Name
Title | ||
|---|---|---|
Naïve Bayes and unsupervised artificial neural nets for Cancun tourism social media data analysis |
Abstract | ||
|---|---|---|
Sentiment mining aims at extracting features on which users express their opinions in order to determine the user's sentiment towards the query object. We mine over 70 million Twitter microblogs to gain knowledge regarding tourist sentiment on the travel resort destination Cancun in the Yucatan Peninsula of Mexico. We measure sentiment using a binary choice keyword algorithm and a multi-knowledge based approach is proposed using, Self-Organizing Maps and tourism domain knowledge in order to model sentiment. We develop a visual model to express this taxonomy of sentiment vocabulary and then apply this model to maximums and minimums in the time sentiment data. The results show practical knowledge can be extracted. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/NABIC.2010.5716370 | Nature and Biologically Inspired Computing |
Keywords | Field | DocType |
Bayes methods,data analysis,data mining,query formulation,self-organising feature maps,social networking (online),travel industry,unsupervised learning,Cancun tourism social media data analysis,Mexico,Twitter microblog,Yucatan peninsula,features extraction,naive Bayes method,self organizing map,sentiment mining,unsupervised artificial neural net,SOM,Semantic Web,Sentiment Mining,Social Networks,Text Mining,Tourism,Twitter | Social media,Naive Bayes classifier,Domain knowledge,Sentiment analysis,Computer science,Microblogging,Semantic Web,Unsupervised learning,Artificial intelligence,Vocabulary,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-7377-9 | 2 | 0.52 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| William B. Claster | 1 | 5 | 1.27 |
| Hung Dinh | 2 | 2 | 0.52 |
| Malcolm Cooper | 3 | 2 | 0.52 |