An Optimized Deep Learning Model for Emotion Classification in Tweets  

作　　者：Chinu Singla Fahd NAl-Wesabi Yash Singh Pathania Badria Sulaiman Alfurhood Anwer Mustafa Hilal Mohammed Rizwanullah Manar Ahmed Hamza Mohammad Mahzari 

机构地区：[1]Department of Computer Science and Engineering,Thapar Institute of Engineering and Technology,Patiala,India [2]Department of Computer Science,King Khalid University,Muhayel Aseer,Kingdom of Saudi Arabia [3]Faculty of Computer and IT,Sana’a University,Sana’a,Yemen [4]Department of Computer Science,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,Saudi Arabia [5]Department of Computer and Self Development,Preparatory Year Deanship,Prince Sattam bin Abdulaziz University,Alkharj,Saudi Arabia [6]Department of English,College of Science&Humanities,Prince Sattam bin Abdulaziz University,Alkharj,Saudi Arabia

出　　处：《Computers, Materials & Continua》2022年第3期6365-6380,共16页计算机、材料和连续体（英文）

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP.2/23/42),www.kku.edu.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-Track Path of Research Funding Program.

摘　　要：The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man.Analyzing this data can be critical for any organization.Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society.Sentiment analysis in Twittermitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter.Resources used for analyzing tweet emotions are also briefly presented in literature survey section.In this paper,hybrid combination of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural Network.Furthermore,the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques used.The main drawback of LSTM is that it’s a timeconsuming process whereas CNN do not express content information in an accurate way,thus our proposed hybrid technique improves the precision rate and helps in achieving better results.Initial step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.

关 键 词：Meta level features lexical mistakes sentiment analysis count vector natural language processing deep learning machine learning naive bayes 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]