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1 – 10 of over 10000Maryam Ghasemaghaei, Seyed Pouyan Eslami, Ken Deal and Khaled Hassanein
The purpose of this paper is twofold: first, to identify and validate reviews’ length and sentiment as correlates of online reviews’ ratings; and second, to understand the…
Abstract
Purpose
The purpose of this paper is twofold: first, to identify and validate reviews’ length and sentiment as correlates of online reviews’ ratings; and second, to understand the emotions embedded in online reviews and how they associate with specific words used in such reviews.
Design/methodology/approach
A panel data set of customer reviews was collected for auto, life, and home insurance from January 2012 to December 2015 using a web scraping technique. Using a sentiment analysis approach, 1,584 reviews for the auto, home, and life insurance services of 156 insurance companies were analyzed.
Findings
The results indicate that, since 2013, consumers have generally had more negative emotions than positive ones toward insurance services. The results also show that consumer review sentiment correlates positively and review length correlates negatively with consumer online review ratings. Furthermore, a two-way ANOVA analysis shows that, in general, short reviews with positive sentiment are associated with high review ratings.
Practical implications
The findings of this study provide service companies, in general, and insurance companies, in particular, with important guidelines that should be considered to increase consumers’ positive attitude toward their services.
Originality/value
This paper highlights the importance of sentiment analysis in identifying consumer reviews’ emotions and understanding the associations and interactions of reviews’ length and sentiment on online review rating, which can lead to improved marketing strategies.
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Cen Song, Li Zheng and Xiaojun (Gene) Shan
Internet-famous food (also known as “online celebrity” food) is very popular in the digital age. This study aims to investigate consumer attitudes and understand consumer behavior…
Abstract
Purpose
Internet-famous food (also known as “online celebrity” food) is very popular in the digital age. This study aims to investigate consumer attitudes and understand consumer behavior towards Internet-famous food.
Design/methodology/approach
The authors collected 136,835 online comments regarding “Internet-famous food” from Dianping platform between 2016 and 2019 using a web scraper. A sentiment lexicon for Internet-famous food was constructed, and sentiment analysis is further conducted to understand consumer attitudes. Additionally, the authors use topic analysis and time series analysis to study consumer behavior.
Findings
Sentiment analysis showed that the number of consumers' comments decreased over time with the attitudes being overall positive, and the Internet-famous food industry has a positive prospect; time series analysis showed that the consumption of Internet-famous food was not affected by the season; topic analysis showed that consumers' comments on Internet-famous food were rich with a large variety, covering food categories, brand, quality, service, environment and price.
Originality/value
To the authors’ knowledge, limited research has focused on public opinions regarding “Internet-famous food”. This is the first study on consumer behavior towards Internet-famous food. This article provides a unique insight into the purchasing behavior and attitude of Chinese Internet-famous food consumers through text mining.
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Marcus Renatus Johannes Wolkenfelt and Frederik Bungaran Ishak Situmeang
The purpose of this paper is to contribute to the marketing literature and practice by examining the effect of product pricing on consumer behaviours with regard to the…
Abstract
Purpose
The purpose of this paper is to contribute to the marketing literature and practice by examining the effect of product pricing on consumer behaviours with regard to the assertiveness and the sentiments expressed in their product reviews. In addition, the paper uses new data collection and machine learning tools that can also be extended for other research of online consumer reviewing behaviours.
Design/methodology/approach
Using web crawling techniques, a large data set was extracted from the Google Play Store. Following this, the authors created machine learning algorithms to identify topics from product reviews and to quantify assertiveness and sentiments from the review texts.
Findings
The results indicate that product pricing models affect consumer review sentiment, assertiveness and topics. Removing upfront payment obligations positively impacts the overall and pricing specific consumer sentiment and reduces assertiveness.
Research limitations/implications
The results reveal new effects of pricing models on the nature of consumer reviews of products and form a basis for future research. The study was conducted in the gaming category of the Google Play Store and the generalisability of the findings for other app segments or marketplaces should be further tested.
Originality/value
The findings can help companies that create digital products in choosing a pricing strategy for their apps. The paper is the first to investigate how pricing modes affect the nature of online reviews written by consumers.
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Ozgur Ozdemir, Wenjia Han and Michael Dalbor
The purpose of this paper is twofold. First, the study examines the prolonged effect of policy-related economic uncertainty on hotel operating performance, particularly the room…
Abstract
Purpose
The purpose of this paper is twofold. First, the study examines the prolonged effect of policy-related economic uncertainty on hotel operating performance, particularly the room demand (occupancy). Second, the study attempts to explain why occupancy drops when the perceived economic uncertainty is high by studying the mediating effect of consumer sentiment in the relationship between economic policy uncertainty and hotel demand.
Design/methodology/approach
This quantitative study uses secondary data – US economic policy uncertainty (EPU) index, University of Michigan's index of consumer sentiment (ICS), and property-level hotel operating data from three states of the US – California, Florida and New York. Data were analyzed using random effect regression and structural equation modeling. Robustness tests were conducted to enhance the reliability of the research findings.
Findings
Random-effects regression analysis reveals that policy-related economic uncertainty has a negative and lead-lag effect on hotel occupancy, average daily rate and revenue per available room (RevPAR). Structural equation modeling results show that the relationship between economic policy uncertainty and hotel occupancy is significantly mediated by consumer sentiment. Robustness test results support the findings from the main analysis.
Practical implications
This study offers valuable implications for the hotel professionals in regard to anticipating the economic impact of policy-related uncertainty on hotel industry and understanding how consumer sentiment affects demand at such crises times. Moreover, the study suggests potential course of actions to deal with declining room demand at times of uncertainty.
Originality/value
This empirical study explores how economic policy uncertainty affects hotel performance at the property level and explains the mediating effect of consumer sentiment on hotel room demand. The study provides a first-hand evidence of how consumer sentiment relates to the perception of economic uncertainty and leads to decline in consumer demand. In that regard, findings of the study have valuable implications for hospitality industry practitioners and relevant policymakers.
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The purpose of this paper is to explore consumer attitudes towards ambush marketing and official event sponsorship through the lens of sentiment analysis, and to examine social…
Abstract
Purpose
The purpose of this paper is to explore consumer attitudes towards ambush marketing and official event sponsorship through the lens of sentiment analysis, and to examine social media users' ethical responses to digital event marketing campaigns during the 2018 FIFA World Cup.
Design/methodology/approach
The study employed a sentiment analysis, examining Twitter users’ utilization of sponsor and non-sponsor promotional hashtags. Statistical modelling programme R was used to access Twitter’s API, enabling the analysis and coding of user tweets pertaining to six marketing campaigns. The valence of each tweet – as well as the apparent user motivation underlying each post – was assessed, providing insight into Twitter users’ ethical impressions of sponsor and ambush marketer activities on social media and online engagement with social media marketing.
Findings
The study’s findings indicate that consumer attitudes towards ambush marketing may be significantly more positive than previously thought. Users’ attitudes towards ambush marketing appear significantly more positive than previously assumed, as users of social media emerged as highly responsive to creative and value-added non-sponsor campaigns.
Originality/value
The findings affirm that sentiment analysis may afford scholars and practitioners a viable means of assessing consumer attitudes towards social marketing activations, dependent upon campaign objectives and strategy. The study provides a new and invaluable context to consumer affect and ambush ethics research, advancing sponsorship and ambush marketing delivery and social sponsorship analytical practice.
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David Burns, Pola B. Gupta and Günter Buerke
– The purpose of this study is to examine whether sentiment toward marketing differs between students attending universities in the USA and Germany.
Abstract
Purpose
The purpose of this study is to examine whether sentiment toward marketing differs between students attending universities in the USA and Germany.
Design/methodology/approach
The sample was drawn from students attending classes in professional programs at two universities in the USA and two universities in Germany. The resulting sample sizes were 312 from the Germany institutions and 392 from the US institutions. Sentiment toward marketing was measured using the Index of Consumer Sentiment toward Marketing.
Findings
The hypothesis that students attending universities in Germany possess lower sentiments toward marketing is supported. Only the first hypothesis addressing the individual aspects of marketing is supported; however, a significant (at the 0.05 level) difference was only observed for sentiment toward advertising. In that instance, students attending universities in Germany were shown to possess more negative sentiment toward advertising than students attending universities in the USA.
Practical implications
The lower sentiment toward advertising among students attending universities in Germany may be expected to present a challenge to marketers attempting to reach these individuals. Their lower sentiment toward advertising may lead German students to be less likely to accept messages conveyed via advertising than their counterparts in the USA.
Originality/value
Past research suggests that differences in sentiment toward marketing exist between consumers residing in nations at different stages of development and with differing types of market structures. Do differences exist, however, between different nations at similar levels of development?
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B. Rajeswari, S. Madhavan, Ramakrishnan Venkatesakumar and S. Riasudeen
This study aims to compare online review characteristics, review length and review sentiment score between “organic” and “regular” food products. In addition, variations in the…
Abstract
Purpose
This study aims to compare online review characteristics, review length and review sentiment score between “organic” and “regular” food products. In addition, variations in the consumer sentiment scores across the review lengths are studied.
Design/methodology/approach
This study fits into the descriptive research design. From Amazon’s website, the consumer product reviews are scrapped. Using the text analytical package “sentiment” in R-Studio, we computed the sentiment scores and counted the number of words in each review. The mean sentiment scores and mean review length are compared for regular and organic products using one-way ANOVA. Sentiment score variation across review length and product class is studied through factorial ANOVA. Sample reviews of ghee and honey are used to test the hypotheses.
Findings
The review length shows a significant difference between the regular and organic products. The mean number of words in the regular products reviews is significantly lower than the mean number of words in the organic product reviews. The regular products’ mean sentiment score is significantly lower than the mean sentiment score of organic products. The mean sentiment scores are not consistent between ghee and honey. Sentiment scores are better for organic honey and regular ghee products. For regular ghee products, longer reviews result in lower sentiment scores. For regular and organic versions of honey, longer reviews are associated with better sentiment scores.
Research limitations/implications
This study did not include the helpfulness of a review and the demographic data of the reviewers.
Practical implications
Sentiment scores’ variations across the regular and organic and product categories such as ghee and honey give a comprehensive feedback to the firms. It also indirectly communicates a brand’s evaluation by the consumers and the performance feedback for an upward extension like the organic category.
Social implications
Studies on organic category give feedback for environment-friendly products and consumer attitude shift towards safer products.
Originality/value
Very limited studies have reported the upward line extensions. The authors study the upward line extension organic and associated sentiment scores variation. The role of review length and its systematic influence on the sentiment scores, variations in the review due to the product nature (organic/regular) are unique contributions of this study.
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Barkha Bansal and Sangeet Srivastava
Vast volumes of rich online consumer-generated content (CGC) can be used effectively to gain important insights for decision-making, product improvement and brand management…
Abstract
Purpose
Vast volumes of rich online consumer-generated content (CGC) can be used effectively to gain important insights for decision-making, product improvement and brand management. Recently, many studies have proposed semi-supervised aspect-based sentiment classification of unstructured CGC. However, most of the existing CGC mining methods rely on explicitly detecting aspect-based sentiments and overlooking the context of sentiment-bearing words. Therefore, this study aims to extract implicit context-sensitive sentiment, and handle slangs, ambiguous, informal and special words used in CGC.
Design/methodology/approach
A novel text mining framework is proposed to detect and evaluate implicit semantic word relations and context. First, POS (part of speech) tagging is used for detecting aspect descriptions and sentiment-bearing words. Then, LDA (latent Dirichlet allocation) is used to group similar aspects together and to form an attribute. Semantically and contextually similar words are found using the skip-gram model for distributed word vectorisation. Finally, to find context-sensitive sentiment of each attribute, cosine similarity is used along with a set of positive and negative seed words.
Findings
Experimental results using more than 400,000 Amazon mobile phone reviews showed that the proposed method efficiently found product attributes and corresponding context-aware sentiments. This method also outperforms the classification accuracy of the baseline model and state-of-the-art techniques using context-sensitive information on data sets from two different domains.
Practical implications
Extracted attributes can be easily classified into consumer issues and brand merits. A brand-based comparative study is presented to demonstrate the practical significance of the proposed approach.
Originality/value
This paper presents a novel method for context-sensitive attribute-based sentiment analysis of CGC, which is useful for both brand and product improvement.
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Farid Irani, Abobaker Al.Al. Hadood, Salih Katircioglu and Setareh Katircioglu
This paper focuses on the role of sentiment and monetary policy (both domestic and the United States (US)) in explaining the changes in the Mexican tourism firms' stock returns…
Abstract
Purpose
This paper focuses on the role of sentiment and monetary policy (both domestic and the United States (US)) in explaining the changes in the Mexican tourism firms' stock returns for the period 1998M03–2019M12.
Design/methodology/approach
The authors conducted the ordinary least square regression estimations using various models to investigate the impact of sentiment and monetary policy changes on tourism firms' stock returns. Furthermore, to provide a robust check, the authors run all regression models based on the capital asset pricing model by regressing the excess returns of tourism firms' stocks on all independent variables.
Findings
Empirical findings reveal that the changes in Mexican consumer sentiment have a stronger positive effect on tourism firms' stock returns than Mexican business sentiment changes. However, the US consumer and business sentiment are irrelevant to tourism firms' stock returns. Moreover, this study’s results indicate that changes in the US interest rates positively influence tourism firms' stock returns. This study’s findings show that as the monetary divergence between Mexico and the US (differential real interest rates) widens, the lower is the tourism firms' stock returns.
Originality/value
This study is the first to extend the prior studies by examining the effects of sentiment and monetary policy (both domestic and US role) on Mexican tourism stock return.
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Xianfeng Zhang, Yang Yu, Hongxiu Li and Zhangxi Lin
User-generated content (UGC), i.e. the feedback from consumers in the electronic market, including structured and unstructured types, has become increasingly important in…
Abstract
Purpose
User-generated content (UGC), i.e. the feedback from consumers in the electronic market, including structured and unstructured types, has become increasingly important in improving online businesses. However, the ambiguity and heterogeneity, and even the conflict between the two types of UGC, require a better understanding from the perspective of human cognitive psychology. By using online feedback on hotel services, the purpose of this paper is to explore the effects of satisfaction level, opinion dispersion and cultural context background on the interrelationship between structured and unstructured UGC.
Design/methodology/approach
Natural language processing techniques – specifically, topic classification and sentiment analysis on the sentence level – are adopted to retrieve consumer sentiment polarity on five attributes relative to itemized ratings. Canonical correlation analyses are conducted to empirically validate the interplay between structured and unstructured UGC among different populations segmented by the mean-variance approach.
Findings
The variety of cognitions displayed by individuals affects the general significant interrelationship between structured and unstructured UGC. Extremely dissatisfied consumers or those with heterogeneous opinions tend to have a closer interconnection, and the interaction between valence and dispersion further strengthens or loosens the relationship. The satisfied or neutral consumers tend to show confounding sentiment signals in relation to the two different UGC. Chinese consumers behave differently from non-Chinese consumers, resulting in a relatively looser interplay.
Practical implications
By identifying consistent opinion providers and promoting more valuable UGC, UGC platforms can raise the quality of information generated. Hotels will then be able to enhance their services through the strategic use of UGC by analyzing reviews with dispersed low-itemized rating and by addressing the differences exhibited by non-Chinese customers. This analytical method can also help to create richly structured sentiment information from unstructured UGC.
Originality/value
This paper investigates the variety of cognitive behaviors in the process when UGC are contributed by online reviewers, focussing on the consistency between structured and unstructured UGC. The study helps researchers understanding emotion recognition and affective computing in social media analytics, which is achieved by exploring the variety of UGC information and its relationship to the contributors’ cognitions. The analytical framework adopted also improves the prior techniques.
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