CAPTURING A SUBJECTIVE VIEWPOINT OF A FINANCIAL MARKET ANALYST VIA A MACHINE-LEARNED MODEL
Systems and methods herein provide for establishing a subjective viewpoint in text. In one embodiment, a method includes identifying intents and metrics in each of a plurality of texts, calculating a sentiment score for each text based on the identified intents and metrics of each text, and calculating a disfluency score for each text to weight the sentiment score of each text. The method also includes training the machine learning model with the texts, and processing a subsequent text through the trained machine learning model to determine a sentiment score of the subsequent text.
1 . A method of establishing a subjective viewpoint with a machine learning model, the method comprising:
identifying intents and metrics in a first text;
calculating a first sentiment score for the first text based on the identified intents and metrics of the first text;
calculating a first disfluency score for the first text;
weighting the first sentiment score of the first text based on the first disfluency score; training the machine learning model with the first text to create a trained machine learning model; and
processing a second text through the trained machine learning model to determine a second text sentiment score of the second text.
2 . The method of claim 1 , wherein the first sentiment score and the second sentiment score are on a scale of −1 to +1, with −1 being most negative and +1 being most positive.
3 . The method of claim 1 , wherein identifying the intents and the metrics utilizes supervised learning.
4 . The method of claim 1 , wherein the first disfluency score is a measure of a degree of fluency within the first text.
5 . The method of claim 4 , wherein repetitive words and filler words are identified by the model as disfluencies.
6 . The method of claim 5 , further comprising:
computing a ratio of the disfluencies to a total number of words to calculate the first disfluency score for the first text.
7 . The method of claim 1 , wherein the second disfluency score is a measure of a degree of fluency within the second text.
8 . The method of claim 7 , wherein repetitive words and filler words are identified by the model as disfluencies.
9 . The method of claim 8 , further comprising:
computing a ratio of the disfluencies to a total number of words to calculate the second disfluency score for the second text.
10 . The method of claim 1 , further comprising:
transcribing a plurality of speech conversations with a natural language processor (NLP) to correspondingly generate a plurality of texts.
11 . The method of claim 1 , wherein an interface is provided to allow a user to identify and label certain features in the first text or the second text.
12 . The method of claim 11 , wherein after the first text or the second text has been labeled by the user, a labeled sentiment score can be computed by the NLP.
13 . The method of claim 12 , wherein the labeled sentiment score is displayed with the labels of the text to the user.
14 . The method of claim 12 , wherein the user verifies that the labels are correct and the text is assigned to the database to train the machine learning model of the NLP.
15 . The method of claim 11 , wherein the labels are additionally identified via machine learning.
16 . The method of claim 1 , wherein an interface is provided to identify and label via machine learning certain features in the first text or the second text.
17 . The method of claim 16 , wherein after the first text or the second text has been labeled via machine learning, a labeled sentiment score can be computed by the NLP.
18 . The method of claim 17 , wherein the labeled sentiment score is displayed with the labels of the text.
19 . The method of claim 17 , wherein the text is assigned to the database to train the machine learning model of the NLP.
20 . The method of claim 1 , wherein the machine learning algorithms implemented by the NLP include one of: a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, a regression analysis algorithm, a reinforcement learning algorithm, a self-learning algorithm, a feature learning algorithm, a sparse dictionary learning algorithm, an anomaly detection algorithm, or a generative adversarial network.