Name
Abstract | ||
|---|---|---|
Most existing research in the area of emotions recognition has focused on short segments or utterances of speech. In this paper we propose a machine learning system for classifying the overall sentiment of long conversations as being Positive or Negative. Our system has three main phases, first it divides a call into short segments, second it applies machine learning to recognize the emotion for each segment, and finally it learns a binary classifier that takes the recognized emotions of individual segments as features. We investigate different approaches for this final phase by varying how emotions for individual segments are aggregated and also by varying classification model used for the final phase. We present our experimental results and analysis based on a simulated data set collected specifically for this research. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ISDA.2010.5687259 | Intelligent Systems Design and Applications |
Keywords | Field | DocType |
audio signal processing,emotion recognition,learning (artificial intelligence),pattern classification,speech processing,audio signal,binary classifier,call classification,emotion recognition,machine learning system,speech emotion analysis,classification of calls,emotions recognition,machine learning,speech analysis | Speech processing,Audio signal,Binary classification,Emotion recognition,Computer science,Natural language processing,Artificial intelligence,Audio signal processing,Pattern recognition,Speech recognition,Feature extraction,Classification tree analysis,Statistical classification | Conference |
ISBN | Citations | PageRank |
978-1-4244-8134-7 | 0 | 0.34 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Esraa Ali Hassan | 1 | 0 | 0.34 |
| Neamat El Gayar | 2 | 0 | 0.68 |
| Moustafa Ghanem | 3 | 538 | 53.05 |