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Article
Publication date: 28 August 2019

Nick Burton

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…

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1742

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.

Details

International Journal of Sports Marketing and Sponsorship, vol. 20 no. 4
Type: Research Article
ISSN: 1464-6668

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Article
Publication date: 2 September 2019

Shenghua Zhou, S. Thomas Ng, Sang Hoon Lee, Frank J. Xu and Yifan Yang

In the architecture, engineering and construction (AEC) industry, technology developers have difficulties in fully understanding user needs due to the high domain…

Abstract

Purpose

In the architecture, engineering and construction (AEC) industry, technology developers have difficulties in fully understanding user needs due to the high domain knowledge threshold and the lack of effective and efficient methods to minimise information asymmetry between technology developers and AEC users. The paper aims to discuss this issue.

Design/methodology/approach

A synthetic approach combining domain knowledge and text mining techniques is proposed to help capture user needs, which is demonstrated using building information modelling (BIM) apps as a case. The synthetic approach includes the: collection and cleansing of BIM apps’ attribute data and users’ comments; incorporation of domain knowledge into the collected comments; performance of a sentiment analysis to distinguish positive and negative comments; exploration of the relationships between user sentiments and BIM apps’ attributes to unveil user preferences; and establishment of a topic model to identify problems frequently raised by users.

Findings

The results show that those BIM app categories with high user interest but low sentiments or supplies, such as “reality capture”, “interoperability” and “structural simulation and analysis”, should deserve greater efforts and attention from developers. BIM apps with continual updates and of small size are more preferred by users. Problems related to the “support for new Revit”, “import & export” and “external linkage” are most frequently complained by users.

Originality/value

The main contributions of this work include: the innovative application of text mining techniques to identify user needs to drive BIM apps development; and the development of a synthetic approach to orchestrating domain knowledge, text mining techniques (i.e. sentiment analysis and topic modelling) and statistical methods in order to help extract user needs for promoting the success of emerging technologies in the AEC industry.

Details

Engineering, Construction and Architectural Management, vol. 27 no. 2
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 16 April 2018

Alfredo Milani, Niyogi Rajdeep, Nimita Mangal, Rajat Kumar Mudgal and Valentina Franzoni

This paper aims to propose an approach for the analysis of user interest based on tweets, which can be used in the design of user recommendation systems. The extract…

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284

Abstract

Purpose

This paper aims to propose an approach for the analysis of user interest based on tweets, which can be used in the design of user recommendation systems. The extract topics are seen positively by the user.

Design/methodology/approach

The proposed approach is based on the combination of sentiment extraction and classification analysis of tweet to extract the topic of interest. The proposed hybrid method is original. The topic extraction phase uses a method based on semantic distance in the WordNet taxonomy. Sentiment extraction uses NLPcore.

Findings

The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results and confirm the suitability of the approach combining sentiment and categorization for the topic of interest extraction.

Research limitations/implications

The hybrid method combining sentiment extraction and classification for user positive topics represents a novel contribution with many potential applications.

Practical implications

The functionality of positive topic extraction is very useful as a component in the design of a recommender system based on user profiling from Twitter user behaviors.

Social implications

The application of the proposed method in short-text social network can be massive and beyond the applications in tweets.

Originality/value

There are few works that have considered both sentiment analysis and classification to find out users’ interest. The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results.

Details

International Journal of Web Information Systems, vol. 14 no. 1
Type: Research Article
ISSN: 1744-0084

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Article
Publication date: 20 July 2015

Rutilio Rodolfo López Barbosa, Salvador Sánchez-Alonso and Miguel Angel Sicilia-Urban

– The purpose of this paper is to assess the reliability of numerical ratings of hotels calculated by three sentiment analysis algorithms.

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1609

Abstract

Purpose

The purpose of this paper is to assess the reliability of numerical ratings of hotels calculated by three sentiment analysis algorithms.

Design/methodology/approach

More than one million reviews and numerical ratings of hotels in seven cities in four countries were extracted from TripAdvisor web site. Reviews were classified as positive or negative using three sentiment analysis tools. The percentage of positive reviews was used to predict numerical ratings that were then compared with actual ratings.

Findings

All tools classified reviews as positive or negative in a way that correlated positively with numerical ratings. More complex algorithms worked better, yet predicted ratings showed reasonable agreement with actual ratings for most cities. Predictions for hotels were less reliable if based on less than 50-60 percent of available reviews.

Practical implications

These results validate that sentiment analysis can be used to transform unstructured qualitative data on user opinion into quantitative ratings. Current tools may be useful for summarizing opinions of user reviews of products and services on web sites that do not require users to post numerical ratings such as traveler forums. This summarizing may be valuable not just to potential users, but also to the service and product providers and offers validation and benchmarking for future improvement of opinion mining and prediction techniques.

Originality/value

This work assesses the correlation between sentiment analysis of hotels’ reviews and their actual ratings. The authors also evaluated the reliability of results of sentiment analysis calculated by three different algorithms.

Details

Aslib Journal of Information Management, vol. 67 no. 4
Type: Research Article
ISSN: 2050-3806

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Article
Publication date: 18 June 2021

Gowhar Rasool and Anjali Pathania

One of the major challenges within the airline industry is to keep pace with the changing customer perception toward their service quality. This paper aims to demonstrate…

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202

Abstract

Purpose

One of the major challenges within the airline industry is to keep pace with the changing customer perception toward their service quality. This paper aims to demonstrate how sentiment analysis of user-generated big data can be used to research airline service quality as a more comprehensive alternative to other survey-based models by investigating real-time passenger insights.

Design/methodology/approach

The present research uses the case of Indigo airlines by studying passenger's trip advisor reviews regarding the low-cost commercial airline service. The authors analyzed 1,777 passenger reviews, which were classified, to uncover sentiments for five dimensions of airline service quality (AIRQUAL).

Findings

The findings of the study demonstrate the need for harnessing the brand-related user-generated content shared on online platforms to identify the critical attributes for airline service quality. Further, through the application of sentiment analysis, the paper provides much-needed clarity in the processing of user-generated content. It illustrates the investigation of passenger interactions as a reflection of their satisfaction, expectation, intention and overall opinion toward the airline service quality.

Practical implications

The analytical framework adopted in the study for examining user-generated content (UGC) can be functional for the marketing managers and equip them for handling large-scale data readily available in action-oriented interactive marketing research.

Originality/value

This paper demonstrates how sentiment analysis of user-generated data can be used to research airline service quality as a more comprehensive alternative to other survey-based models. The study supplements the methodological advances in the field of UGC analysis and adds to the existing knowledge domain.

Details

Journal of Research in Interactive Marketing, vol. 15 no. 3
Type: Research Article
ISSN: 2040-7122

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Article
Publication date: 31 January 2018

Meena Rambocas and Barney G. Pacheco

The explosion of internet-generated content, coupled with methodologies such as sentiment analysis, present exciting opportunities for marketers to generate market…

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3696

Abstract

Purpose

The explosion of internet-generated content, coupled with methodologies such as sentiment analysis, present exciting opportunities for marketers to generate market intelligence on consumer attitudes and brand opinions. The purpose of this paper is to review the marketing literature on online sentiment analysis and examines the application of sentiment analysis from three main perspectives: the unit of analysis, sampling design and methods used in sentiment detection and statistical analysis.

Design/methodology/approach

The paper reviews the prior literature on the application of online sentiment analysis published in marketing journals over the period 2008-2016.

Findings

The findings highlight the uniqueness of online sentiment analysis in action-oriented marketing research and examine the technical, practical and ethical challenges faced by researchers.

Practical implications

The paper discusses the application of sentiment analysis in marketing research and offers recommendations to address the challenges researchers confront in using this technique.

Originality/value

This study provides academics and practitioners with a comprehensive review of the application of online sentiment analysis within the marketing discipline. The paper focuses attention on the limitations surrounding the utilization of this technique and provides suggestions for mitigating these challenges.

Details

Journal of Research in Interactive Marketing, vol. 12 no. 2
Type: Research Article
ISSN: 2040-7122

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Article
Publication date: 13 May 2019

Susanne Becken, Ali Reza Alaei and Ying Wang

Destination monitoring is crucial to understand performance and identify key points of differentiation. Visitor satisfaction is an essential driver of destination…

Abstract

Purpose

Destination monitoring is crucial to understand performance and identify key points of differentiation. Visitor satisfaction is an essential driver of destination performance. With the fast-growing volume of user-generated content through social media, it is now possible to tap into very large amounts of data provided by travellers as they share their experiences. Analysing these data for consumer sentiment has become attractive for destinations and companies. The idea of drawing on social media sentiment for satisfaction monitoring aligns well with the broader move towards smart destinations and real-time information processing. Thus, this paper aims to examine whether the electronic word of mouth originating from Twitter posts offers a useful source for assessing destination sentiment. Importantly, this research examines what caveats need to be considered when interpreting the findings.

Design/methodology/approach

This research focusses on a prominent tourist destination situated on Australia’s East Coast, the Gold Coast. Using a geographically informed filtering process, a collection of tweets posted from within the Gold Coast destination was created and analysed. Metadata were analysed to assess the population of Twitter users, and sentiment analysis, using the Valence Aware Dictionary for Sentiment Reasoning algorithm, was performed.

Findings

Twitter posts provide considerable information, including about who is visiting and what sentiment visitors and residents express when sending tweets from a destination. They also uncover some challenges, including the “noise” of Twitter data and the fact that users are not representative of the broader population, in particular for international visitors.

Research limitations/implications

This paper highlights limitations such as lack of representativeness of the Twitter data, positive bias and the generic nature of many tweets. Suggestions for how to improve the analysis and value of tweets as a data source are made.

Practical implications

This paper contributes to understanding the value of non-traditional data sources for destination monitoring, in particular by highlighting some of the pitfalls of using information sources, such as Twitter. Further research steps have been identified, especially with a view to improving target-specific sentiment scores and the future employment of big-data approaches that involve integrating multiple data sources for destination performance monitoring.

Social implications

The identification of cost-effective ways of measuring and monitoring guest satisfaction can lead to improvements in destination management. This in turn will enhance customer experience and possibly even resident satisfaction. The social benefits, especially at times of considerable visitation pressure, can be important.

Originality/value

The use of Twitter data for the monitoring of visitor sentiment at tourist destinations is novel, and the analysis presented here provides unique insights into the potential, but also the caveats, of developing new, smart systems for tourism.

研究目的

目的地监控对理解绩效和确立区别关键点至关重要。游客满意是目的地绩效的关键动力。随着社交媒体上用户生成内容的快速增长, 研究其游客提供的大量数据变成可能, 这些数据体现了游客的旅游体验。分析这些消费者情绪的数据对目的地和有关企业的吸引力巨大。研究社交媒介情绪数据和满意度与更广泛地对智慧旅游和实时信息处理等方面的研究和谐一致。因此, 本论文旨在检验Twitter帖子中的在线口碑效应是否成为测量目的地情绪的有用数据。更重要的是, 本论文检验在研究结果中哪些领域应该着重考虑研究。

研究方法

本论文集中研究了澳大利亚东海岸的一处旅游目的地, 黄金海岸。本论文使用地理信息过滤的处理方式, 有关黄金海岸的tweets为样本, 进行分析。本论文分析了元数据, 使用VADER数算, 检测了Twitter用户人口和情绪分析。

研究结果

Twitter帖子提供相当多的信息, 包括谁是游客, 当游客发布有关旅游目的地的tweets的时候, 拥有什么样的情绪。研究结果还指出了一些挑战, 包括twitter数据的“杂音”, 用户并不能代表广大研究对象的事实, 特别是国际游客。

研究理论限制

本论文强调了几点限制, 如Twitter数据的代表性、积极偏见、大多数tweets千篇一律等。本论文对如何提高分析结果和使用tweets作为数据源的价值提出了几点建议。

研究理论意义

本论文对非传统数据以对旅游目的地监控的价值做出贡献, 尤其是强调了使用信息数据的弊端, 如Twitter。未来研究方向应该着重研究目标明确的情绪指数, 以及运用大数据分析方法, 分析多个数据源来检测旅游目的地性能。

研究社会意义

本论文确立的经济有效的方法以衡量和监控游客满意度, 对提高目的地管理有着巨大帮助。同时, 这也可以提高游客体验和甚至提高当地居民的满意度。社会利益, 特别有的时候很大的旅游压力, 是巨大的。

Details

Journal of Hospitality and Tourism Technology, vol. 11 no. 1
Type: Research Article
ISSN: 1757-9880

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Article
Publication date: 14 November 2016

Seunghyun Brian Park, Jichul Jang and Chihyung Michael Ok

The purpose of this paper is to use Twitter analysis to explore diner perceptions of four types of Asian restaurants (Chinese, Japanese, Korean and Thai).

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1386

Abstract

Purpose

The purpose of this paper is to use Twitter analysis to explore diner perceptions of four types of Asian restaurants (Chinese, Japanese, Korean and Thai).

Design/methodology/approach

Using 86,015 tweets referring to Asian restaurants, this research used text mining and sentiment analysis to find meaningful patterns, popular words and emotional states in opinions.

Findings

Twitter users held mingled perceptions of different types of Asian restaurants. Sentiment analysis and ANOVA showed that the average sentiment scores for Chinese restaurants was significantly lower than the other three Asian restaurants. While most positive tweets referred to food quality, many negative tweets suggested problems associated with service quality or food culture.

Research limitations/implications

This research provides a methodology that future researchers can use in applying social media analytics to explore major issues and extract sentiment information from text messages.

Originality/value

Limited research has been conducted applying social media analysis in hospitality research. This study fills a gap by using social media analytics with Twitter data to examine the Twitter users’ thoughts and emotions for four different types of Asian restaurants.

Details

Journal of Hospitality and Tourism Technology, vol. 7 no. 4
Type: Research Article
ISSN: 1757-9880

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Article
Publication date: 30 September 2014

Hamid Khobzi and Babak Teimourpour

The purpose of this study is to assign polarity score to each post from Facebook fan pages, and then examine whether the Comments submitted by users on a post from fan…

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2006

Abstract

Purpose

The purpose of this study is to assign polarity score to each post from Facebook fan pages, and then examine whether the Comments submitted by users on a post from fan page have a significant relationship with the popularity of that post. Being aware of how to enhance the popularity of posts will help companies in terms of administrating their fan pages.

Design/methodology/approach

In the context of fan page and post popularity, the authors test significance of the relationship between Comments’ polarity and number of Likes and Comments of a post in different Facebook pages by regression method. The data are collected from different fan page posts in Facebook, and a sentiment analysis approach is proposed to accomplish this research.

Findings

Results show that the relation between users’ Comments and popularity of fan page posts is strongly significant. Outcomes of this research are useful for every company in terms of monitoring and managing their brand fan pages on social networking sites such as Facebook.

Originality/value

Investigation of factors influencing popularity of fan page posts in social media is almost a new area of study that dates back to recent years. The authors use a sentiment analysis approach to evaluate a new concept describing the relationship between users’ Comments and popularity of posts from Facebook fan pages. Moreover, a part of dataset is extracted from Facebook by a crawler which is an advantage to prior studies.

Details

International Journal of Accounting & Information Management, vol. 22 no. 4
Type: Research Article
ISSN: 1834-7649

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Article
Publication date: 10 October 2016

Marina Bagić Babac and Vedran Podobnik

Due to an immense rise of social media in recent years, the purpose of this paper is to investigate who, how and why participates in creating content at football websites…

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2007

Abstract

Purpose

Due to an immense rise of social media in recent years, the purpose of this paper is to investigate who, how and why participates in creating content at football websites. Specifically, it provides a sentiment analysis of user comments from gender perspective, i.e. how differently men and women write about football. The analysis is based on user comments published on Facebook pages of the top five 2015-2016 Premier League football clubs during the 1st and the 19th week of the season.

Design/methodology/approach

This analysis uses a data collection via social media website and a sentiment analysis of the collected data.

Findings

Results show certain unexpected similarities in social media activities between male and female football fans. A comparison of the user comments from Facebook pages of the top five 2015-2016 Premier League football clubs revealed that men and women similarly express hard emotions such as anger or fear, while there is a significant difference in expressing soft emotions such as joy or sadness.

Originality/value

This paper provides an original insight into qualitative content analysis of male and female comments published at social media websites of the top five Premier League football clubs during the 1st and the 19th week of the 2015-2016 season.

Details

Online Information Review, vol. 40 no. 6
Type: Research Article
ISSN: 1468-4527

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