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Found 10 papers, 1 papers with code
Relation Analysis between Hotel Review Rating Scores and Sentiment Analysis of Reviews by Chinese Tourists Visiting Japan
In current times, the importance of online hotel review sites has become more and more apparent.
Differences in Chinese and Western tourists faced with Japanese hospitality: A natural language processing approach
For this, we used a data-driven approach to our analysis.
Cultural Vocal Bursts Intensity Prediction
Dependency Parsing
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Emotional Contribution Analysis of Online Reviews
In response to the constant increase in population and tourism worldwide, there is a need for the development of cross-language market research tools that are more cost and time effective than surveys or interviews.
Causal relationship between eWOM topics and profit of rural tourism at Japanese Roadside Stations "MICHINOEKI"
Affected by urbanization, centralization and the decrease of overall population, Japan has been making efforts to revitalize the rural areas across the country.
Analysis of Chinese Tourists in Japan by Text Mining of a Hotel Portal Site
With an increasingly large number of Chinese tourists in Japan, the hotel industry is in need of an affordable market research tool that does not rely on expensive and time-consuming surveys or interviews.
Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks
Scoring patent documents is very useful for technology management.
Community Detection and Growth Potential Prediction from Patent Citation Networks
The scoring of patents is useful for technology management analysis.
Topic Classification Method for Analyzing Effect of eWOM on Consumer Game Sales
Electronic word-of-mouth (eWOM) has become an important resource for the analysis of marketing research.
Development of an Entropy-Based Feature Selection Method and Analysis of Online Reviews on Real Estate
In recent years, data posted about real estate on the Internet is currently increasing.
Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models
In response to this, we applied machine learning algorithms SVM and XGBoost, as well as Logistic Regression, to construct a number of prediction models based on at-home advertisement exposure time and demographic data, examining the predictability of Actual Purchase and Purchase Intention behaviors of 3000 customers across 36 different products during the span of 3 months.
