Jul 2026· International Journal For Multidisciplinary Research· Vol 8· 0 citations· 24 references
TL;DR
The proposed model provides an efficient solution for automating sentiment classification in large-scale e-commerce platforms and achieves an impressive accuracy of 95%, outperforming traditional machine learning models.
Abstract
With the rapid growth of e-commerce platforms, understanding customer feedback has become crucial for businesses to improve their products and services. Sentiment analysis, a subfield of Natural Language Processing (NLP), plays a vital role in extracting opinions from user-generated content such as product reviews. This project aims to perform sentiment analysis on e-commerce product reviews using a hybrid deep learning approach. A CNN-LSTM (Convolutional Neural Network - Long Short-Term Memory) model was implemented to classify customer reviews into positive and negative sentiments. The CNN layer effectively captures local features from the text, while the LSTM layer processes sequential dependencies for better contextual understanding. The dataset was preprocessed using techniques such as tokenization, stop word removal, and padding, followed by word embedding for semantic representation. Experimental results demonstrate that the CNN-LSTM model achieves an impressive accuracy of 95%, outperforming traditional machine learning models. The proposed model provides an efficient solution for automating sentiment classification in large-scale e-commerce platforms.
Sentiment analysis and sarcasm detection as become an important area in natural language processing
(NLP) due to growth of e-commerce and social media platforms. Customers give feedback through reviews which
helps to understand the contextual meaning and sentiment present in the text.The system integrates DistilBERT and
CNN-LSTM hybrid model where CNN (Convolutional Neural Network) used to extract the local features, and LSTM
(Long Short-Term Memory) learns sequential and long-term dependencies in the text, and DistilBERT is used to
capture contextual embeddings in the text. The proposed system processes the reviews through preprocessing
techniques including text cleaning, negation handling, and tokenization. Experimental results shows that the hybrid
model achieves high accuracy compared to standalone and architecture architectures. The proposed model is applied in
e-commerce platforms, recommendation systems and customer feedback analysis for better decision making.
M. Arathi, Asripathi Nikhitha· International Journal of Inn...· 0 citations
Nowadays, Natural Language Processing, or NLP, is a key component of many programs that analyze and comprehend human language. The sentiment analysis of mobile product reviews collected from the Kaggle repository—more especially, the 20,710-review Amazon Mobile evaluations dataset—is the main emphasis of this research. Reviews of well-known cellphone companies including Samsung, Nokia, Apple, Redmi, and others are included in the dataset. The main goal of this research is to categorize consumer attitudes into three groups: neutral, negative, and positive. This research heavily relies on Natural Language Processing (NLP), particularly in the commercial and e-commerce domains where decision-making and product enhancement depend on a comprehension of client input. In this research, sentiment categorization is carried out using deep learning methods like Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN). Lowercase conversion, punctuation and symbol removal, tokenization, stopword removal, stemming, and lemmatization are some of the preprocessing methods used to enhance text quality and model performance. The algorithms are compared based on execution time and accuracy. The survey also determines the top-performing mobile brand by counting the amount of positive reviews. The outcomes show that deep learning models perform better and produce the best results. This research promotes business intelligence in the e-commerce industry and advances our understanding of consumer sentiment behavior.
Dhananchezhiyan R, M. Rameshkumar· International journal of com...· 0 citations
User-generated content on social media for sentiment analysis is a critical tool for businesses
seeking to understand and respond to customer emotions at social media on product brands.
Currently, customers’ emotions on social media are expressed using text and images, while
existing systems analyze only textual data to understand customer emotions. The study
addresses limitations in traditional sentiment analysis methods by incorporating both textual
and image features, thereby improving accuracy and reliability in predicting consumer
sentiments. The model architecture combined Convolutional Neural Networks (CNN) for
image feature extraction and Long Short-Term Memory (LSTM) networks for text sequence
modeling. The implementation was carried out using Python programming language with
deep learning libraries such as TensorFlow and Keras, and training was conducted using
binary cross-entropy loss with the Adam optimizer. Model evaluation was performed using
accuracy, precision, recall, F1-score, and confusion matrix metrics. Experimental results on
the same dataset indicated that the proposed CNN-LSTM model achieved a test accuracy of
95%, outperforming baseline traditional models such as Naïve Bayes (73%), Support Vector
Machine (75%), and Decision Tree (55%) that utilized single-modality textual data.
M. S. Udoh· International Journal of Com...· 0 citations
The rapid growth of digital communication platforms has generated vast amounts of textual data containing valuable insights into public sentiment toward products, services, and digital assets. Accurately analyzing this data is crucial for understanding consumer behavior and market trends in digital marketing and cryptocurrency ecosystems. This study presents a comparative analysis of two deep learning architectures, Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN), for sentiment classification using textual data. Both models were trained for twenty epochs without early stopping to evaluate their full learning potential. The experimental results revealed that the BiLSTM model achieved superior performance with an accuracy of 73.82% and an F1-score of 72.40%, while the CNN model obtained 65.10% accuracy and an F1-score of 64.28%. The BiLSTM demonstrated a higher capability to capture sequential dependencies and contextual semantics through its bidirectional processing, whereas the CNN primarily relied on local feature extraction, limiting its contextual understanding. Class-wise analysis showed that BiLSTM performed strongly in identifying neutral and positive sentiments but struggled with negative sentiment detection due to data imbalance and linguistic ambiguity. These findings highlight BiLSTM’s robustness and suitability for sentiment analysis in digital market applications. The study emphasizes the importance of context-aware deep learning models in supporting data-driven marketing strategies, customer sentiment monitoring, and digital asset analysis. Future research is recommended to integrate transformer-based architectures such as BERT or RoBERTa to further enhance contextual comprehension and improve overall classification performance.
Hendro Budiyanto· Journal of Digital Market an...· 0 citations
Indonesia's expanding e-commerce sector generates a growing volume of customer-written product reviews that can reveal both satisfaction and dissatisfaction. Automatically determining sentiment in these reviews is nevertheless difficult because marketplace language commonly includes informal wording, inconsistent spelling, brief statements, and domain-specific terms. This research benchmarks conventional machine learning methods for classifying the sentiment of Indonesian e-commerce reviews in the PRDECT-ID dataset. The data were obtained from Tokopedia and contain sentiment and emotion annotations. Following preprocessing, the experiment used 5,305 reviews, comprising 2,752 negative and 2,553 positive instances. The processing pipeline included case folding, text cleaning, normalization, tokenization, selective removal of stopwords, and Term Frequency-Inverse Document Frequency (TF-IDF) feature construction. Multinomial Naive Bayes, Support Vector Machine, and Random Forest were then evaluated under the same experimental configuration. The TF-IDF and Support Vector Machine combination produced the strongest results, reaching 0.9595 accuracy, 0.9594 macro-F1, and 0.9595 weighted-F1. These findings establish a reproducible reference point for sentiment classification in Indonesian e-commerce reviews.
Muhammad Fairuzabadi, Indo Intan, Sitti Suhada· JTH: Journal of Technology a...· 0 citations
This paper presents a context-aware hybrid deep learning approach by integrating the Robustly Optimized BERT Pretraining Approach (RoBERTa) with Bidirectional Long Short-Term Memory (BiLSTM) networks to generate rich contextual word embeddings.
V. Gayatri, Rajani Rajalingam· International Journal for Re...· 0 citations
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