Impact of Event Reputation on the Sponsor's Sentiment
11 Oct 2016-pp 18-21
TL;DR: This study aims to examine the impact of event reputation on the sponsoring firm's sentiment on social media and tracked the sentiment of those sponsors to see the effect of sponsorship andevent reputation on sponsor's sentiment.
Abstract: Firms sponsor the events for improving their image and strengthening their presence in market. As a commercial activity, sponsorship is largely used by corporates to reach new markets, increase awareness, and improve their image. Due to extensive use of social media by users and firms, Social media provides good opportunity to measure the effectiveness of sponsorship activities of firms. Sponsor's valence can be taken as measure for assessing the effectiveness of the sponsorship on social media. In this study, we aim to examine the impact of event reputation on the sponsoring firm's sentiment on social media. For achieving this, we selected two events: one with good reputation and second with bad reputation. Using twitter platform, we tracked the sentiment of those sponsors to see the effect of sponsorship and event reputation on sponsor's sentiment.
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Journal Article•
324 citations
TL;DR: Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple media formats, like text, image, video and audio.
Abstract: Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.
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TL;DR: A thorough systematic review was carried out on Social Opinion Mining research, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats.
Abstract: Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing tasks and other aspects derived from the published studies. Such multi-source information fusion plays a fundamental role in mining of people's social opinions from social media platforms. These can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.
11 citations
Cites background from "Impact of Event Reputation on the S..."
...Lastly, 19 further studies –not represented in Figure 4– focused on the following application areas: Human Development [541], Human Mobility [171], Public Facilities [464], Smart Cities [93], Web Publishing [134], Sponsorships [127], Countries [427], Industry [465], Entertainment [465], Refugee/Migrant crisis [329], Tourism [233], Music [125], Cryptocurrency [230], Economy [198], Social Issues [217], Law [321], Insurance/Social Security [57], Geographic Information [447] and Social Interactions [552]....
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...References of studies [46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 47, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84] [85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 47, 99, 100, 101, 102] [103, 104, 105, 106, 107, 108, 109, 110, 111, 112] [113, 114, 115, 116] [117] [118] [119] [46, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139]...
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TL;DR: Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple media formats, like text, image, video and audio as discussed by the authors.
Abstract: Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.
2 citations
TL;DR: In this article , the authors examined the effects of fans' event involvement on event reputation, event commercialization, corporate brand credibility, corporate brands image and purchase intentions of the corporate sponsor brand.
Abstract: PurposeIt is not possible for every fan of a sport to watch matches at stadiums because of the capacity and location constraints. Furthermore, although sport fans could not physically attend sporting events during the COVID-19 pandemic, corporations still showed interest in sponsoring such events. To better understand this phenomenon, this study examined the effects of fans' event involvement on event reputation, event commercialization, corporate brand credibility, corporate brand image and purchase intentions of the corporate sponsor brand.Design/methodology/approachA total of 646 responses were collected from fans of Indian Premier League teams. Confirmatory factor analysis and covariance-based structural equation modelling analyses were conducted on the collected data.FindingsResults showed that fans' involvement in televised sporting events had a positive influence on the events' reputation, which, in turn, had a significant impact on their corporate brand credibility and image. Furthermore, the corporate brand credibility and image had a positive impact on the fans' purchasing decisions.Originality/valueThis study provides valuable implications for marketing managers aiming to enhance their understanding of the impact of event sponsorship on corporate brands. In addition, the findings provide insight into how to support the development of effective sponsorship strategies in the future. The results suggest that sponsoring companies should consider maintaining the credibility and image of their brands to achieve the desired outcomes from sponsoring such sporting events.
References
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"Impact of Event Reputation on the S..." refers background in this paper
...An entity’s Image refers to set of associations user holds about the entity [7]....
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Proceedings Article•
30 Jul 1999TL;DR: This work proposes a widely applicable generalization of maximum likelihood model fitting by tempered EM, based on a mixture decomposition derived from a latent class model which results in a more principled approach which has a solid foundation in statistics.
Abstract: Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.
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"Impact of Event Reputation on the S..." refers methods in this paper
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TL;DR: Probabilistic Latent Semantic Analysis (PLSA) as mentioned in this paper is a statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text and in related areas.
Abstract: Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.
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