Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 2.111

Sentiment–topic modeling in text mining

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

In recent years, there has been a rapid growth of research interest in natural language processing that seeks to better understand sentiment or opinion expressed in text. There are several notable issues in most previous work in sentiment analysis, among them: the trained classifiers are domain‐dependent; the labeled corpora required for training can be difficult to acquire from real‐world text; and dependencies between sentiments and topics are not taken into consideration. In response to these limitations, a new family of probabilistic topic models, namely joint sentiment–topic models, have been developed, which are capable of detecting sentiment in connection with topic from text without using any labeled data for training. In addition, the sentiment‐bearing topics extracted by the joint sentiment–topic models provide means for automatically discovering and summarizing opinions from a vast amount of user‐generated data. WIREs Data Mining Knowl Discov 2015, 5:246–254. doi: 10.1002/widm.1161

Topic examples extracted by sentiment–topic models.
[ Normal View | Magnified View ]
Parameter notations of the JST model.
[ Normal View | Magnified View ]
(a) Graphical model of JST and (b) JST generative process.
[ Normal View | Magnified View ]
Amazon Kindle cover reviews. Text highlighted in green and red indicate the pros and cons, respectively, on particular topics about the product.
[ Normal View | Magnified View ]
Encode sentiment prior by modifying the Dirichlet prior with the transformation matrix.
[ Normal View | Magnified View ]

Browse by Topic

Technologies > Machine Learning
Technologies > Structure Discovery and Clustering
Algorithmic Development > Text Mining

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts