ABSTRACT
Purpose>This study examines a phygital approach to rural cultural heritage tourism, adopted by a rural community in Sapphaya, Chai Nat Province, Thailand, in response to the Covid-19 crisis. Specifically, it investigates a community's initiatives to amalgamate its physical and digital marketing communications in order to engage with consumers as a strategy for destination recovery and resilience.Design/methodology/approach>This is a qualitative exploratory study involving three stages of action, applying two research approaches: (1) participatory action research (PAR) with Sapphaya's tourism stakeholders, and (2) social media research utilising netnographic analysis of Sapphaya's tourism social enterprise social media pages.Findings>The findings indicate that a phygital rural cultural heritage strategy can facilitate the interconnectivity between a destination's physical and digital dimensions of its cultural heritage tourism product, thereby enhancing its intrinsic value, meaning and experiential perceptions. Specifically, it recommends that a successful community-based phygitalisation strategy requires grassroot engagement across all stages of planning, development, implementation and management of the rural cultural heritage tourism product.Practical Implications>The paper focusses on the cultural heritage tourism strategy adopted by a rural community across the physical-digital-phygital spectrum to augment its sustainable tourism development during a time of crisis. A framework for phygital rural cultural heritage as a strategy for destination resilience and recovery is also proposed.Originality/value>This study adopts a local engagement approach to develop a cooperative community heritage management strategy, based upon local rural capacity building towards digitalisation and empowering innovative partnerships amongst its stakeholders.
ABSTRACT
Distance learning has become a necessary option to the education system for almost every nation because of the COVID-19 outbreak. Due to the closure of educational institutions, which creates obstacles to students' learning in the current COVID 19 pandemic environment, the role of information technology has gained momentum. With the current technology advancements, online learning is made possible for everyone. Although this method appears to be helpful to students and instructors, the effectiveness depended on several factors. For instance, the availability of internet services and the economic capacity of the users may affect the user experience of online learning. The student's reaction is desperately needed to enhance the university's effectiveness and acquire insight into student wants. However, it is difficult to collect and evaluate all the text data on social media, particularly Twitter. In this research, the analysis regarding perception students towards online learning is done through the Natural Language Processing technique which is sentiment analysis. The aim for this study is to determine the terms or keywords used for online learning and identify which machine learning classifiers work best with a large dataset. This research used a Twitter dataset that consisted of 38,602 tweets posted between 23rd July and 14th August 2020. Firstly, pre-processed the data to remove irrelevant tweets. Second, classify the tweet into three classes namely positive, negative, and neutral using rapid miner. Consequently, several machine learning classifiers are trained using different techniques which are Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DCT), and Random Forest (RF). Support Vector Machine classifier with a percentage split 80:20 using VADER Lexicon managed to get the highest accuracy which is approximately 90.41%. Finally, the result of the sentiment analysis has been shown using Power BI data visualization for better understanding. For future work, it would better if this project can provide a real time sentiment using the data from different platforms such as Facebook, Instagram and so on © 2022 IEEE.