Method and apparatus for detection of sentiment in automated transcriptions
First Claim
1. A method for automatically detecting sentiments in an audio signal of an interaction held in a call center, comprising:
- receiving the audio signal from a logging and capturing unit associated with the call center;
performing audio analysis on the audio signal to obtain text spoken within the interaction;
segmenting the text into context units according to acoustic information acquired from the audio signal to identify units of speech bound by non-speech segments;
wherein each context unit includes one or more words;
extracting a sentiment candidate context unit from the context units using a phonetic based search;
extracting linguistic features from the text of the sentiment candidate context unit and acoustic features from a segment of the audio signal associated with the sentiment candidate context unit;
determining in accordance with the linguistic features and acoustic features whether the sentiment candidate context unit is valid or erroneous; and
determining sentiment polarity and intensity for the sentiment candidate context unit.
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Abstract
A method for automatically detecting sentiments in an audio signal of an interaction held in a call center, including, receiving the audio signal from a logging and capturing unit. Performing audio analysis on the audio signal to obtain text spoken within the interaction. Segmenting the text into context units according to acoustic information acquired from the audio signal to identify units of speech bound by non-speech segments, wherein each context unit includes one or more words. Extracting a sentiment candidate context unit from the context units using a phonetic based search. Extracting linguistic features from the text of the sentiment candidate context unit and acoustic features from a segment of the audio signal associated with the sentiment candidate context unit. Determining in accordance with the linguistic features and acoustic features whether the sentiment candidate context unit is valid or erroneous, and determining sentiment polarity and intensity.
49 Citations
20 Claims
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1. A method for automatically detecting sentiments in an audio signal of an interaction held in a call center, comprising:
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receiving the audio signal from a logging and capturing unit associated with the call center; performing audio analysis on the audio signal to obtain text spoken within the interaction; segmenting the text into context units according to acoustic information acquired from the audio signal to identify units of speech bound by non-speech segments;
wherein each context unit includes one or more words;extracting a sentiment candidate context unit from the context units using a phonetic based search; extracting linguistic features from the text of the sentiment candidate context unit and acoustic features from a segment of the audio signal associated with the sentiment candidate context unit; determining in accordance with the linguistic features and acoustic features whether the sentiment candidate context unit is valid or erroneous; and determining sentiment polarity and intensity for the sentiment candidate context unit. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 20)
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15. An apparatus for automatically detecting sentiments in an audio signal of an interaction held in a call center, comprising:
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an interaction receiving component arranged to receive an audio signal representing the interaction; an extraction component for obtaining text spoken within the interaction; a context unit segmentation component arranged to segment the audio signal into context units according to acoustic information acquired from the audio signal to identify units of speech bound by non-speech segments;
wherein each context unit includes one or more words;a sentiment candidate identification component for identifying a sentiment candidate from the context units using a phonetic based search; a feature extraction component arranged to extract linguistic features from the text of the sentiment candidates and acoustic features from a segment of the audio signal associated with the sentiment candidates; and a classification component arranged to apply a model to the linguistic features and acoustic features to determine whether the audio signal contains sentiment, or sentiment polarity and intensity of the sentiment within the audio signal. - View Dependent Claims (16, 17, 18, 19)
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Specification