Abstract
PurposeTwitter is the most widely used platform with an open network; hence, tourists often resort to Twitter to share their travel experiences, satisfaction/dissatisfaction and other opinions. This study is divided into two sections, first to provide a framework for understanding public sentiments through Twitter for tourism insights, second to provide real-time insights of three Indian heritage sites i.e., the Taj Mahal, Red Fort and Golden Temple by extracting 5,000 tweets each (n = 15,000) using Twitter API. Results are interpreted using NRC emotion lexicon and data visualisation using R.Design/methodology/approachThis study attempts to understand the public sentiment on three globally acclaimed Indian heritage sites, i.e. the Taj Mahal, Red Fort and Golden temple using a step-by-step approach, hence proposing a framework using Twitter analytics. Extensive use of various packages of R programming from the libraries has been done for various purposes such as extraction, processing and analysing the data from Twitter. A total of 15,000 tweets from January 2015 to January 2021 were collected of the three sites using different key words. An exploratory design and data visualisation technique has been used to interpret results.FindingsAfter data processing, 12,409 sentiments are extracted. Amongst the three tourists' spots, the greatest number of positive sentiments is for the Taj Mahal and Golden temple with approximately 25% each. While the most negative sentiment can be seen for the Red Fort (17%). Amongst the positive emotions, the maximum joy sentiment (12%) can be seen in the Golden Temple and trust (21%) in the Red Fort. In terms of negative emotions, fear (13%) can be seen in the Red fort. Overall, India's heritage sites have a positive sentiment (20%), which surpasses the negative sentiment (13%). And can be said that the overall polarity is towards positive.Originality/valueThis study provides a framework on how to use Twitter for tourism insights through text mining public sentiments and provides real- time insights from famous Indian heritage sites.
Highlights
Social media has transformed the tourism and hospitality sector (Leung et al, 2013) by influencing the social life of its users (Zeng and Gerritsen, 2014), of tourists with decision-making and information search (Power and Phillips-Wren, 2011) and sharing of experiences pre- and post-travel (Zeng and Gerritsen, 2014)
The social media platform is a hub of enormous data relating to tourist destinations, hotels, restaurants, leisure, services etc
Even in the Asia Pacific region, it has been contributing at a growth rate of 10% (Runcle and Associates, Inc., 2015), and the Asian tourism sector has been anticipated to grow at the “quickest rate” of more than 6% per annum (Fuller, 2013)
Summary
Social media has transformed the tourism and hospitality sector (Leung et al, 2013) by influencing the social life of its users (Zeng and Gerritsen, 2014), of tourists with decision-making and information search (Power and Phillips-Wren, 2011) and sharing of experiences pre- and post-travel (Zeng and Gerritsen, 2014). This study attempts to understand the public sentiment on three globally acclaimed Indian heritage sites, i.e. the Taj Mahal, Red Fort and Golden temple, using a step-by-step approach, proposing a framework using Twitter analytics. We first propose a framework on how to use Twitter analytics using R programming language for tourism insights and take an experiment of three globally acclaimed Indian heritage sites, i.e. the Taj Mahal, Red Fort and Golden temple for understanding real-time sentiments of these heritage sites. It becomes imperative to remove white spaces, punctuations and other signs (like “@” “/” “:” “##”) and stop words (like “is”, “the,” etc.) included in the tweets as they provide hindrance in understanding the underlying sentiment of the tweet For this purpose, “tm” and “gsub” functions are used to perform text mining and cleaning the data, respectively. Scores of: anger, fear, anticipation, trust, surprise, sadness, joy, and disgust
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