tf–国际设计公司
计算机科学
逻辑回归
萧条(经济学)
期限(时间)
情绪分析
人工智能
特征提取
特征(语言学)
自然语言处理
模式识别(心理学)
数据挖掘
机器学习
语言学
宏观经济学
哲学
物理
经济
量子力学
作者
M. Mounika,N. Srinivasa Gupta,B. Valarmathi
出处
期刊:Lecture notes on data engineering and communications technologies
日期:2019-12-19
被引量:1
标识
DOI:10.1007/978-3-030-43192-1_70
摘要
A 15-step pre-processing procedure is proposed to improve the accuracy of sentiment mining of depression related posts in the tweets. Perhaps, for the first time, converting emoticons in the depression related tweets into text form is proposed during the pre-processing stage. In this paper, Term Frequency-Inverse Document Frequency with n-grams is used for feature extraction. Sentiment analysis conducted on a dataset consisting of 1.6 million depression related tweets using the proposed pre-processing module with feature extraction using Term Frequency-Inverse Document Frequency with n-grams and Logistic Regression (LR) for classification resulted in 81% of accuracy in detecting depression related tweets.
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