Monitoring the environment and human sentiment on the Great Barrier Reef: Assessing the potential of collective sensing

Abstract

With the growth of smartphone usage the number of social media posts has significantly increased and represents potentially valuable information for management, including of natural resources and the environment. Already, evidence of using 'human sensor' in crises management suggests that collective knowledge could be used to complement traditional monitoring. This research uses Twitter data posted from the Great Barrier Reef region, Australia, to assess whether the extent and type of data could be used to Great Barrier Reef organisations as part of their monitoring program. The analysis reveals that large amounts of tweets, covering the geographic area of interest, are available and that the pool of information providers is greatly enhanced by the large number of tourists to this region. A keyword and sentiment analysis demonstrates the usefulness of the Twitter data, but also highlights that the actual number of Reef-related tweets is comparatively small and lacks specificity. Suggestions for further steps towards the development of an integrative data platform that incorporates social media are provided.

Sentiment analysis as a measure of conservation culture in scientific literature

“…Understanding attitudes is critical to effective conservation (Becken et al. ; Davies et al. ; Fidino et al.…”

Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions

“…In terms of methodology, dictionary matching method and machine learning methods have been applied equally frequently. The dictionaries used mainly includes English and Chinese dictionaries, such as WordNet, SentiWordNet, and HowNet [15,20,35,36], and some dictionaries used in studies, such as the Valence Aware Dictionary for Sentiment Reasoning (VADER), even include a tourism vocabulary [21,31]. Among the aforementioned three types of machine learning methods, the support vector machine and the naïve Bayesian methods have been widely used in tourism research due to their fast processing speeds [18].…”

“…The purpose of a sentiment analysis is to extract the emotional orientation of a text toward a certain thing based on the words in the unstructured text [13]. Currently, sentiment analysis has allowed for many achievements in tourism studies [14][15][16][17][18][19][20], and existing UGC (user-generated content) data can effectively support sentiment analysis to understand tourists' perception of the overall environmental perception of tourism destinations [21]; however, whether the existing data can help understand tourists' perceptions of air quality at the level of environmental components through sentiment analysis is still a proposition worth examining.…”

Lexicon based Chinese language sentiment analysis method

“…A total of 24,308 relevant posts have been captured. As a demonstration of sentiment calculation we relied on our previous experience on Great Barrier Reef project [4], and selected several relevant keywords including key locations (Townswille, Cairns, etc), food (Seafood), hotel and some relevant activities such as 'snorkeling' and calculated overall sentiment for these keywords. In Table 1 we provide some sentiment scores calculated by method proposed in this work.…”

“…Social media posts have been harnessed for different purposes including monitoring environmental changes [4], [5] as well as sentiment analysis in tourism [19], [22], [37]. In most cases concept relied on sentiment derived from short text of social media posts and analytics of posts' meta data along other available online or scientific data.…”