Online investigation of users’ attitudes using automatic question answering

Zhang, Chengzhi; Zhou, Qingqing

doi:10.1108/oir-10-2016-0299

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…The results on a range of experiments illustrate that the approach significantly outperforms several alternative methods for polarity shift detection and elimination. Also, inspired by the traditional methods like questionnaire, observation, and face-to-face interviews, Zhang and Zhou (2018) conducted an online investigation via automatic question answering. The researchers generate questions based on question templates, and extract corresponding answers using sentiment lexicon-based aspect-level sentiment analysis.…”

Section: Research Papermentioning

confidence: 99%

Sentiment Analysis of Japanese Tourism Online Reviews

Zhu

Feng

et al. 2019

Journal of Data and Information Science

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Purpose: Online reviews on tourism attractions provide important references for potential tourists to choose tourism spots. The main goal of this study is conducting sentiment analysis to facilitate users comprehending the large scale of the reviews, based on the comments about Chinese attractions from Japanese tourism website 4Travel.Design/methodology/approach: Different statistics-and rule-based methods are used to analyze the sentiment of the reviews. Three groups of novel statistics-based methods combining feature selection functions and the traditional term frequency-inverse document frequency (TF-IDF) method are proposed. We also make seven groups of different rulesbased methods. The macro-average and micro-average values for the best classification results of the methods are calculated respectively and the performance of the methods are shown. Findings:We compare the statistics-based and rule-based methods separately and compare the overall performance of the two method. According to the results, it is concluded that the combination of feature selection functions and weightings can strongly improve the overall performance. The emotional vocabulary in the field of tourism (EVT), kaomojis, negative and transitional words can notably improve the performance in all of three categories. The rule-based methods outperform the statistics-based ones with a narrow advantage.Research limitation: Two limitations can be addressed: 1) the empirical studies to verify the validity of the proposed methods are only conducted on Japanese languages; and 2) the deep learning technology is not been incorporated in the methods. Practical implications:The results help to elucidate the intrinsic characteristics of the Japanese language and the influence on sentiment analysis. These findings also provide practical usage guidelines within the field of sentiment analysis of Japanese online tourism reviews.

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Section: Research Papermentioning

confidence: 99%

Sentiment Analysis of Japanese Tourism Online Reviews

Zhu

Feng

et al. 2019

Journal of Data and Information Science

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“…Their experiment verified that users' published content and following relations can indicate their online consumption preferences. Zhang and Zhou (2018) mined online users' attitudes from a huge pool of tourism reviews via automatic question answering, and compared results with the traditional questionnaire. The high consistency showed online information can be used to identify users' attitudes.…”

Section: Online User Surveys Via Social Mediamentioning

confidence: 99%

Using social media to explore regional cuisine preferences in China

Zhang

Yue

Zhou

et al. 2019

OIR

Self Cite

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Purpose Food plays an important role in every culture around the world. Recently, cuisine preference analysis has become a popular research topic. However, most of these studies are conducted through questionnaires and interviews, which are highly limited by the time, cost and scope of data collection, especially when facing large-scale survey studies. Some researchers have, therefore, attempted to mine cuisine preferences based on online recipes, while this approach cannot reveal food preference from people’s perspective. Today, people are sharing what they eat on social media platforms by posting reviews about the meal, reciting the names of appetizers or entrees, and photographing as well. Such large amount of user-generated contents (UGC) has potential to indicate people’s preferences over different cuisines. Accordingly, the purpose of this paper is to explore Chinese cuisine preferences among online users of social media. Design/methodology/approach Based on both UGC and online recipes, the authors first investigated the cuisine preference distribution in different regions. Then, dish preference similarity between regions was calculated and few geographic factors were identified, which might lead to such regional similarity appeared in our study. By applying hierarchical clustering, the authors clustered regions based on dish preference and ingredient usage separately. Findings Experimental results show that, among 20 types of traditional Chinese cuisines, Sichuan cuisine is most favored across all regions in China. Geographical proximity is the more closely related to differences of regional dish preference than climate proximity. Originality/value Different from traditional definitions of regions to which cuisine belong, the authors found new association between region and cuisine based on dish preference from social media and ingredient usage of dishes. Using social media may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

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“…This research shows that users' performance has a significant impact on usage. Further, the results vary according to the levels of experience of the professionals; and (5) users' performance toward users' satisfaction by Chan et al (2015), Ali and Kaur (2018), Park and Pobil (2013), Zhang and Zhou (2018) and Lin (2016).…”

Section: Introductionmentioning

confidence: 99%

The technology tasks fit, its impact on the use of information system, performance and users’ satisfaction

Ratna

Utami

Astuti

et al. 2020

VJIKMS

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Purpose Find out how the employees’ performance on the implementation of the hotel reservation information system. Therefore, this paper aims to examine the effect of the task-technology fit (TTF) on the use of information systems, as well as its effect on user performance and satisfaction. Design/methodology/approach This research type is explanatory research. In explanatory research, the aim is to provide an explanation related to the causal relationship between variables and hypothesis testing. The unit of analysis adopted in this study is the individual of the front office employees who use the star hotel reservation information system in South Kalimantan Province (the population is 239, and the taken are 150 samples, based on the number of indicators multiplied 5). Findings The higher the TTF, the higher the level of using information systems. The higher the use level of information systems, the higher the information systems user performance and vice versa in which the higher the user’s performance, the more increase the use of information systems. On the other hand, this study found that the use of information systems and user performance has no significant effect on user satisfaction. Originality/value The novelty in this study is shown in the influence between performance variables on the usage and the usage variables on the users’ performance. This study examines the importance of reciprocal usage and user performance relationships based on previous research studies that examine the relationship and that information technology (IT) usage will affect user performance. In addition, the users’ performance will affect the users’ behavior in using IT.

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Online investigation of users’ attitudes using automatic question answering

Cited by 10 publications

References 32 publications

Sentiment Analysis of Japanese Tourism Online Reviews

Sentiment Analysis of Japanese Tourism Online Reviews

Using social media to explore regional cuisine preferences in China

The technology tasks fit, its impact on the use of information system, performance and users’ satisfaction

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