Sentiment analysis in retail: the case of Parfois facebook page

The way that consumers are interacting with brands is changing, and in Retail it is no different. With the growth of internet usage and with all the social networks that we interact with, social media is gaining more and more relevance and importance. This research extracted 1.845 posts, 8.256 comments and more than 500.000 reactions from Parfois Facebook page. The comments were translated to English due to having comments made in several different languages, modelled and finally made the sentiment analysis. This analysis was made concerning the post dates, the reasons of the post and the products associated in the post. It was used decision tree algorithms to predict sentiments, so it can be predicted the sentiment when making a new post. With the Sentiment Analysis from Social Media, Parfois can gain understanding about their own brand, from the marketing department through to the buying or even design departments. Using Social Media analysis together with Business Intelligence, can help Parfois decision makers gain competitive advantage regarding their competitors or even improve their products.A maneira como os consumidores interagem com as marcas está a mudar, e no retalho não é diferente. Com o aumento do uso da internet e com todas as redes sociais que interagimos, as redes sociais ganham mais relevância e importância. Esta pesquisa extraiu 1.845 posts, 8.256 comentários e mais de 500.000 reações da página de Facebook da Parfois. Os comentários foram traduzidos para o inglês devido ao fato de haver comentários feitos em várias línguas diferentes, modelados e finalmente feita a análise de sentimentos. Esta análise foi feita em relação às datas das publicações, os motivos do post, os produtos associados ao post. Foram utilizados algoritmos de árvores de decisão para prever sentimentos para que se possa prever o sentimento ao fazer um novo post. Com a analise de sentimentos das redes sociais, a Parfois pode entender melhor a sua própria marca, desde o departamento de marketing até ao departamento de compras ou mesmo o departamento de design. Usar a análise de sentimentos das redes sociais junto com o Business Intelligence organizacional, pode ajudar os decisores da Parfois a ganhar vantagem competitiva em relação aos concorrentes ou mesmo a melhorar seus produtos

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

A Modified Decomposed Theory of Planned Behaviour Model to Analyze User Intention towards Distance-Based Electronic Toll Collection Services

This study proposes a modified decomposed theory of planned behaviour model (DTPB) that integrates satisfaction and trust into the original DTPB model to explore what kind of factors affect the user intention towards distance-based electronic toll collection (ETC) services. The proposed model is empirically tested by using data collected from a questionnaire survey with a computer assisted telephone interview system. Empirical analysis is carried out in three stages that combine confirmatory factor analysis, structural equation modelling (SEM), and Bayesian network: (1) examination of reliability and validity of the measurement model; (2) analysis of structural model; (3) prediction of the probability of user intention change based on rigorous framework of SEM. The results confirm that the satisfaction and trust have positive effects on the behaviour intention, also validating that five constructs have indirect effects on the behaviour intention through attitude and perceived behaviour control. Compatibility is the most important influence factor, followed by perceived usefulness, facilitating conditions, self-efficacy, and perceived ease of use. The findings of this study identify potential improvements for ETC operator, such as contributing to the society to enhance the company image and trust of enterprise with charity activities, and simultaneously upgrading the information platform of website, software, and Apps.</p

CONSUMERS’ ENDORSEMENT EFFECTS ON MARKETER AND USER-GENERATED CONTENT IN A SOCIAL MEDIA BRAND COMMUNITY

The effects of marketer-generated content (MGC) and user-generated content (UGC) on inducing consumers’ responses have been widely studied as stand-alone main effects. Extending these research, this paper studies the interaction effects of consumers’ endorsements on MGC and UGC posts in a social media brand community (SMBC) of a popular Asian fashion retailer. We examined if passive and active consumers’ endorsements have enhancement effects on MGC/UGC and if they are also effective in inducing consumers’ expenditure by themselves. Passive endorsement refers to “likes” on social network sites (SNS), while active endorsement refers to the more involved act of “commenting” on a post. We found evidence that active endorsements positively moderate the effects of MGC in inducing consumers’ expenditure. However, passive endorsements negatively moderated MGC, making it less effective in inducing expenditure. Interestingly, the results were reversed for UGC whereby passive endorsements positively moderated UGC, while active endorsements negatively moderated UGC in inducing expenditure. Meanwhile, active endorsements through social-tagging on brand fans were found to be very effective, with recipients of social-tags spending $6 more than non-recipients in a particular week. Additional robustness checks on selection bias were conducted, and results remain qualitatively similar

Understanding the Association between Star Ratings and Review Helpfulness: The Perspectives of Expectation Confirmation Theory and Negativity Bias

Consisting of textual, multimedia, and numerical information elements, online consumer reviews (OCR) have been considered an essential information source of products for prospective consumers. Researchers have made significant efforts to comprehend how these information elements are associated with OCRs’ information value or helpfulness. However, there is a paucity of theoretical evidence on consumers’ perception and evaluation of star ratings and their information, even though star ratings as numerical information cues can imply multiple meanings. In this study, we leverage (1) expectation-confirmation theory to delineate star ratings as the extent of consumer satisfaction and (2) negativity bias to explain the relationship between star ratings and helpfulness. Using 45,621 reviews of 20 products across three categories, we empirically find that our theoretical approaches improve our understanding of the effect of star ratings on helpfulness. Therefore, this study contributes to the extant literature on OCRs by providing the theory-based evaluation of star ratings in relation to helpfulness

SENTIMENTALNA ANALIZA SADRŽAJA DRUŠTVENIH MEDIJA HRVATSKE HOTELSKE INDUSTRIJE

While social media have become a daily routine in modern society, brand communication and engagement with customers have become essential elements of marketing strategy and success in the tourism and hotel industry. This revolution of social media, in tourism and hospitality marketing, contributed to the rise of a novel sentiment analysis from a machine learning and natural language processing point of view. The purpose of the study is: to provide a general descriptive overview of comments posted by Facebook page followers; to identify specific textual attributes of hotel brand posts on social media and to apply the sentiment analysis to Facebook comments from four- and five-star hotel brands in Croatia to identify and compare customers\u27 feelings and attitudes towards the staff, services and products that hotel brands promote by posting messages on Facebook pages. To analyse hotel brand sentiments, the authors collected a total of 4,248 comments and 2,373 postings in English, German and Italian. The results showed that the comments on four- and five-star hotel brands expressed predominantly positive sentiments. Despite the positively oriented sentiments in the comments, Facebook page followers are predominantly passive users and do not tend to comment actively. The results can be used by marketers in the tourism and hospitality industry to plan their future social media communication strategies.Iako su društveni mediji postali svakodnevica u modernom društvu, brend komuniciranje i uključenost potrošača postali su ključni elementi marketinške strategije i uspjeha u sektoru turizma i ugostiteljstva. Revolucija društvenih medija, u marketingu, turizmu i ugostiteljstvu, pridonijela je razvoju sentimentalne analize sa stajališta strojnog učenja i obrade prirodnog jezika. Svrha ovog rada je: pružiti opći deskriptivni pregled komentara objavljenih od strane pratitelja Facebook stranice; identificirati specifične tekstualne karatkeristike objava hotelskih brendova na Facebook društvenoj mreži i primijeniti sentimentalnu analizu nad Facebook komentarima hotelskih brendova s četiri i pet zvjezdica u Hrvatskoj kako bi se identificirali i usporedili osjećaji, mišljenja i stavovi kupaca prema osoblju, uslugama i proizvodima koje hotelski brendovi promoviraju objavljivanjem poruka na Facebook stranicama. Da bi se analizirali sentimenti komentara pratitelja hotelskih brendova na Facebook društvenoj mreži, autori su prikupili ukupno 4.248 komentara i 2.373 objave na engleskom, njemačkom i talijanskom jeziku. Rezultati su pokazali da su komentari na stranicama hotelskih brendova imali pretežno pozitivan sentiment. Unatoč pozitivno orijentiranim osjećajima u komentarima, pratitelji Facebook stranica su uglavnom pasivni korisnici i ne sudjeluju aktivno u komentiraju objava. Rezultati mogu koristiti marketinškim stručnjacima u turizmu i ugostiteljstvu za planiranje budućih strategija komunikacije putem društvenih media

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

Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques

Livestreaming commerce, a hybrid of e-commerce and self-media, has expanded the broad spectrum of traditional sales performance determinants. To investigate the factors that contribute to the success of livestreaming commerce, we construct a longitudinal firm-level database with 19,175 observations, covering an entire livestreaming subsector. By comparing the forecasting accuracy of eight machine learning models, we identify a random forest model that provides the best prediction of gross merchandise volume (GMV). Furthermore, we utilize explainable artificial intelligence to open the black-box of machine learning model, discovering four new facts: 1) variables representing the popularity of livestreaming events are crucial features in predicting GMV. And voice attributes are more important than appearance; 2) popularity is a major determinant of sales for female hosts, while vocal aesthetics is more decisive for their male counterparts; 3) merits and drawbacks of the voice are not equally valued in the livestreaming market; 4) based on changes of comments, page views and likes, sales growth can be divided into three stages. Finally, we innovatively propose a 3D-SHAP diagram that demonstrates the relationship between predicting feature importance, target variable, and its predictors. This diagram identifies bottlenecks for both beginner and top livestreamers, providing insights into ways to optimize their sales performance.Comment: 25 pages, 10 figure