Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-6· 0 citations· 20 references
Abstract
Sentiment analysis (or opinion) is a core problem in Natural Language Processing (NLP) that aims to automatically determine the polarity of opinions expressed in text. As online review platforms continue to expand, the volume of user generated content has grown beyond what can be examined manually. Movie reviews are particularly challenging because they are subjective, vary widely in length, and use diverse writing styles. This paper presents a sentiment classification pipeline for IMDb movie reviews that combine rigorous text cleaning with Term Frequency Inverse Document Frequency (TF–IDF) feature extraction and classical supervised learning models. Exploratory analysis is used to examine sentiment balance and review-length characteristics. The pre-processing stage removes HTML/URLs, normalizes text, filters stop words, and applies lemmatization to reduce noise and vocabulary sparsity. TF–IDF vectors with unigram and bigram features are then constructed to capture both individual sentiment cues and short contextual phrases. Logistic Regression, Linear SVM, Multinomial Naïve Bayes, and Random Forest are evaluated under a consistent experimental setup. Performance is reported using accuracy, precision, recall, F1-score, and ROC-based analysis. The results show that Logistic Regression achieves the best overall performance, with an accuracy of 88.68% and an F1-score of 88.62%, followed closely by Linear SVM. Overall, TF–IDF with linear classifiers remains an interpretable and computationally efficient baseline for practical sentiment classification.
The high flow of information from online media in Indonesia makes it difficult for manual analysis to identify emerging themes and sentiments. News headlines, as the first element seen by the public, play a crucial role in shaping opinion, but their massive volume and diverse themes make it difficult for manual analysis to identify topics and their underlying sentiments. To address this challenge, this study analyzed 30,329 news headlines from the online news portal detik.com for the entire year 2024. A quantitative Natural Language Processing (NLP) framework was applied, consisting of data collection through web scraping, text preprocessing, transformer-based topic modeling using BERTopic, sentiment classification using IndoBERT, and a topic sentiment intersection analysis. Preprocessing included case folding, text cleaning, normalization of informal words, and tokenization. For lexicon-based labeling, stopword removal and stemming were applied, while transformer-based models utilized minimally processed text to preserve contextual information. Topic modeling was performed using BERTopic, while sentiment classification (positive, negative, and neutral) used the IndoBERT model. The main objective of this study was to evaluate the combined performance of the two models in mapping dominant issues and the sentiments contained in media reports. The results showed that BERTopic successfully identified 366 topics. An evaluation of the 10 most dominant topics yielded a coherence score of 0.5145, indicating a relevant topic clustering. The IndoBERT demonstrated high agreement with lexicon-generated sentiment labels, with an accuracy of 94.78%, a precision of 95.04%, a recall of 94.79%, and an F1-score of 94.81%. These findings confirm that the combination of transformer-based models is effective for in-depth analysis of discourse in Indonesian-language political news headlines from a major Indonesian online news portal (detik.com).
Bagas Yana Prayoga, Qurrotul Aini, Fitroh Fitroh· International Journal of Int...· 0 citations
An integrated framework based on transformer architecture for topic modeling and sentiment analysis for Hindi and Italian social-media discourse, customer reviews and news corpus is introduced and it is suggested that there is clear benefit for morphologically complex text and mixed script text for using contextual embeddings and language-specific pretraining.
Sunita Basalingayya, T. J. Peter· Journal of Intelligent Decis...· 0 citations
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.
Unknown authors· International Journal of Inn...· 0 citations
Experimental results show that BERT-LSTM's superior performance stems from its ability to capture bidirectional contextual dependencies, improving contextual modelling of complex linguistic constructs, however, sarcasm and negation remain important sources of error.
Mohd. Danish, Saifullah Khalid· Journal of Intelligent &...· 0 citations
The rapid expansion of digital payments has produced massive volumes of user-generated reviews, making manual analysis impractical. This study focuses on the challenge of neutral sentiment classification in Indonesian e-wallet reviews, where neutral comments often contain ambiguous language and are underrepresented relative to positive and negative classes. A total of 26,537 preprocessed DANA application reviews were used to evaluate whether Word2Vec embeddings and Easy Data Augmentation (EDA) can improve neutral sentiment detection when combined with Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) architectures. Experiments comparing eight model configurations showed that the combination of Word2Vec, EDA, and LSTM achieved the best performance, with 0.861 accuracy, 0.841 macro-F1, and 0.749 F1-score for the neutral class. These findings demonstrate that semantic representations and controlled lexical variation can jointly enhance minority-class recognition in short informal Indonesian text and highlight the importance of aligning embedding strategies with sequence architectures.
Muhammad Fattah Edric Camilo, Fatma Indriani, M. Faisal et al.· Jurnal Informatika· 0 citations
Obvious performance discrepancies exist across LLMs; high-quality annotations significantly boost downstream classification accuracy, which highlights the importance of selecting competent LLMs for sentiment labeling tasks.
Wenjing Pi, Changxian He· Computers and artificial int...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
