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All Journal International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Baker, Mohammed Rashad
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Computer Science & IT Engineering

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Advanced deep learning techniques for sentiment analysis: combining Bi-LSTM, CNN, and attention layers Mirdan, Asmaa Sami; Buyrukoglu, Selim; Baker, Mohammed Rashad
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1848

Abstract

Online platforms enhance customer engagement and provide businesses with valuable data for predictive analysis, critical for strategic sales forecasting and customer relationship management. This study explores in depth the potential of sentiment analysis (SA) to enhance sales forecasting and customer retention for small and large businesses. We collected a large dataset of product review tweets, representing a rich consumer sentiment source. We developed an artificial neural network based on a dataset of product review tweets that captures both positive and negative sentiments. The core of our model is Bi-LSTM (Bidirectional Long Short-Term Memory) architecture, enhanced by an attention mechanism to capture relationships between words and emphasize key terms. Then, a one-dimensional convolutional neural network with 64 filters of size 3x3 is applied, followed by Average_Max_Pooling to reduce the feature map. Finally, two dense layers classify the sentiment as positive or negative. This research provides significant benefits and contributions to sentiment analysis by accurately identifying consumer sentiment in product review tweets. The proposed model that integrated Bi-LSTM with attention mechanism and CNN detects negative sentiment with a precision of 0.97, recall of 0.98, and F1-score of 0.98, allowing companies to address customer concerns, improving satisfaction and brand loyalty proactively. In addition, the proposed model presents a better sentiment classification on average for both positive and negative sentiments, and accuracy (96%) compared to the other baselines. It ensures high-quality input data by reducing noise and inconsistencies in product review tweets. Moreover, the dataset collected in this study serves as a valuable benchmark for future research in sentiment analysis and predictive analytics.
Analyzing Public Sentiment on Electric Vehicles Through BERTopic and Emotion-Based Data Clustering Jihad, Kamal H.; Bilal, Azhar Ahmed; Baker, Mohammed Rashad; Aljanabi, Yaser Issam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6830

Abstract

The escalating impact of technological advancements on worldwide society prompts a closer examination of their profound consequences. Enhanced communication methods and the significant influence of social media platforms stand out as critical factors, with the automotive industry responding to environmental concerns through the emergence of electric vehicles (EVs). In this work the relationship between the trends of EV evolving and social media was utilized using X (aka, Twitter) data. Specifically, this work studies the increasing market demand for EVs due to the impact of social media. Consequently, the study is crucial for both clients and EV manufacturers. To identify the primary discussion themes on Twitter, this article utilizes a topic modelling technique (BERTopic) a data mining method and analyses the production and sales of EV manufacturers. We utilized The National Research Council Canada's Emotion Lexicon (NRCLex) for emotion analysis. Trust, surprise, anger, anticipation, positive, negative, disgust, fear, sadness, and joy are the eight emotions of NRCLex that can provide awareness of the present dynamics. We compared current media coverage of EVs and topic-modeled data. The results showed that BERTopic and NRCLex provided a depth of analysis via the emotional analysis. Consequently, this study contributes to improving the understanding of public sentiment's influence on EV trends.
Co-Authors Aljanabi, Yaser Issam Bilal, Azhar Ahmed Buyrukoglu, Selim Jihad, Kamal H. Mirdan, Asmaa Sami