Opinion Ensembling for Improving Economic Growth through Tourism

Puri, Charu; Akhil,; Gaurav, .; Kush,; Kumar, Naveen

doi:10.1016/j.procs.2017.11.366

Cited by 10 publications

(7 citation statements)

References 5 publications

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“…Therefore, the businesses' marketers ought to monitor online conversations. They may use crawlers to track eWOM publicity in different websites including social media (Puri & Kumar, 2017).…”

Section: Research Implicationsmentioning

confidence: 99%

A Taxonomy of Online Marketing Methods

Camilleri

2020

SSRN Journal

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This chapter presents a systematic review of over thirty (30) types of online marketing methods. It describes different methods like email marketing, social network marketing, in-game marketing and augmented reality marketing, among other approaches. The researchers discuss that the rationale for using these online marketing strategies is to increase brand awareness, customer centric marketing and consumer loyalty. They shed light on various personalization methods including recommendation systems and user generated content in their taxonomy of online marketing terms. Hence, they explain how these online marketing methods are related to each other. The researchers contend that the boundaries between online marketing methods have not been clarified enough within the academic literature. Therefore, this chapter provides a better understanding of different online marketing methods. A review of the literature suggests that the 'oldest' online marketing methods including the email and the websites are still very relevant for today's corporate communication. In conclusion, the researchers put forward their recommendations for future research about contemporary online marketing methods.

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Section: Research Implicationsmentioning

confidence: 99%

A Taxonomy of Online Marketing Methods

Camilleri

2020

SSRN Journal

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“…Analyses based on a numerical rating system cannot uncover nuanced opinions that are often expressed in textual content. Only text reviews provide the understanding of expressed consumers' opinions (Puri, 2017). In strict numerical ratings they are generally lost.…”

Section: Approaches To Sentiment Analysismentioning

confidence: 99%

Contemporary Data Analysis Techniques for Online Reputation Management in Hospitality and Tourism

Grljević

Bošnjak

2019

FU Econ Org

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Knowing what attracts or deters tourists to/from a tourist visit and what products to offer them and to pay special attention to is crucial for good economic results. Such knowledge can be obtained by analysis of online comments and reviews that tourists leave on travel websites (such as Booking, TripAdvisor, Trivago, etc.). This paper describes the value which information about opinions and emotions hidden in online reviews has for managers who receive it, especially the knowledge of (dis)satisfaction of users with certain aspects of the tourist offer. Uncovered knowledge from online reviews provides a chance to take advantage of the strong points, and correct the shortcomings through timely corrective measures and actions. Contemporary approaches and methods of analyzing online reviews and the opportunities for development they provide in the tourism industry are described through a case study conducted over a subset of 20491 hotel reviews from TripAdvisor. We have conducted sentiment analysis of reviews with the goal of building an automated model which will successfully distinguish positive from negative reviews. Logistic Regression classifier has the best performance, in 90% of reviews it has correctly classified positive reviews and in 83% negative. We have illustrated how association rules can help management to uncover relationships between concepts under discussion in negative and positive reviews.

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“…Analyses based on numerical rating system are not sufficient. Only analysis of textual content uncovers nuanced opinions that are generally lost in crude numerical ratings and provides the understanding of consumers opining expressed in reviews [5], while numerical ratings can be used as sentiment indicators [11] - [14].…”

Section: Related Workmentioning

confidence: 99%

“…The dataset we have used for our research comprises of 20491 reviews of hotels collected from TripAdvisor website 5 [42]. Besides the textual reviews, the dataset contains ratings on one-tofive scale that the authors of the reviews provide to reflect their overall satisfaction.…”

Section: Mixedmentioning

confidence: 99%

“…The amount of travel data grows every day as people continuously add reviews to the sheer amount of existing data. One illustrative example of the vast amount of travel data is given in [5]. The authors reported on more than 60 million members and over 170 million reviews and opinions on TripAdvisor, one of the largest and most visited travel and tourism websites.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Customer Satisfaction Through Online Reviews and Ratings

Grljević¹,

Bošnjak²

2018

3rd International Thematic Monograph - Thematic Proceedings: Modern Management Tools and Economy of Tourism Sector in Present

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Travelers nowadays express their opinions, feelings, or (dis)satisfaction on the Web through reviews and ratings of hotels, restaurants, or other travel-related entities and services. The paper proposes a novel approach to determining the sentiment orientation of reviews with attached average numerical ratings, which usually convey both positive and negative sentiment. It is shown that the analysis and comparison of subsets of reviews with the same attached mark in terms of writing style and vocabulary can significantly reduce the size of reviews with biased sentiment polarity.

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Opinion Ensembling for Improving Economic Growth through Tourism

Cited by 10 publications

References 5 publications

A Taxonomy of Online Marketing Methods

A Taxonomy of Online Marketing Methods

Contemporary Data Analysis Techniques for Online Reputation Management in Hospitality and Tourism

Evaluating Customer Satisfaction Through Online Reviews and Ratings

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