An online comment fine-grained emotion analysis method based on multi-task learning

A multi-task learning and sentiment analysis technology, applied in the field of natural language processing, can solve the problems of inability to classify multiple fine-grained emotions at the same time, low accuracy, overfitting, etc., to reduce training time and prediction time, and reduce accuracy. , the effect of reducing the impact of noise

Active Publication Date: 2019-05-10
XIDIAN UNIV
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Problems solved by technology

However, this method cannot classify multiple fine-grained emotions at the same time, and also faces the risk of low efficiency and overfitting
[0006] Due to the

Method used

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  • An online comment fine-grained emotion analysis method based on multi-task learning
  • An online comment fine-grained emotion analysis method based on multi-task learning
  • An online comment fine-grained emotion analysis method based on multi-task learning

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Embodiment 1

[0075] 1. Introduction to the processing process:

[0076] Step 1, text segmentation and removal of stop words.

[0077] Chinese word segmentation refers to dividing Chinese sequences into individual words. Use the jieba tool for Chinese word segmentation. Stop words refer to words with no practical meaning such as "的" and "how" in Chinese, and other stop words can also be added manually. Delete these words from the data set according to the preset stop vocabulary.

[0078] Step 2, train word vectors, and map words to numbers.

[0079] Sentiment analysis technology based on deep learning needs to represent all words as low-dimensional dense vectors. Here, word2vec technology is used to train Chinese word vectors. Set the parameters of word2vec: the window size is set to 5, the minimum word frequency is set to 2, and the word vector dimension is set to 128. The word2vec technique is a model used to generate word vectors, wherein words whose occurrence times are lower than...

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Abstract

The invention discloses an online comment fine-grained sentiment analysis method based on multi-task learning. The method comprises the steps that a text representation matrix is sequentially input into a text sentiment feature extractor, a coarse-grained sentiment feature extractor and a fine-grained sentiment feature classifier to obtain a fine-grained sentiment classification result; the text sentiment feature extractor selects a single-layer CNN network to extract text sentiment information from the input text representation matrix to obtain an sentiment representation matrix; wherein thecoarse-grained emotion feature extractor extracts coarse-grained emotion features from an input emotion representation matrix by using a plurality of single-layer CNNs (convolutional neural networks)to obtain coarse-grained emotion feature vectors, and the fine-grained emotion feature classifier performs fine-grained emotion classification on the coarse-grained emotion feature vectors by using amulti-layer full-connection neural network. The method has the advantages of accurate classification and short training time, can be used for emotion analysis of multi-level and multi-granularity Internet user comments, and can be used for personalized recommendation, intelligent search or product feedback.

Description

technical field [0001] The invention belongs to the field of natural language processing, and relates to a fine-grained sentiment analysis method for online comments based on multi-task learning, in particular to a natural language sentiment analysis method, which can be used for personalized recommendation, intelligent search or product feedback. Background technique [0002] With the increasing development of e-commerce, the number of online user comments has shown a blowout growth. Facing unstructured and huge amount of text information, relying on traditional methods for information screening is not only very heavy workload, but also difficult to obtain valuable information in a timely and effective manner. How to automatically analyze and extract opinion information and emotional information from huge user comment data in a timely and efficient manner is an important research topic in the field of text mining. [0003] Sentiment analysis of online reviews is a technolo...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06N3/04
Inventor 公茂果姚传宇王善峰武越张明阳解宇
Owner XIDIAN UNIV
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