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An Optimized Deep Learning Model for Emotion Classification in Tweets

Computers, Materials & Continua 2022年第3期70卷 6365-6380页

作者： Chinu Singla Fahd NAl-Wesabi Yash Singh Pathania Badria Sulaiman Alfurhood Anwer Mustafa Hilal Mohammed Rizwanullah Manar Ahmed Hamza Mohammad Mahzari Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaIndia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

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 *** this data can be critical for any *** are often expressed with different intensity and topics which can provide great insight into how something affects *** 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 *** used for analyzing tweet emotions are also briefly presented in literature survey *** this paper,hybrid combination of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural ***,the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques *** 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 *** 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

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