Novel Sentiment Lexica Derived from User Generating Content by Chinese Tourists in Pacific Islands
Abstract
:1. Introduction
2. Literature Review
2.1. Online Reviews in Tourism Research
2.2. Cognitive Appraisal Theory
2.3. Sentiment Analysis in Tourism Research
2.4. Key Outtakes and Research Questions
- What are the Chinese tourist’s sentiments towards the PICTs?
- Do the Chinese tourists’ sentiments differ among the PICTs, and if yes, how?
- What factors influence Chinese tourists’ sentiments toward the PICTs?
- Do the influential factors differ among the PICTs, and if yes, how?
3. Method
3.1. Chinese Tourists and the Pacific Island Countries and Territories
3.2. Data Collection: Sample Selection
3.3. Data Collection: Sources
3.4. Data Processing
3.4.1. Text Cleaning
- Text = re.sub(r1, ‘’, file)
- text = text.replace(‘ ‘,’’)
- text = text.replace(‘posted on’, ‘’)
- tmp_str = “”.join(text.split())
- result = ‘ ‘.join(tmp_str.split())
- text = re.sub(“[{}]+”.format(punctuation), “”, text)
3.4.2. Word Segmentation
3.4.3. Removal of Stopwords
3.4.4. Word Cloud
3.5. Data Analysis Methods
3.6. Limitation of Data and Method
4. Results and Discussion
4.1. Sentiments Frequency
4.2. Sentiments Fluctuation
4.3. Sentiment Lexicon Construction
4.4. Pre-Testing
4.5. Sentiment Analysis
4.6. Sentiment Tendency
4.7. Word Frequency
5. Research Conclusions
5.1. Managerial Implications
5.2. Limitations and Future Research
Author Contributions
Funding
Conflicts of Interest
Appendix A
References
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Step | Aim | Measures |
---|---|---|
| To select appropriate PICTs | Based on available tourism data |
| To obtain online review text | Through Python crawler |
| To make text data suitable for analysis | (a) text cleaning (b) word segmentation (c) stopwords removal (d) word cloud generation |
| To construct a sentiment lexicon and calculate sentiment values. | (a) sentiment lexicon construction (b) semantic rules setting (c) sentiment analysis based on Python SnowNLP, sentiment lexicon, and word frequency |
Types | Examples |
---|---|
Locations | South Pacific, Melanesia |
Attractions | Port Moresby, Denarau, Santo Island |
Culture | Koroways, Fijian dollar, tattoo |
Animals and plants | Palm tree, Kava, Manta, Bull shark |
Index | Hownet Words (Chinese Sentiment Lexicon) | New Words | Degree | N | Weight |
---|---|---|---|---|---|
Mostdict | Most (最), Extreme (极端), Enough (充分), Completely (彻底) | Hundredfold (百倍), Super (爆表) | Most (最) | 75 | 2.5 |
Verydict | Exceptionally (特别), Really (何止), Indeed (实在) | Remarkably (非凡) Fully (满满, 连连, 十分) Very (敲) | Very (很) | 77 | 2.0 |
Moredict | More (较为, 不少) | Rather (颇) | More (较) | 42 | 1.5 |
Ishdict | A little (一点儿), Slightly (些微), A bit (蛮) | Roughly (差不多), Almost (几乎) | Little (稍微) | 46 | 0.5 |
Sentiment Valence | Examples of Basic Sentiment Lexicon Words | Examples of Added Sentiment Lexicon Words | Number |
---|---|---|---|
Positive | like, enjoy, immerse | luscious, dazzled, mysterious | 11,045 |
Negative | grieved, disappointed, gloomy | temperamental, tearless | 8496 |
Sentiment | Tourism Context | Score | Non-Tourism Context | Score |
---|---|---|---|---|
positive | On the slow-paced island, there is a sense of leisure and tranquility everywhere. | 1 | The weather is so good today. I’m very happy! | 1 |
negative | Try not to act alone; the public security environment is very poor. | −1 | You are a big badass. | −1 |
neutral | Visa can be applied for online. | 0 | He is a physicist at Stanford University. | 0 |
Country or Territory | Sentiment Tendency | Sentiment | N | P |
---|---|---|---|---|
Papua New Guinea | Positive | Positive (1) | 824 | 77.7% |
Negative (−1) | 20 | 1.9% | ||
Neutral (0) | 217 | 20.4% | ||
Cook Islands | Positive | Positive (1) | 269 | 86.8% |
Negative (−1) | 3 | 1.0% | ||
Neutral (0) | 38 | 12.2% | ||
Fiji | Positive | Positive (1) | 3407 | 97.1% |
Negative (−1) | 16 | 0.5% | ||
Neutral (0) | 85 | 2.4% | ||
Tahiti | Positive | Positive (1) | 617 | 97.9% |
Negative (−1) | 0 | 0 | ||
Neutral (0) | 13 | 2.1% | ||
Tonga | Positive | Positive (1) | 146 | 84.9% |
Negative (−1) | 0 | 0 | ||
Neutral (0) | 26 | 15.1% | ||
Vanuatu | Positive | Positive (1) | 302 | 97.8% |
Negative (−1) | 2 | 0.6% | ||
Neutral (0) | 5 | 1.6% | ||
Samoa | Positive | Positive (1) | 216 | 93.1% |
Negative (−1) | 6 | 2.6% | ||
Neutral (0) | 10 | 4.3% | ||
Seven research areas | Positive | Positive (1) | 5781 | 93.0% |
Negative (−1) | 47 | 0.7% | ||
Neutral (0) | 394 | 6.3% |
Sentiment Tendency | N | P (%) |
---|---|---|
Positive | 2603 | 51.7% |
Neutral | 1883 | 37.4% |
Negative | 550 | 10.9% |
Total | 5036 | 100% |
Vanuatu | Samoa | ||||||
---|---|---|---|---|---|---|---|
Words | Frequency | Words | Frequency | Words | Frequency | Words | Frequency |
Vanuatu | 410 | postcard | 41 | Samoa | 270 | island | 18 |
volcano | 248 | shoot | 40 | Visa | 120 | air ticket | 18 |
hotel | 223 | Australia | 38 | airport | 61 | downtown | 17 |
Port Vila | 219 | volcanic vent | 36 | China | 56 | harbour | 17 |
Fiji | 185 | Beijing | 35 | passport | 47 | volcano | 16 |
local | 149 | transfer | 33 | beach | 43 | port | 16 |
Underwater Post Office | 103 | New Zealand | 33 | hotel | 37 | sea-island | 16 |
airport | 101 | inbound | 32 | Sand-beach | 36 | cruise | 16 |
country | 95 | eager | 32 | Apia | 35 | guide | 16 |
plane | 86 | coconut | 32 | New Zealand | 33 | culture | 15 |
photograph | 80 | global | 31 | Fiji | 32 | ticket | 15 |
flight | 79 | sand-beach | 31 | Visa-free | 30 | wind tunnel | 14 |
sea-island | 74 | aviation | 31 | church | 29 | wharf | 15 |
seawater | 64 | dive | 31 | locals | 27 | bus | 15 |
China | 64 | holiday | 30 | inbound | 27 | people | 15 |
Pacific island | 63 | sea | 30 | plane | 27 | Pacific | 14 |
happiness | 61 | beautiful | 29 | on-island | 27 | fruit | 14 |
capital | 61 | Pacific | 29 | waterfall | 25 | swim | 14 |
Hong Kong | 60 | lobster | 29 | island-country | 25 | turtle | 13 |
phone | 57 | island-country | 28 | dollar | 23 | car-rent | 13 |
isles | 55 | free-of-charge | 28 | USA | 22 | trench | 13 |
tribe | 53 | wharf | 28 | seawater | 22 | building | 13 |
erupt | 51 | simple | 28 | free-of-charge | 22 | fee | 13 |
beach | 49 | smile | 26 | seaside | 21 | supermarket | 13 |
honour | 48 | package | 26 | cave | 20 | travel | 13 |
friend | 47 | room | 26 | market | 20 | sunset | 13 |
island | 47 | traffic | 26 | roundabout | 20 | breakfast | 13 |
original | 45 | magma | 25 | friend | 19 | resort | 13 |
easy | 43 | green | 24 | apply | 19 | tropics | 13 |
locals | 43 | special | 24 | expiry-date | 18 | coconut | 13 |
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Zhang, Y.; Song, J.; Sciacca, A.; Chan, J.; Qi, X. Novel Sentiment Lexica Derived from User Generating Content by Chinese Tourists in Pacific Islands. Sustainability 2022, 14, 15833. https://doi.org/10.3390/su142315833
Zhang Y, Song J, Sciacca A, Chan J, Qi X. Novel Sentiment Lexica Derived from User Generating Content by Chinese Tourists in Pacific Islands. Sustainability. 2022; 14(23):15833. https://doi.org/10.3390/su142315833
Chicago/Turabian StyleZhang, Ying, Jiehang Song, Angelo Sciacca, Jin Chan, and Xiaoguang Qi. 2022. "Novel Sentiment Lexica Derived from User Generating Content by Chinese Tourists in Pacific Islands" Sustainability 14, no. 23: 15833. https://doi.org/10.3390/su142315833