Name
Abstract | ||
|---|---|---|
Motivated by the numerous applications of analysing opinions in multi-domain scenarios, this paper studies the potential of a still rarely considered approach to the problem of multi-domain sentiment analysis based on SentiWordNet as lexical resource. SentiWordNet scores are exploited together with additional features to assign a polarity to a text using machine learning. On the other hand, a rule-based approach is studied based on sentiment scores. The introduced methods are tested on single domains of a real-world data set consisting of documents in six different domains, but also in cross-domain settings. The results show that for cross-domain sentiment analysis rule-based approaches with fix opinion lexica are unsuited. For machine-learning based sentiment classification a mixture of documents of different domains achieves good results. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICDIM.2009.5356764 | 2009 FOURTH INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT |
Keywords | Field | DocType |
data mining,rule based,text analysis,natural language processing,learning artificial intelligence,materials,sentiment analysis,machine learning,single domain,accuracy | Data mining,Text mining,Information retrieval,Computer science,Sentiment analysis,Multi domain,Opinion analysis | Conference |
Citations | PageRank | References |
10 | 0.51 | 11 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kerstin Denecke | 1 | 140 | 23.57 |