#4245. An optimized deep learning model for emotion classification in tweets

September 2026publication date
Proposal available till 28-05-2025
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Visual Arts and Performing Arts;
Cultural Studies;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
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. 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 LSTM is Long Short Term Memory and CNN represents Convolutional Neural 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 time-consuming 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.
Keywords:
Count vector; Deep learning; Lexical mistakes; Machine learning; Meta level features; Naive bayes; Natural language processing; Sentiment analysis

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