<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The novel coronavirus pandemic drastically impacted economies in 2020, including travel and tourism. The nationwide lockdown to curb infection has led people to use social media such as Twitter to express their opinions and share information on several issues. This paper focuses on sentiment analysis using Indian tourist Tweets during Covid-19 using Python and the maximum likelihood method to determine the parameters. The sentiment analysis yielded valuable insights into which sites will revive quickly; the analysis of the Tweets using sentiment analysis will help in predicting the revival of the tourism sector after the pandemic. The various algorithm scores will help in predicting the best ways to enhance the customer experience at various sites in the tourism sector. Further, the study will enable the tourism sector to design a revival strategy in the Covid-19 pandemic.</p>
</abstract>

The novel coronavirus pandemic drastically impacted economies in 2020, including travel and tourism. The nationwide lockdown to curb infection has led people to use social media such as Twitter to express their opinions and share information on several issues. This paper focuses on sentiment analysis using Indian tourist Tweets during Covid-19 using Python and the maximum likelihood method to determine the parameters. The sentiment analysis yielded valuable insights into which sites will revive quickly; the analysis of the Tweets using sentiment analysis will help in predicting the revival of the tourism sector after the pandemic. The various algorithm scores will help in predicting the best ways to enhance the customer experience at various sites in the tourism sector. Further, the study will enable the tourism sector to design a revival strategy in the Covid-19 pandemic.

Twitter sentiment analysis: An estimation of the trends in tourism after the outbreak of the Covid-19 pandemic

European Journal of Tourism, Hospitality and Recreation

This work is licensed under the Creative Commons Attribution 4.0 International License.

The novel coronavirus pandemic drastically impacted economies in 2020, including travel and tourism. The nationwide lockdown to curb infection has led people...

Keywords	sentiment	likes	re-tweet	Performance
Trekking	0.52	0.06	97.71	High
Beaches	0.2	0.04	325.68	Moderate
Travel	0.34	0.01	439.42	Moderate
Wanderlust	0.24	0.81	1521.9	Moderate
Holiday	0.3	0.15	1455.17	Moderate
Travelgram	0.55	0.5	16.04	High
Nature	0.58	0	1238.4	High
Insta travel	0.44	0.59	5.83	Moderate
Travel life	0.43	1.43	9.33	Moderate
Travel diaries	0.44	0.58	6.78	Moderate
Mountains	0.38	0.04	3471.22	Moderate
Cruise	0.25	0.12	822.25	Moderate
Birding	.32	0.99	66.11	Moderate
Museums	0.51	0.03	6895.4	High
Memorials	0.27	0.85	965.5	Moderate
Nostalgia tourism	0.3636	2.367	3.41	Low
‘Spiritual’ tourism	0.12	0.12	5310.9	Low (many religious places are closed till date)
Adventure Tour	0.54	1.26	77.6	High

Twitter sentiment analysis: An estimation of the trends in tourism after the outbreak of the Covid-19 pandemic

Article Category: Research Article

Pubblicato online: 18 nov 2023

Pagine: 40 - 48

Ricevuto: 06 giu 2021

Accettato: 16 gen 2023

DOI: https://doi.org/10.2478/ejthr-2023-0004

Parole chiave
Tourism, sentiment analysis, Covid-19 pandemic, logistic regression, Python, revival strategy

© 2023 Garima Malik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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Sentiment Analysis

Twitter sentiment analysis: An estimation of the trends in tourism after the outbreak of the Covid-19 pandemic

Article Category: Research Article

Pubblicato online: 18 nov 2023

Pagine: 40 - 48

Ricevuto: 06 giu 2021

Accettato: 16 gen 2023

DOI: https://doi.org/10.2478/ejthr-2023-0004

Parole chiaveTourism, sentiment analysis, Covid-19 pandemic, logistic regression, Python, revival strategy

© 2023 Garima Malik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Sentiment Analysis

Parole chiave
Tourism, sentiment analysis, Covid-19 pandemic, logistic regression, Python, revival strategy