Name
Abstract | ||
|---|---|---|
Our purpose is to use Twitter data to infer personal values in marketing for Japanese consumers. In this paper, we reintroduce our personal value system and apply the model for inferring personal values with tweets. To adapt the model to the rapid change of wording in tweets, we propose a dynamic model based on time-weighted frequency in this research. We evaluated the prediction results from our previous approach, newly proposed approach (the dynamic model), and other methods with 10-fold cross-validation. Our experiment results show that personal values can be inferred from Twitter data, and our approach based on Bayesian network performs well with skewed training data. |
Year | Venue | Field |
|---|---|---|
2015 | PACLIC | Training set,Data science,World Wide Web,Value system,Bayesian network,Business |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinjun Hu | 1 | 0 | 0.34 |
| Yasuo Tanida | 2 | 0 | 0.68 |