequency, and then a stepwise regression method was used to analyze the dynamic changes of consumer attention. The paper finds that consumers’ attention on logistics and products presented in online reviews show a downward trend, and the attention on brands shows an upward trend; There is no obvious change in the attention degree on services, prices, and promotion; On the different dimensions of products, there is a significant difference in the attention degree. The research results broad the research ideas of online reviews, provide decision-making basis for enterprises to grasp the characteristics of consumers at different stages and to formulate production and marketing strategies

Effects of Sentiment on Impulsive Buying Behavior: Evidence of COVID-19 in Indonesia

Abstract This study aims to investigate the effects of positive and negative sentiment on impulsive buying behavior among Indonesia people based on the theory of stimulus organism response (S-O-R). First, it examines how COVID-19 information, information credibility, and scarcity affect positive sentiment and negative sentiment. Second, it verifies the influence of positive sentiment and negative sentiment on impulsive buying tendencies and impulsive buying behavior. Third, this study verifies impulsive buying tendency impacts impulsive buying behavior. Data was collected from Indonesian people living in a COVID-19 red zone with an online survey via Google form. In total, 320 respondents completed the survey and data analysis employs confirmatory factor analysis (CFA) and structural equation modelling (SEM).  The result found that COVID-19 information and information credibility have a positive effect on positive sentiment, while it has an insignificant effect on negative sentiment. Scarcity has a positive effect on negative sentiment; on the other hand, it has no significant effect on positive sentiment. Both positive sentiment and negative sentiment have positive effects on impulsive buying tendencies.  Only positive sentiment has a positive effect on impulsive buying behavior, while negative sentiment does not. Finally, impulsive buying tendencies have a positive effect on impulsive buying behavior.   AbstrakPenelitian ini bertujuan untuk menginvestigasi pengaruh positif sentimen dan negative sentimen terhadap perilaku pembelian tidak terencana masyarakat Indonesia berpijak pada teori stimulus organism response (S-O-R). Pertama, penelitian ini menguji bagaimana pengaruh informasi tentang COVID-19, kredibilitas informasi, dan kelangkaan terhadap sentimen positif dan sentimen negatif. Kedua, memverifikasi pengaruh sentimen positif dan sentimen negatif terhadap kecenderungan untuk melakukan pembelian tidak terencana dan perilaku pembelian tidak terencana. Ketiga, memverifikasi pengaruh kecenderungan untuk melakukan pembelian tidak terencana dan perilaku pembelian tidak terencana. Pengumpulan data penelitian ini dilakukan terhadap orang-orang Indonesia yang tingga di zona merah COVID-19 melalui survey online dengan Google form. Secara total ada 320 responden berpartisipasi dalam survey ini, kemudian data dianalisis menggunakan analisis confirmatory (CFA) dan struktural equation modeling (SEM). Hasilnya menunjuukan bahwa informasi tentang COVID-19 dan kredibilitas informasi mempunyai pengaruh positif terhadap sentimen positif, tetapi tidak mempunyai pengaruh yang signifikan terhadap sentimen negatif. Kelangkaan mempunyai pengaruh positif terhadap sentimen negatif, sebaliknya tidak mempunyai pengaruh yang signifikan terhadap sentimen positif. Baik sentimen positif maupun sentimen negatif mempunyai pengaruh positif terhadap kecenderungan untuk melakukan pembelian tidak terencana. Hanya, sentimen positif yang mempunyai pengaruh positif terhadap perilaku pembelian tidak terencana, sedangkan sentimen negatif tidak berpengaruh. Terakhir, kecenderungan untuk melakukan pembelian tanpa rencana mempunya pengaruh positif terhadap perilaku pembelian tidak terencana

How tourists perceive two island destinations with identical culture, but different demographic characteristics, through social networking sites?: the case of Madeira and Bermuda

This thesis is focused and aimed at exploring how tourists perceive two island destinations with identical culture, but different demographic characteristics, through Social Networking Sites. The islands of Madeira and Bermuda were chosen as the ones to be studied. In order to provide a trustful answer, it was conducted two different methodologies that complete each other: Netnography and Text Mining. Eight codings were used when creating the database, based on the literature studied: Rating, Categories, Membership Level of reviewers, Language type, Tourism Experience Model, Content, Symbology and Positive/Negative information. In the first methodology, all of these codings were studied, while in the Text Mining one, only three were considered: TEM, ML and categories. The database was created after an extensive extraction process, which included in the end 1783 reviews – 1148 from Madeira and 635 from Bermuda, extracted from the TripAdvisor platform regarding August of 2017. The results indicate an overall positive customer satisfaction regarding both islands, but slightly superior in the Madeira’s case. The reviewers were, globally, low ranked reviewers and their favorite type of language was literal (vs figurative). The negative content was similar in both cases and the positive one as well. Based on the literature it was perceived that the reviews extracted are capable of persuading readers, and increase their booking intention. Also, both islands were perceived as destinations where tourists are able, through Recreational Activities, to get closer with their existential being. Lifestyle and Leisure and Tourism, Travel and Commuting were the main themes found to being mentioned by the reviewers. It was perceived that reviewers from Madeira can highly influence potential tourists into becoming real ones, in comparison with Bermuda. In both cases, reviewers were found to be engaged with the destinations, and the “re-purchase” scenario is highly probableO objectivo desta tese é explorar de que forma o turista percebe dois destinos insulares com cultura idêntica, mas características demográficas distintas, através das Redes Sociais. Para o efeito, as ilhas da Madeira e da Bermuda foram escolhidas para ser objecto de estudo. Para obter uma resposta fidedigna foram estabelecidas duas metodologias diferentes, com vista a complementarem-se: Netnografia e Mineração de Dados. Com base na literatura estudada, foram utilizados oito critérios aquando a criação da base de dados: Classificação, Categorias, Nível dos utilizadores, Tipo de linguagem, Tourism Experience Model, Conteúdo, Simbologia e Informações Positivas/Negativas. Na primeira metodologia, todas os critérios foram utilizados, enquanto na segunda foram considerados apenas três: TEM, Nível dos utilizadores e Categorias. A base de dados foi criada após um longo processo de extracção, que culminou no final com 1783 avaliações – 1148 referentes à Madeira e 635 relativos à Bermuda, retiradas da plataforma TripAdvisor, referentes a Agosto de 2017. Os resultados indicam uma satisfação geral positiva dos utilizadores em relação a ambas as ilhas, mas ligeiramente superior no caso da Madeira. Estes eram, globalmente, caracterizados com baixo nível de membership, e predomina a linguagem literal (vs figurativa). A quantidade de conteúdo negativo é similar em ambos os casos, bem como a informação positiva. Com base na literatura, foi concluído que as avaliações extraídas são capazes de persuadir os leitores e aumentar as suas intenções de reserva para com os destinos estudados. Além disso, ambas as ilhas foram percebidas como destinos onde os turistas podem, por meio de Atividades Recreacionais, aproximar-se do seu ser existencial. "Lifestyle and Leisure" e "Tourism, Travel and Commuting" foram os principais temas de conversa mencionados pelos utlizadores. Foi concluído também que as avaliações extraídas da Madeira têm maior capacidade para tornar potenciais turistas, em turistas reais do destino (comparativamente à Bermuda). Em ambos os casos, verificou-se engagement positivo, e o cenário de "recompra" é altamente possível

Disney plus Hotstar on Twitter: Using netnography and word clouds to gain consumer insights

Microblogging platform Twitter is being used more and more by businesses to promote and connect with their brands. The main goal of this manuscript is to identify the content typologies that Disney Plus Hotstar utilizes on Twitter to encourage customer engagement. This has been accomplished through the usage of technique termed as Netnography. The document then uses wordclouds to extract user data from the Disney Plus Hotstar twitter feed

A text-mining based model to detect unethical biases in online reviews: a case-study of Amazon.com

The rapid growth of social media in the last decades led e-commerce into a new era of value co-creation between the seller and the consumer. Since there is no contact with the product, people have to rely on the description of the seller, knowing that sometimes it may be biased and not entirely truth. Therefore, reviewing systems emerged in order to provide more trustworthy sources of information, since customer opinions may be less biased. The problem was, once sellers realized the importance of reviews and their direct impact on sales, the need to control this key factor arose. One of the methods developed was to offer customers a certain product in exchange for an honest review. However, in the light of the results of some studies, these "honest" reviews were proved to be biased and skew the overall rating of the product. The purpose of this work is to find patterns in these incentivized reviews and create a model that may predict whether a new review is biased or not. To study this subject, besides the sentiment analysis performed on the data, some other characteristics were taken into account, such as the overall rating, helpfulness rate, review length and the timestamp when the review was written. Results show that some of the most significant characteristics when predicting an incentivized review are the length of a review, its helpfulness rate and the overall polarity score, calculated through VADER algorithm, as the most important sentiment-related factor.O rápido crescimento das redes sociais nas últimas décadas levaram o comércio electrónico a uma nova era de co-criação de valor entre o vendedor e o consumidor. Uma vez que não há contacto com o produto, os clientes têm de se basear na descrição do vendedor, mesmo sabendo que por vezes tal descrição pode ser tendenciosa e não totalmente verdadeira. Deste modo, surgiu um sistema de reviews com o propósito de disponibilizar um meio de informação de maior confiança, uma vez que se trata de partilha de informação entre clientes e por isso mais imparcial. No entanto, quando os vendedores se aperceberam da importância das "reviews" e o seu impacto direto nas vendas, surgiu a necessidade de controlar este fator chave. Uma das formas de o fazer foi através da oferta de determinados produtos em troca de "reviews" honestas. Contudo, à luz dos resultados de alguns estudos, foi demonstrado que estas "reviews" "honestas" são tendenciosas e enviesam a classificação geral do produto. O objetivo deste estudo foi o de encontrar padrões na forma como estas "reviews" incentivadas são escritas e criar um modelo para prever se uma determinada review seria enviesada. Para esta análise, além da análise de sentimentos realizada sobre os dados, outras características foram tidas em conta, tal como a classificação geral, a taxa de "helpfulness", o tamanho da "review" e a hora a que foi escrita. Os modelos gerados mostraram que as características mais importantes na previsão de parcialidade numa "review" são o tamanho e a taxa de utilidade e como característica sentimental mais relevante a pontuação geral da "review", calculada através do algoritmo VADER