Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions
Abstract
:1. Introduction
2. Literature Review
2.1. Tourist’ Perception of Air Quality
2.2. Sentiment Analysis in Tourism
3. Research Methods and Data Sources
3.1. Research Methods
3.2. Data Source
4. Result and Analysis
4.1. Analysis of the Number of Comments
4.2. Content Analysis
4.3. Sentiment Analysis
4.3.1. ROST Sentiment Analysis
4.3.2. ANN Sentiment Analysis
5. Conclusions and Discussion
5.1. Conclusions
5.2. Discussion
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Predicted | ||||
---|---|---|---|---|
Positive | Neutral | Negative | ||
Original | Positive | a | b | c |
Neutral | d | e | f | |
Negative | g | h | i | |
(1) | ||||
(2) |
High-Frequency Word | OB | GO | RO | BO | High-Frequency Word | OB | GO | RO | BO | High-Frequency Word | OB | GO | RO | BO |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Air | 329 | 2 | 2 | 2 | Environment | 17 | 2 | 2 | 2 | Huangpu River | 20 | 1 | 2 | 2 |
Sun Island | 136 | 0 | 7 | 0 | Shanghai | 15 | 0 | 0 | 0 | Finance | 19 | 0 | 0 | 0 |
Wudalianchi | 112 | 3 | 0 | 3 | Taste | 15 | 0 | 0 | 0 | Edifice | 19 | 1 | 1 | 2 |
Freshing ** | 90 | 0 | 0 | 4 | Forest * | 14 | 0 | 0 | 1 | Clear ** | 19 | 2 | 3 | 3 |
The North Pole | 83 | 3 | 3 | 3 | Comfortable ** | 14 | 0 | 0 | 1 | Visibility | 17 | 0 | 0 | 0 |
Jingpo Lake | 64 | 0 | 0 | 0 | Enjoy ** | 14 | 0 | 0 | 0 | See | 17 | 0 | 0 | 0 |
Harbin | 55 | 2 | 2 | 3 | Nature * | 14 | 0 | 1 | 1 | Building | 16 | 3 | 3 | 3 |
Fresh ** | 49 | 1 | 0 | 0 | Mood | 14 | 1 | 0 | 1 | Place | 15 | 0 | 0 | 0 |
Mohe River | 43 | 0 | 0 | 0 | All The Way | 14 | 3 | 1 | 3 | First | 13 | 7 | 0 | 13 |
Breathe | 42 | 6 | 3 | 6 | Morning | 13 | 0 | 0 | 0 | Everyday | 13 | 0 | 0 | 0 |
Scenery * | 41 | 18 | 17 | 18 | Russia | 13 | 0 | 0 | 1 | Park * | 13 | 0 | 0 | 0 |
Volcano * | 40 | 16 | 12 | 16 | Heihe | 13 | 2 | 6 | 2 | Beijing | 13 | 0 | 1 | 1 |
Northeast | 33 | 5 | 2 | 5 | Time | 12 | 0 | 0 | 0 | Zoo * | 12 | 0 | 0 | 0 |
Scenic Spot * | 32 | 18 | 17 | 18 | Feeling | 12 | 0 | 0 | 0 | Balcony | 12 | 0 | 0 | 0 |
Travel * | 30 | 3 | 3 | 3 | Quiet ** | 12 | 1 | 1 | 1 | Afternoon | 12 | 0 | 0 | 2 |
Heilongjiang River | 30 | 10 | 10 | 10 | Beijing | 11 | 0 | 0 | 0 | Distance | 12 | 1 | 1 | 1 |
Blue Sky * | 29 | 0 | 1 | 0 | Total | 1795 | 114 | 100 | 124 | Opposite | 11 | 0 | 0 | 0 |
China | 29 | 2 | 0 | 2 | Air | 367 | 5 | 9 | 9 | Happy ** | 11 | 0 | 0 | 0 |
Place | 27 | 0 | 0 | 0 | East | 330 | 0 | 0 | 1 | Square | 11 | 0 | 1 | 0 |
Sky | 16 | 0 | 0 | 0 | Pearl | 323 | 0 | 0 | 0 | Outside | 10 | 0 | 0 | 0 |
White Cloud * | 22 | 0 | 0 | 0 | Shanghai | 245 | 4 | 4 | 5 | Wild ** | 10 | 0 | 0 | 0 |
Park * | 22 | 1 | 0 | 1 | Unseen | 57 | 40 | 1 | 21 | Television Tower | 9 | 1 | 0 | 0 |
Travel * | 22 | 3 | 1 | 3 | Sky | 52 | 44 | 41 | 41 | Hong Kong | 9 | 0 | 0 | 0 |
Hour | 21 | 4 | 3 | 4 | The Bund | 41 | 10 | 0 | 0 | Century | 9 | 0 | 0 | 0 |
Nice and Cool ** | 20 | 0 | 0 | 0 | Fresh ** | 41 | 2 | 2 | 3 | Tomorrow | 9 | 0 | 0 | 0 |
Sunshine * | 20 | 0 | 0 | 0 | Weather | 37 | 0 | 1 | 1 | Beautiful ** | 9 | 0 | 0 | 0 |
Vacation * | 20 | 6 | 5 | 6 | Science museum | 29 | 8 | 0 | 0 | Rain △ | 9 | 0 | 0 | 0 |
Beautiful ** | 19 | 0 | 0 | 0 | Pollution △ | 28 | 0 | 0 | 0 | Sunshine * | 9 | 0 | 0 | 0 |
Scenic Spot * | 17 | 0 | 0 | 0 | JinMao | 28 | 28 | 8 | 2 | Enjoy ** | 9 | 0 | 0 | 0 |
Da Hinggan Mountains | 17 | 0 | 0 | 1 | Centrality | 23 | 0 | 0 | 0 | Airport | 9 | 1 | 1 | 1 |
Afternoon | 17 | 0 | 0 | 0 | Global | 21 | 0 | 0 | 0 | Morning | 8 | 1 | 0 | 0 |
Weather | 17 | 0 | 0 | 0 | Breathe | 21 | 0 | 0 | 0 | Hongqiao | 8 | 0 | 0 | 0 |
Songhua River | 17 | 1 | 0 | 2 | Pudong | 20 | 0 | 0 | 1 | Taste | 8 | 0 | 0 | 0 |
Mudan River | 17 | 1 | 1 | 1 | Evening | 20 | 4 | 2 | 2 | Total | 2075 | 163 | 81 | 114 |
Research | Method | Source of Comments | Language | Data Volume | Polarity Type | Accuracy | Precision | Recall | F1 Index |
---|---|---|---|---|---|---|---|---|---|
Ye et al. [34] | SVM | Travel reviews | English | 1191 | 2 | 0.851 | 0.851 | 0.851 | — |
Ganu et al. [53] | SVM | Hotel reviews | English | 52,264 | 4 | 0.81 | 0.51 | 0.45 | 0.48 |
Zheng and Ye [54] | SVM | Hotel reviews | Chinese | 479 | 2 | 0.912 | 0.912 | 0.901 | — |
Zhang et al. [14] | SVM | Hotel reviews | Chinese | 1800 | 2 | 0.948 | 0.948 | 0.948 | — |
Brob [32] | SVM | Hotel reviews | English | 417,170 | 3 | — | 0.67 | 0.66 | 0.68 |
Markopoulos et al. [55] | SVM | Hotel reviews | Greek | 1800 | 2 | 0.718 | 0.65 | 1 | 0.79 |
Ye et al. [34] | Naïve Bayes | Travel reviews | English | 1191 | 2 | 0.807 | 0.82 | 0.82 | — |
Gindl et al. [56] | Naïve Bayes | Travel reviews | English | 1800 | 2 | — | 0.81 | 0.78 | 0.78 |
Zhang et al. [14] | Naïve Bayes | Hotel reviews | Chinese | 1800 | 2 | 0.957 | 0.957 | 0.957 | — |
Shimada et al. [15] | Naïve Bayes | Twitter data | English | 116 | 2 | 0.92 | — | — | — |
Kang et al. [30] | Naïve Bayes | Hotel reviews | English | 70,000 | 2 | — | 0.737 | 0.728 | — |
Kasper and Vela [57] | Statistical classifier | Hotel reviews | German | 4792 | 2 | 0.81 | — | — | 0.80 |
Bjorkelund et al. [58] | Dynamic language model classifier | Hotel reviews | English | 501,083 | 2 | 0.90 | — | — | — |
293,879 | 2 | 0.66 | — | — | — | ||||
Marrese-Taylora et al. [15] | Lexicon-based method | Hotel reviews | English | 200 | 2/3 | — | 0.90 | 0.93 | 0.92 |
Gräbner et al. [31] | Lexicon-based method | Hotel reviews | English | 80,000 | 3 | — | 0.68 | 0.57 | 0.62 |
Bucur [59] | Lexicon-based method | Hotel reviews | English | 3000 | 3 | 0.72 | 0.737 | 0.856 | 0.792 |
García et al. [13] | Lexicon-based method | Hotel reviews | Spanish | 1994 | 3 | 0.80 | — | — | — |
Chiu et al. [28] | SVM and statistical classifier | Hotel reviews | Chinese | 2147 | 2 | — | 0.89 | 0.91 | 0.89 |
Schmunk et al. [26] | SVM and lexicon-based method | Hotel reviews | English | 1516 | 3 | 0.724 | — | — | — |
Pablos et al. [60] | SVM and CRF | Hotel reviews | 6 languages | 1200 | 3 | — | 0.76 | 0.49 | 0.59 |
Kirilenko et al. [18] | Lexicon-based method | Surveys/Website/Twitter | English | 2232/500/762,475 | 3 | 0.57/0.55/0.60 | 0.38/0.52/0.64 | 0.37/0.49/0.60 | — |
Kirilenko et al. [18] | SVM | Surveys/Website/Twitter | English | 2232/500/762,475 | 3 | 0.87/0.60/0.51 | 0.52/0.58/0.50 | 0.47/0.55/0.50 | — |
Kirilenko et al. [18] | Naïve Bayes | Surveys/Website/Twitter | English | 2232/500/762,475 | 3 | 0.87/0.62/0.55 | 0.33/0.56/0.56 | 0.34/0.57/0.55 | — |
Kirilenko et al. [18] | Deeply Moving | Surveys/Website/Twitter | English | 2232/500/762,475 | 3 | 0.65/0.53/0.39 | 0.45/0.61/0.62 | 0.65/0.58/0.44 | — |
Northeast | North | Central | East | South | Southwest | Northwest | Qinghai-Tibet | |
---|---|---|---|---|---|---|---|---|
First Quarter | 0.804 | 0.795 | 0.789 | 0.797 | 0.780 | 0.783 | 0.776 | 0.757 |
Second Quarter | 0.804 | 0.802 | 0.784 | 0.792 | 0.774 | 0.765 | 0.767 | 0.774 |
Third Quarter | 0.799 | 0.779 | 0.779 | 0.786 | 0.773 | 0.768 | 0.764 | 0.783 |
Fourth Quarter | 0.804 | 0.787 | 0.782 | 0.788 | 0.805 | 0.777 | 0.758 | 0.768 |
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Tao, Y.; Zhang, F.; Shi, C.; Chen, Y. Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions. Sustainability 2019, 11, 5070. https://doi.org/10.3390/su11185070
Tao Y, Zhang F, Shi C, Chen Y. Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions. Sustainability. 2019; 11(18):5070. https://doi.org/10.3390/su11185070
Chicago/Turabian StyleTao, Yuguo, Feng Zhang, Chunyun Shi, and Yun Chen. 2019. "Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions" Sustainability 11, no. 18: 5070. https://doi.org/10.3390/su11185070