Back to results
US 2019/0379791 A1Published application
Classification of Transcripts by Sentiment
AssigneeVerint Systems Ltd.
InventorsYaron Winter, Saar Carmi
Life of this patent
Estimating the term…
Abstract
A system and method for distinguishing the sentiment of utterances in a dialog is disclosed. The system utilizes a lexicon that is expanded from a seed using unsupervised machine learning. What results is a sentiment classifier that may be optimized for a variety of environments (e.g., conversation, chat, email, etc.), each of which may communicate sentiment differently.
Claims20
A method for classifying a sentiment of a dialog transcript, the method comprising:
- training a lexicon, wherein the training comprises:
- splitting a training set of dialog transcripts into a negative set and a non-negative set;
- identifying n-grams in the dialog transcripts;
- computing, for each n-gram, a polarity score that corresponds to the likelihood of the n-gram having either a negative or a non-negative sentiment, wherein the computing the polarity score for a particular n-gram comprises comparing the frequency of the particular n-gram in the negative set to the frequency of the particular n-gram in the non-negative set;
- identifying prominent n-grams based on each n-gram's polarity score;
- expanding the lexicon by adding the prominent n-grams, which are not already in the lexicon, to the lexicon;
- and
- repeating the splitting, computing, identifying, and expanding for a plurality of iterations to obtain a trained lexicon, wherein the splitting for each iteration uses the expanded lexicon from the previous iteration;
- and
- classifying the sentiment of the dialog transcript using the trained lexicon.
7 more elements
A call center transcription system comprising:
- a call recorder recording conversations between agents and customers in a call center;
- a transcriber receiving the recorded conversations and creating dialog transcripts corresponding to the conversations;
- and
- a sentiment classifier comprising a processor and a memory, wherein the processor executes software stored in the memory to:
- receive a dialog transcript from the transcriber;
- retrieve a trained lexicon from the memory, wherein the trained lexicon is the product of unsupervised machine learning, wherein the unsupervised machine learning comprises:
- analyzing, using a seed, a set of unannotated dialog transcripts to identify negative words and phrases;
- adding identified negative words and phrases to the seed to produce an expanded lexicon;
- analyzing the set of unannotated dialog transcripts using the expanded lexicon to find new negative words and phrases;
- adding the new negative words and phrases to the expanded lexicon to produce a further expanded lexicon;
- and
- repeating the analyzing and adding to produce the machine-generated lexicon that is larger than the seed.
9 more elements
A non-transitory computer readable medium containing program instructions that upon execution by a computer processor cause the processor to:
- split a training set of dialog transcripts into a negative set and a non-negative set;
- identify n-grams in the dialog transcripts;
- compute, for each n-gram, a polarity score that corresponds to the likelihood of the n-gram having either a negative or a non-negative sentiment, wherein the computing the polarity score for a particular n-gram comprises comparing the frequency of the particular n-gram in the negative set to the frequency of the particular n-gram in the non-negative set;
- identify prominent n-grams based on each n-gram's polarity score;
- expand the lexicon by adding the prominent n-grams, which are not already in the lexicon, to the lexicon;
- and
- repeat the splitting, computing, identifying, and expanding for a plurality of iterations to obtain a trained lexicon, wherein the splitting for each iteration uses the expanded lexicon from the previous iteration.
4 more elements
Dependencies are inferred from the claim text.