%0 Journal Article
%T Lexicon and Deep Learning-Based Approaches in Sentiment Analysis on Short Texts
%A Taminul Islam
%A Md. Alif Sheakh
%A Md. Rezwane Sadik
%A Mst. Sazia Tahosin
%A Md. Musfiqur Rahman Foysal
%A Jannatul Ferdush
%A Mahbuba Begum
%J Journal of Computer and Communications
%P 11-34
%@ 2327-5227
%D 2024
%I Scientific Research Publishing
%R 10.4236/jcc.2024.121002
%X Social media is an essential component of our personal and professional lives. We use it extensively to share various things, including our opinions on daily topics and feelings about different subjects. This sharing of posts provides insights into someone¡¯s current emotions. In artificial intelligence (AI) and deep learning (DL), researchers emphasize opinion mining and analysis of sentiment, particularly on social media platforms such as Twitter (currently known as X), which has a global user base. This research work revolves explicitly around a comparison between two popular approaches: Lexicon-based and Deep learning-based Approaches. To conduct this study, this study has used a Twitter dataset called sentiment140, which contains over 1.5 million data points. The primary focus was the Long Short-Term Memory (LSTM) deep learning sequence model. In the beginning, we used particular techniques to preprocess the data. The dataset is divided into training and test data. We evaluated the performance of our model using the test data. Simultaneously, we have applied the lexicon-based approach to the same test data and recorded the outputs. Finally, we compared the two approaches by creating confusion matrices based on their respective outputs. This allows us to assess their precision, recall, and F1-Score, enabling us to determine which approach yields better accuracy. This research achieved 98% model accuracy for deep learning algorithms and 95% model accuracy for the lexicon-based approach.
%K Opinion Mining
%K Lexicon Analysis
%K Twitter Data
%K LSTM
%K Machine Learning
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=130385