Sentiment annotations for reviews: an information quality perspective

Abstract

Purpose The purpose of this paper is to propose sentiment annotation at sentence level to reduce information overloading while reading product/service reviews in the internet. Design/methodology/approach The keyword-based sentiment analysis is applied for highlighting review sentences. An experiment is conducted for demonstrating its effectiveness. Findings A prototype is built for highlighting tourism review sentences in Chinese with positive or negative sentiment polarity. An experiment results indicates that sentiment annotation can increase information quality and user’s intention to read tourism reviews. Research limitations/implications This study has made two major contributions: proposing the approach of adding sentiment annotation at sentence level of review texts for assisting decision-making; validating the relationships among the information quality constructs. However, in this study, sentiment analysis was conducted on a limited corpus; future research may try a larger corpus. Besides, the annotation system was built on the tourism data. Future studies might try to apply to other areas. Practical implications If the proposed annotation systems become popular, both tourists and attraction providers would obtain benefits. In this era of smart tourism, tourists could browse through the huge amount of internet information more quickly. Attraction providers could understand what are the strengths and weaknesses of their facilities more easily. The application of this sentiment analysis is possible for other languages, especially for non-spaced languages. Originality/value Facing large amounts of data, past researchers were engaged in automatically constructing a compact yet meaningful abstraction of the texts. However, users have different positions and purposes. This study proposes an alternative approach to add sentiment annotation at sentence level for assisting users.

OpinionML—Opinion Markup Language for Sentiment Representation

“…In this product review, the author hardly gave a direct opinion of the product but it is the sarcastic language which is indicating his true thoughts about the product (apparently negative). If a human has to judge then it is bit easier to evaluate the polarity of such reviews but the process becomes more complex when computers are to judge such reviews [20]. Turney and Littman [21] highlight the importance of context when processing sarcastic or ironic text for opinion mining.…”

Online Information Review Research and Its Future Direction: A Literature Review

“…There could be further studies into this phenomenon to have a mix between reviews and actual purchase-experience. The annotation design in Yang and Chao (2018) provides a summary for the review sentences using specific keywords. This keyword-based sentiment analysis, although reduces the volume of reviews, could have its flaws in the form of wrong interpretation or incomplete feedback.…”

“…When consumers are aware of the three types of manipulation tactics (perceived deceptiveness, ease of detection, unethicality) their perceived deceptiveness and unethicality tend to be different. Yang and Chao, 2018 Experiment using keywordbased sentiment analysis Sentiment annotation can increase information quality and user's intention to read tourism reviews. Sher and Lee, 2009 Online experiment with 278 undergraduates…”

“…Brody and Elhadad (2010) designed a system for extracting aspects and determining sentiment in review text which have been tested and proven to be more representative than manually derived ones. Yang and Chao (2018) designed keyword-based sentiment analysis using annotations. This approach increases the information quality of the original review texts.…”

“…The extant literature has recently been finding solutions to some notable challenges faced by managers of online reviews namely: (i) usefulness of online reviews (Liu and Park, 2015;Marinkovic and Kalinic, 2017;Sebastianelli and Tamimi, 2018;Zhang and Lin, 2018), (ii) quality and reliability of consumer reviews as against the increasing quantities (Yagci and Das, 2018;Yang and Chao, 2018;Chakraborty, 2019), (iii) review-based ranking of reviews (Chen et al, 2017;Saumya et al, 2018;Lee et al, 2019), (iv) user recommendation (Xu and Yao, 2015;Luan et al, 2018) and (v) the valence or effectiveness of the reviews (Baker and Kim, 2019;Song et al, 2020;Hong et al, 2020). Varied research methodologies have been applied in these studies such as statistical experiments (Kwok and Xie, 2016;Zhang and Lin, 2018), evaluation and categorization of online reviews (Chen, 2016), surveys (Lee et al, 2017;Liu et al, 2018), modeling (Pan et al, 2018) and designed theoretical frameworks (Banerjee and Chua, 2014).…”