METHODS, SYSTEMS AND PROCESSOR-READABLE MEDIA FOR SIMULTANEOUS SENTIMENT ANALYSIS AND TOPIC CLASSIFICATION WITH MULTIPLE LABELS

摘要

Methods, systems and processor-readable media for simultaneous sentiment analysis and topic classification with multiple labels. A sentiment and topic associated with a post can be classified at similar time and a result can be incorporated to predict a feature so that a label of two (or more) tasks can promote and reinforce each other iteratively. A feature extraction and selection can be performed on the tasks and a multi-task multi-label classification model can be trained for each task with maximum entropy utilizing multiple labels to ascertain information derived from an extra label and to manage class ambiguities. Each task has a separate classification model with different predicting features and they can be trained collectively which allows flexibility in model construction. The multi-task multi-label classification model produces a probabilistic result and the classes can be ranked by the probabilistic result and the post can be classified with the multi-label.

主权项

1. A method for simultaneous sentiment analysis and topic classification, said method comprising:
classifying a sentiment and a topic associated with a post simultaneously to thereafter incorporate a result thereof for use in predicting a feature so that a label associated with at least two tasks is capable of promoting and reinforcing each other iteratively; performing a feature extraction and selection with respect to said at least two tasks for training a multi-task multi-label classification model for each of said at least two tasks with a maximum entropy utilizing said label to derive data from an extra label and to deal with class ambiguities; and generating a probabilistic result via said multi-task multi-label classification model so as to thereafter rank said class according to said probabilistic result.