Social network text sentiment fine-grained classification method based on deep learning

Abstract

The invention provides a social network text sentiment fine-grained classification method based on deep learning, which relates to the field of sentiment multi-classification, and comprises the following steps of crawling social network text data by using a Scrapy framework, performing data cleaning and word segmentation, and performing word vector conversion by taking a word segmentation result as input of word2vec; carrying out text sentiment 8 classification based on a CNN model; taking a word vector conversion result as the input of a CNN (Convolutional Neural Network) embedding layer, carrying out forward and reverse propagation process training models such as convolution, pooling, probability calculation and the like, realizing transfer learning of network comment emotion classification, carrying out two rounds of sampling on social network texts to realize instance migration, training a classifier, and carrying out emotion prediction on comments; and performing system design onthe above work, performing visual display on an analysis result, designing a display module by utilizing an MVC three-layer architecture, and designing an interface for three aspects of functions of single-text or multi-text emotion fine-grained classification, cross-platform transfer learning text emotion fine-grained classification and a social network popularity map.

Description

technical field [0001] The invention relates to the technical field of sentiment multi-classification, in particular to a fine-grained classification method for social network text sentiment based on deep learning. Background technique [0002] With the explosive development of the Internet and mobile devices, the interaction and connection between people is increasingly dependent on social networks. These social networking sites have brought earth-shaking changes to people's lives and greatly facilitated the connection between people. Social networks such as Sina Weibo, Tencent Weibo, Baidu Tieba, and WeChat Moments in China, as well as Facebook, Twitter, and Instagram abroad, have become an indispensable part of modern people's daily life. On August 9, 2017, Weibo released the financial report for the second quarter and full year of 2017. The financial report shows that Weibo's user scale, activity and revenue have all achieved rapid growth. As of the end of the second ...

AI Technical Summary

Problems solved by technology

[0005] At the same time, the labeled data of e-commerce reviews is scarce, resulting in insufficient training models

Images