南通大学学报（自然科学版）2018，Vol.17Issue(3)：1-5,5.
基于短语结构和词语词性相结合的情感分类方法
Sentiment Classification Based on Combination of Phrase Structure and Word Parts of Speech
摘要
Abstract
A sentiment classification based on the combination of phrase structure and word parts of speech is proposed to improve the quality of the input text and the semantic features between words. This method firstly optimizes word segmentation through phrase structure to extract text features better, and then Word2vec is used to train the text corpus combined of words and parts of speech to obtain a word vector model to solve the problem that Word2vec cannot recognize the word polysemy. Finally, the text is subjected to emotional classification through SVM algorithm.The experimental results show that the algorithm can improve the correctness of text sentiment classification. This method has high practicability for monitoring public opinion, forecasting the stock market, and understanding consumers'' product preference.关键词
短语结构/词性/情感分类/Word2vec/SVMKey words
phrase structure/parts of speech/sentiment classification/Word2vec/SVM分类
信息技术与安全科学引用本文复制引用
郑亚平,施佺..基于短语结构和词语词性相结合的情感分类方法[J].南通大学学报（自然科学版）,2018,17(3):1-5,5.基金项目
江苏省自然科学基金项目 (BK20151272) （BK20151272）
江苏省"六大人才高峰" (2014-WLW-029) （2014-WLW-029）
江苏省"333工程" (BRA2017475) （BRA2017475）
江苏省"青蓝工程"项目 （）
