Extraction and Visualization of Tourist Attraction Semantics from Travel Blogs
Abstract
:1. Introduction
- (1)
- What kind of information would a blog reader be potentially interested in the massive unstructured text and how do we organize those distinct information chunks in a POI graph to facilitate travel planning?
- (2)
- How do we extract the most representative semantic features related to a POI that would not only improve frequency-based treatment of blog texts but also enhance visualization from an end user’s perspective?
2. Background
2.1. Travel Blogs and Tourists’ Movement Patterns
2.2. Semantic Information Extraction and Representation
3. Methodology
3.1. Semantic Model of a POI
3.2. Sem_POI: Proposed Method for Place Semantics Extraction
3.2.1. Content Analysis for Frequency-Based Weighting
Travel Blog Data Preprocessing
Keyword Co-Word Analysis
MDS Representation
3.2.2. Dependency Parsing for Semantics Extraction
- i.
- Nominal subject (nsubj)
- ii.
- Direct object (dobj)
- iii.
- Negation (neg)
- iv.
- Modifiers
- v.
- Adjective and clausal complement (xcomp)
3.3. POI Graph and Geographic Feature Association
3.4. Weighted-Sum Equation Model for Multi-Criteria Weight Computation
- 1)
- , Wnode = R_count, B_rate}
- The function value ranges from 0 to 100, where the first two factors will be assigned 50 points and the succeeding two will be assigned the rest of the 50 points.
- The first two factors are to be computed using travel blog data, while the other two are to be retrieved from social media.
- Each factor is to be separately assigned a unique weight to further distribute the 50 points.
- First, wDegree and wpolarity are assigned values of 20 and 30, respectively. Here, we want more influence of sentiment analysis than frequency-based popularity, which is why wpolarity has a higher value than wDegree.
- Second, wRating and wReview are equally assigned a value of 10 because a greater value would not return a score in the range of 50.
- Since the rating parameter can have a value from 1 to 5 stars, the minimum value this factor can return now is 0 and the maximum value is 40. The review ratio parameter is normalized based on the description of Yahi et al. [79] and it can return a maximum of score of 10.
- The greater the value of , the more popular the POI.
- 2)
- E, Wedge =
- This function value also ranges from 0 to 100, where WCorrelation and WSI are assigned values of 50 and 25, respectively.
- The values are decided so that both factors could contribute half of the points out of 100.
- The attribute SpatialInformation can range from 0 to 2 based on the presence or absence of spatial indicators for a route; hence, in order to have the maximum value of 50, WSI has to be equal to 25.
- The greater the value of , the more popular the route.
4. Experiments and Results
4.1. Performance Comparison Case Study
4.2. Multi-Criteria-Weighted POI Graph
4.3. Comparative Results for Other POIs
5. Discussion and Implications
5.1. Semantics Extraction
5.2. Graph Visualization
5.3. Tourism Research and Practice
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Value |
---|---|
Texts | 60 |
Contexts | 1536 |
Words | 7893 |
Lemma | 6341 |
Occurrences (Tokens) | 72,967 |
Threshold | 10 |
Dependency Type | Grammatical Triples | |
---|---|---|
compound | compound (temples-5, Hindu-4) compound (Caves-8, Batu-7) compound (north-4, km-3) compound (Caves-10, Batu-9) | compound (outcrop-4, limestone-3) compound (temples-12, cave-11) compound (temple-12, cave-11) |
dobj | dobj (Visit-1, temples-5) dobj (Located-1, north-4) | dobj (houses-5, series-7) dobj (climb-4, steps-7) |
nsubj | nsubj (place-14, Caves-10) | nsubj (houses-5, outcrop-4) |
amod | amod (place-14, intriguing-13) amod (outcrop-4, massive-2) | amod (temples-5, historic-3) amod (temple-12, main-10) |
nmod | nmod (Visit-1, Caves-8) nmod (north-4, KL-6) | nmod (series-7, caves-9) nmod (climb-4, temple-1) |
Parameter | Value |
---|---|
Texts | 70 |
Contexts | 68 |
Words | 999 |
Lemmas | 887 |
Occurrences | 3296 |
Threshold | 4 |
Extracted | Not Extracted | |
---|---|---|
Semantic features related to a POI | true positive (tp) | false negative (fn) |
Semantic features not related to a POI | false positive (fp) | true negative (tn) |
Method | Extracted Top Semantic Features for Batu Caves | Precision | Recall | |
---|---|---|---|---|
Term frequency (TF) | cave temple India hindu top site | minute hindu god dedicate steps world | 0.54 | 0.6 |
Term frequency–inverse document frequency (TF–IDF) | temple top site minute dedicate hindu god | hindu shrines impressive lord Murugan famous feature | 0.63 | 0.53 |
Frequent item-set mining | cave limestone kl north India visit | hindu temple train minute steps | 0.54 | 0.5 |
Topic model | kuala lumpur city train day visit petronas | monkeys air things hindu towers | 0.27 | 0.3 |
Sem_POI | hindu temples popular shrines golden statue limestone hill train ride wild monkeys | lord Murugan climb steps kl Sentral ride minute main cave | 0.81 | 0.75 |
Popular POI | Degree Ratio | Polarity | Rating | Review Ratio | Multi-Criteria Weight |
---|---|---|---|---|---|
Batu Caves | 0.5 | 0.875 | 4 | 1 | 76 |
POIs Sequence (n = 2) | Correlation Weight | Spatial Indictor(s) | Spatial Information | Multi-Criteria Weight |
---|---|---|---|---|
{Batu Caves, Kuala Lumpur} | 0.9 | 13 km north | 2 | 95 |
{Batu Caves, KL Sentral} | 0.8 | 30 min | 1 | 65 |
Method | Extracted Top Semantic Features for Petronas Towers | Precision | Recall | |
---|---|---|---|---|
TF | twin lumpur kuala city world | ticket visit klcc bridge night | 0.4 | 0.4 |
TF–IDF | klcc lumpur kuala tallest ticket | skyline bridge malaysia mall deck | 0.6 | 0.5 |
Frequent item-set mining | city world ticket klcc sky | lumpur kuala bridge things walk | 0.4 | 0.36 |
Topic model | world waiting highest hour malaysia | light entire visually petrosains hotel | 0.3 | 0.23 |
Sem_POI | twin towers skybridge floor observation deck night view park towers | impressive towers klcc park tickets Petronas shopping mall skyline city | 0.8 | 0.67 |
Method | Extracted Top Semantic Features for KL Bird Park | Precision | Recall | |
---|---|---|---|---|
TF | birds park kl garden aviary | world free-flight lumpur kuala largest | 0.3 | 0.27 |
TF–IDF | kl free-flight aviary parrot botanical | lumpur kuala Perdana walk-in garden | 0.4 | 0.33 |
Frequent item-set mining | birds aviary largest world free | garden lumpur kuala visit flight | 0.3 | 0.27 |
Topic model | park bird birds kl world | free gardens aviary flight largest | 0.3 | 0.27 |
Sem_POI | free flight aviary walk largest aviary birds hornbill aviary flight | lake gardens botanical garden bird species home park bird shows | 0.7 | 0.538 |
Graph Representation | Methodology | Spatial Information | Semantic Information |
---|---|---|---|
POI graph [45] | FSPM | × | Route context |
POI and Things of Interest (ToI) graph [46] | FSPM, correlation analysis | × | POI services |
POI and ToI graph [47] | Frequent pattern mining, compact pattern mining | × | Things-to-do |
Word network [20] | FSPM, word correlation analysis | Geographically close POIs | Local features |
Multi-criteria-weighted POI graph (Proposed Model) | Keywords co-word analysis, dependency parsing | Geographically close POIs with precise spatial information | Geo-features, activities, sentiments |
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Haris, E.; Gan, K.H. Extraction and Visualization of Tourist Attraction Semantics from Travel Blogs. ISPRS Int. J. Geo-Inf. 2021, 10, 710. https://doi.org/10.3390/ijgi10100710
Haris E, Gan KH. Extraction and Visualization of Tourist Attraction Semantics from Travel Blogs. ISPRS International Journal of Geo-Information. 2021; 10(10):710. https://doi.org/10.3390/ijgi10100710
Chicago/Turabian StyleHaris, Erum, and Keng Hoon Gan. 2021. "Extraction and Visualization of Tourist Attraction Semantics from Travel Blogs" ISPRS International Journal of Geo-Information 10, no. 10: 710. https://doi.org/10.3390/ijgi10100710
APA StyleHaris, E., & Gan, K. H. (2021). Extraction and Visualization of Tourist Attraction Semantics from Travel Blogs. ISPRS International Journal of Geo-Information, 10(10), 710. https://doi.org/10.3390/ijgi10100710