Name
Abstract | ||
|---|---|---|
This paper describes a system for automatic opinion analysis from spoken messages collected in the context of a user satisfaction survey. Opinion analysis is performed from the perspective of opinion monitoring. A process is outlined for detecting segments expressing opinions in a speech signal. Methods are proposed for accepting or rejecting segments from messages that are not reliably analyzed due to the limitations of automatic speech recognition processes, for assigning opinion hypotheses to segments and for evaluating hypothesis opinion proportions. Specific language models are introduced for representing opinion concepts. These models are used for hypothesizing opinion carrying segments in a spoken message. Each segment is interpreted by a classifier based on the Adaboost algorithm which associates a pair of topic and polarity labels to each segment. The different processes are trained and evaluated on a telephone corpus collected in a deployed customer care service. The use of conditional random fields (CRFs) is also considered for detecting segments and results are compared for different types of data and approaches. By optimizing the choice of the strategy parameters, it is possible to estimate user opinion proportions with a Kullback-Leibler divergence of 0.047 bits with respect to the true proportions obtained with a manual annotation of the spoken messages. The proportions estimated with such a low divergence are accurate enough for monitoring user satisfaction over time. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TASL.2009.2028918 | IEEE Transactions on Audio, Speech & Language Processing |
Keywords | Field | DocType |
opinion analysis,automatic opinion analysis,user satisfaction survey,opinion monitoring,hypothesis opinion proportion,opinion concept,user satisfaction,opinion expressions,spoken surveys,assigning opinion hypothesis,user opinion proportion,hypothesizing opinion,opinion expression,conditional random field,specification language,telephony,speech processing,natural languages,speech recognition,speech segmentation,learning artificial intelligence,signal detection,kullback leibler divergence,conditional random fields,automatic speech recognition | Conditional random field,Speech processing,Computer science,Speech recognition,Natural language,Data type,Natural language processing,Artificial intelligence,Speech segmentation,Classifier (linguistics),CRFS,Language model | Journal |
Volume | Issue | ISSN |
18 | 2 | 1558-7916 |
Citations | PageRank | References |
4 | 0.50 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nathalie Camelin | 1 | 39 | 14.29 |
| Frederic Bechet | 2 | 14 | 1.78 |
| Géraldine Damnati | 3 | 185 | 26.15 |
| Renato De Mori | 4 | 960 | 161.75 |