Challenges Of Sentiment Analysis Research Articles

The study presents a sentiment analysis model to tackle two key challenges in multimodal sentiment analysis. The first challenge focuses on effectively capturing both modality-specific and modality-invariant features, which demands deep interactions across various modalities. The second challenge is to minimize interference among modalities, as such interference can degrade predictive accuracy. To address these issues, the modal feature interaction model utilizes RoBERTa and long short-term memory for feature extraction and analysis across text, audio, and video data. For the first challenge, the model employs self-attention and crossmodal attention mechanisms to facilitate modal feature interaction, enriching both intramodal and intermodal representations. To overcome the second challenge, the model reduces the L2 distance between multimodal representations during fusion, enabling seamless integration of intra- and intermodal features while capturing sentiment-related information for precise emotion prediction. Experimental results on two datasets reveal that the modal feature interaction model surpasses existing baseline models in sentiment analysis tasks.

Easy accessibility to the internet and social media allows individuals to communicate anonymously, providing opportunities for abusive and harmful behavior. The psychological impact of cyberbullying can be very detrimental, triggering stress, depression, and even causing more serious consequences such as suicide. This paper describes cyberbullying sentiment analysis with a focus on the use of four different boosting methods, namely Gradient Booster, Gradient Booster, XGBoost, AdaBoost, dan LightGBM on a multi-label public dataset covering 6 categories. The aim of this research is to compare and analyze the relative performance of these boosting methods in overcoming the challenges of multi-label sentiment analysis in the context of cyberbullying. Results reveal that XGBoost and LightGBM have a tendency to more effectively overcome the challenges of detecting cyberbullying in more complex categories, making a positive contribution to the development of superior detection systems in the context of multi-label sentiment analysis. This research contributes to the field by providing a comparative analysis of state-of-the-art boosting algorithms, highlighting their strengths in multi-label classification tasks, and offering practical insights for developing more accurate and reliable cyberbullying detection systems. The findings from this study are expected to serve as a reference for future development of machine learning-based tools that can help mitigate the psychological harm caused by online abuse, particularly in detecting subtle and complex forms of cyberbullying behavior.

The absence of a sentiment lexicon tailored to agricultural product reviews presents significant challenges for accurate sentiment analysis in this domain. Existing general-purpose lexicons, such as NTUSD, HOWNET, and BosonNLP, fail to capture the unique linguistic features of agricultural reviews, leading to suboptimal classification performance. To address this gap, this study constructs the BSTS sentiment lexicon, using a dataset of 19,843 preprocessed reviews from JD.com. Positive and negative seed words were extracted through BERT-based Term Frequency (TF) analysis, and the SO-PMI algorithm was applied to calculate sentiment scores for candidate words. By determining an optimal threshold, a balanced and effective lexicon was developed. Experimental results demonstrate that the BSTS lexicon outperforms existing lexicons in sentiment classification, achieving precision, recall, and F1 scores of 85.21%, 91.92%, and 88.44%, respectively. Furthermore, additional experiments on Taobao's agricultural product reviews confirmed the lexicon's robustness, with performance metrics of 93.28% precision and 87.34% F1 score, highlighting its effectiveness across different e-commerce platforms. The BSTS lexicon significantly improves sentiment classification in the agricultural domain, offering a reliable and domain-specific tool for sentiment analysis in agricultural product reviews.

This study addresses the challenges of sentiment analysis on Indonesian-language YouTube comments, which are complex due to the use of dialects, slang words, emojis, and Latinized Arabic text. The proposed LABERT-LSTM model integrates BERT for deep feature extraction and Bi-LSTM to capture word sequence context effectively. The dataset comprises 24,593 YouTube comments from five renowned Islamic preachers discussing the topic of “tahlilan”. After data preprocessing, the model was evaluated using accuracy, precision, recall, and F1-score metrics. The results demonstrate that LABERT-LSTM achieved an accuracy of 0.95756, precision of 0.94014, recall of 0.91815, and an F1-score of 0.92868, outperforming standalone BERT and Bi-LSTM models by reducing misclassification and improving predictions for negative, positive, and neutral sentiment classes. Future research recommendations include expanding the dataset to other social media platforms, adopting advanced NLP techniques, conducting studies in other languages, and optimizing the model for enhanced performance and computational efficiency.

The rapid growth of digital assets like Bitcoin and cryptocurrencies has increased the need for secure trading platforms such as Indodax. With the growing number of users, reviews on platforms like Google Play Store provide valuable insights into user experience and satisfaction. This research applies Machine Learning methods to classify user review sentiments by comparing three main algorithms Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). One of the main challenge in sentiment analysis is the presence of irrelevant or redundant features, which can reduce model accuracy and increase computational costs. The Feature Selection Chi-Square technique is used to filter the most influential features, enhancing model efficiency without losing critical information. Experimental results show that SVM delivers the best performance compared to Random Forest and XGBoost. Before applying Chi-Square, SVM achieved 91% accuracy, which increased to 94% after applying the feature selection technique. The number of features used was reduced from 52,312 to 2,000 without significant information loss. This combination of SVM and Feature Selection Chi-Square proves to be an efficient and accurate solution for analyzing user sentiment on crypto trading platforms like Indodax. This method is expected to improve the responsiveness of trading applications to user needs and serve as a foundation for further research in Machine Learning-based sentiment analysis.

A major challenge in sentiment analysis on social media is the increasing prevalence of image-based content, which integrates text and visuals to convey nuanced messages. Traditional text-based approaches have been widely used to assess public attitudes and beliefs; however, they often fail to fully capture the meaning of multimodal content where cultural, contextual, and visual elements play a significant role. This study aims to provide practical guidance for collecting, processing, and analyzing social media data using multimodal machine learning models. Specifically, it focuses on training and fine-tuning models to classify sentiment and detect hate speech. Social media data were collected from Facebook and Instagram using CrowdTangle, a public insights tool by Meta, and from X via its academic research application programming interface. The dataset was filtered to include only race-related terms and lesbian, gay, bisexual, transgender, queer, intersex, and asexual community-related posts with image attachments, ensuring focus on multimodal content. Human annotators labeled 13,000 posts into 4 categories: negative sentiment, positive sentiment, hate, or antihate. We evaluated unimodal (Bidirectional Encoder Representations from Transformers for text and Visual Geometry Group 16 for images) and multimodal (Contrastive Language-Image Pretraining [CLIP], Visual Bidirectional Encoder Representations from Transformers [VisualBERTs], and an intermediate fusion) models. To enhance model performance, the synthetic minority oversampling technique was applied to address class imbalances, and latent Dirichlet allocation was used to improve semantic representations. Our findings highlighted key differences in model performance. Among unimodal models, Bidirectional Encoder Representations from Transformer outperformed Visual Geometry Group 16, achieving higher accuracy and macro-F1-scores across all tasks. Among multimodal models, CLIP achieved the highest accuracy (0.86) in negative sentiment detection, followed by VisualBERT (0.84). For positive sentiment, VisualBERT outperformed other models with the highest accuracy (0.76). In hate speech detection, the intermediate fusion model demonstrated the highest accuracy (0.91) with a macro-F1-score of 0.64, ensuring balanced performance. Meanwhile, VisualBERT performed best in antihate classification, achieving an accuracy of 0.78. Applying latent Dirichlet allocation and the synthetic minority oversampling technique improved minority class detection, particularly for antihate content. Overall, the intermediate fusion model provided the most balanced performance across tasks, while CLIP excelled in accuracy-driven classifications. Although VisualBERT performed well in certain areas, it struggled to maintain a precision-recall balance. These results emphasized the effectiveness of multimodal approaches over unimodal models in analyzing social media sentiment. This study contributes to the growing research on multimodal machine learning by demonstrating how advanced models, data augmentation techniques, and diverse datasets can enhance the analysis of social media content. The findings offer valuable insights for researchers, policy makers, and public health professionals seeking to leverage artificial intelligence for social media monitoring and addressing broader societal challenges.

This paper explores the operation of Natural Language Processing (NLP) techniques in sentiment analysis of social media platforms. With the exponential growth of social media as a major communication tool, understanding public sentiment has become pivotal for businesses, governments, and organizations. This study reviews various sentiment analysis techniques, challenges, and advancements in NLP, and evaluates their efficacy in understanding public opinion. The paper also highlights practical use cases of sentiment analysis in marketing, political campaigning, brand management, and customer service.

Sentiment analysis, also known as opinion mining, is a crucial branch of Natural language processing, which focuses on recognizing, extracting, and quantifying sentiment tendencies, emotional intensity and specific emotion types in textual data. With the rapid development of the internet and communication, analyzing sentiment contained in textual data becomes important and crucial for understanding public opinion, consumer behavior, and emotional trends. This paper provides a comprehensive review of sentiment analysis in the range of its application, evolution, task types, methodology and future development by analyzing the literature of this field. Sentiment analysis has developed from traditional lexicon-based methods to modern deep learning methods like CNN, RNN and transformer model, which have significantly improved accuracy and robustness. This paper also discussed challenges in sentiment analysis like sarcasm detection and cross-lingual analysis, and proposed potential solutions. The findings aim to provide comprehensive insight for researchers and contribute to innovations in sentiment analysis.

Hinglish, a hybrid of Hindi and English, poses unique challenges for sentiment analysis due to its linguistic complexity, lack of structured grammar, and limited availability of annotated datasets. This research investigates and analyses existing methods for Hinglish and English sentiment analysis and explores the complexities of this code-mixed domain. By analyzing research papers, reports, and white papers, the study identifies key obstacles like language detection, intricate grammar, and limited data availability. It also highlights the difficulties of adapting models to new contexts. The research proposes innovative solutions based on these challenges and emphasizes the need for a multi-faceted approach to achieve accurate sentiment analysis in Hinglish. This paves the way for further innovation in code-mixed language sentiment analysis, and at the same time, it can form the base for building large language models for sentiment analysis as well as llm poorly understood Hinglish in current capacities.

The rapid growth of social media platforms, particularly Twitter, has led to an unprecedented surge in user-generated data, which presents both opportunities and challenges for sentiment analysis. Traditional sentiment analysis methods often struggle with the noisy and unstructured nature of Twitter data, necessitating advanced techniques for effective feature extraction and visualization. This paper presents a novel approach to enhancing sentiment analysis on Twitter data through advanced feature extraction and visualization techniques. We propose a multi-faceted feature extraction framework that incorporates linguistic, syntactic, and semantic features, leveraging techniques such as word embeddings, part-of-speech tagging, and sentiment lexicons. Additionally, we introduce advanced visualization methods to represent sentiment trends and user interactions, providing a clearer understanding of public sentiment dynamics. Our approach is evaluated using a comprehensive dataset of Twitter posts, demonstrating significant improvements in sentiment classification accuracy and interpretability compared to traditional methods. The results indicate that integrating advanced feature extraction with effective visualization techniques can offer deeper insights into sentiment trends and user behavior, paving the way for more nuanced social media analytics and decision-making.

This study explores sentiment analysis of social media texts for aquatic product listed companies using the BERT-CNN-BiLSTM-Att hybrid model. The model addresses challenges in text sentiment analysis, such as polysemy and feature extraction. By leveraging the Archive Team Twitter Grabs dataset, the study selects text content related to the aquatic industry to analyze the temporal evolution of public sentiment and its relationship with major events. The experiments illustrate sentiment trends for two listed companies and validate the model's applicability using data from ten companies. Results demonstrate that the BERT-CNN-BiLSTM-Att model achieves more accurate text sentiment classification.

Sentiment analysis, the automated process of determining emotions and opinions expressed in content, has evolved to encompass visual media, allowing for a deeper understanding of sentiments conveyed in images. This paper explores the application of image processing techniques, coupled with Python programming, to conduct sentiment analysis by extracting emotional cues from visual data. The study begins with an overview of the significance and challenges of sentiment analysis in images, emphasizing the need for advanced tools to analyze the ever-growing volume of visual content on the internet. Leveraging Python's rich ecosystem of libraries, the paper delves into the technical aspects of sentiment analysis using image data. Key components of this research include the utilization of Convolutional Neural Networks (CNNs) for feature extraction, pre-trained models for sentiment recognition, and the development of custom datasets to train and validate sentiment analysis models. Python libraries like TensorFlow and Keras provide a robust framework for building and deploying deep learning models. The paper discusses the ethical considerations related to image-based sentiment analysis, addressing concerns about privacy, bias, and cultural nuances. It also explores the potential applications of this technology, ranging from brand sentiment analysis in marketing to monitoring public sentiment on social media platforms. Furthermore, the study identifies challenges and opportunities in the field, paving the way for future research endeavors. By bridging the domains of computer vision, natural language processing, and machine learning, sentiment analysis by image processing in Python opens up new avenues for understanding the emotional impact of visual content in an increasingly digital and visually driven world.

A systematic literature review on sentiment analysis techniques, challenges, and future trends

Imbalanced data is a significant challenge in sentiment analysis, as it often impacts the performance of machine learning models. This study applies the Naïve Bayes algorithm, enhanced with the Synthetic Minority Oversampling Technique (SMOTE), to address class imbalance in user reviews of the by.U application. Using the Knowledge Discovery in Databases (KDD) framework, the research involves data selection, preprocessing (text cleaning, normalization, stemming), transformation using TF-IDF, and train-test data splitting. SMOTE is applied to the training data to improve minority class representation, while Naïve Bayes performs sentiment classification. Model evaluation using cross-validation demonstrates that SMOTE increases accuracy from 84.42% to 85.83%. These results underscore the effectiveness of integrating SMOTE with Naïve Bayes in addressing imbalanced data, offering meaningful insights into user sentiment and aiding the development of improved features for the by.U application.

The naturalization of players for Indonesia's national football team has sparked diverse reactions on Twitter, ranging from support to opposition. This situation poses challenges for sentiment analysis, particularly in interpreting public opinion on the policy. A significant challenge arises from the imbalance in sentiment classes, with neutral sentiments outweighing positive and negative ones. This research investigates the effect of class imbalance on sentiment analysis accuracy by employing the KNN algorithm enhanced with the SMOTE technique. A quantitative approach is used, adopting an experimental method aligned with the KDD process stages. The findings reveal that the KNN algorithm without SMOTE achieved an accuracy of 54.77%, with a Precision of 0.65, Recall of 0.57, and F1-Score of 0.44. However, integrating SMOTE with the KNN algorithm significantly improved the outcomes, boosting accuracy to 81.49%, with a Precision of 0.87, Recall of 0.80, and F1-Score of 0.80. These results demonstrate that oversampling techniques like SMOTE are highly effective in mitigating class imbalance and enhancing classification performance, especially for underrepresented classes. This study underscores the efficacy of SMOTE as a solution for addressing class imbalance in sentiment analysis tasks.

ABSTRACT The rapid growth of e-commerce has led to a significant increase in user feedback, especially in the form of post-purchase comments on online platforms. These reviews not only reflect customer sentiments but also crucially influence other users’ purchasing decisions due to their public accessibility. The sheer volume and complexity of product reviews make manual sorting challenging, necessitating businesses to autonomously process and discern customer sentiments. Chinese, a predominant language on e-commerce platforms, presents unique challenges in sentiment analysis due to its character-based nature. This paper introduces an innovative Dual-Channel BiLSTM-CNN (DC-BiLSTM-CNN) algorithm. Based on the language characteristics of Chinese product reviews, a sentiment analysis algorithm, dual channel BiLSTM-CNN (DC-BiLSTM-CNN), is proposed. The algorithm constructs two channels, transforming text into both character and word vectors and inputting them into Bidirectional Long Short-Term Memory (BiLSTM), and Convolutional Neural Network (CNN) models. The combination of these channels facilitates a more comprehensive feature extraction from reviews. Comparative analysis revealed that DC-BiLSTM-CNN significantly outperforms baseline models, substantially enhancing the classification of product reviews. We conclude that the proposed DC-BiLSTM-CNN algorithm offers an effective solution for handling Chinese product reviews, carrying positive implications for businesses seeking to enhance product and service quality, ultimately resulting in heightened user satisfaction.

In the rapidly digitizing landscape of healthcare feedback, online reviews have become a vital source of patient-reported experiences. This study leverages sentiment analysis to decode the narrative content of Google reviews for Mother and Child Hospitals in Jakarta. Utilizing the VADER sentiment analysis tool and GloVe for keyword extraction, the research aimed to correlate qualitative sentiment with quantitative star ratings. This study meticulously processed and analyzed a selection of Google reviews using VADER for sentiment scoring and GloVe for refining the focus on relevant healthcare discussions. This methodological approach allowed for a comprehensive sentiment assessment of the reviews. The analysis revealed a prevalent positive sentiment in higher-rated reviews and negative sentiment in lower-rated reviews, with notable anomalies that underscore the complexity of patient experiences and perceptions. Specific aspects of care, including staff behavior, facility quality, and treatment efficacy, were recurrent themes in the feedback. These findings highlight the potential of patient-reported experiences in shaping healthcare practices and policy. The study emphasizes the importance of healthcare providers understanding and responding to patient feedback to improve care quality. Limitations such as the representativeness of online reviews and the challenges of sentiment analysis in capturing nuanced emotions are discussed. This study offers valuable insights into patient perceptions of maternal and pediatric care in Jakarta, affirming the significance of leveraging online reviews for healthcare quality monitoring and improvement

The analysis of user-generated content, such as product reviews on platforms like Amazon, is critical for understanding consumer sentiment. However, the unstructured nature of these reviews poses challenges for accurate sentiment analysis (SA). This study examines the influence of different preprocessing techniques on the effectiveness of sentiment analysis utilizing three feature extraction methods: BERT, TF-IDF, and GloVe. We evaluated the effectiveness of these techniques with machine learning classifiers such as: Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and Extreme Gradient Boosting (XGBoost). Our findings indicate that preprocessing significantly enhances classification accuracy, particularly for models using TF-IDF and GloVe features, while BERT-based models showed robust performance even with minimal preprocessing. By combining BERT with preprocessing techniques, we attained an exceptional accuracy rate of 98.3% in sentiment analysis. This underscores the significance of meticulous data pretreatment in this field. These insights enhance the creation of more efficient sentiment classification algorithms, providing reliable information from Amazon product reviews.

This study addresses challenges in sentiment analysis for low-resource educational contexts by proposing a framework that integrates Few-Shot Learning (FSL) with Transformer-based ensemble models and boosting techniques. Sentiment analysis of student feedback is crucial for improving teaching quality, yet traditional methods struggle with data scarcity and computational inefficiency. The proposed framework leverages self-attention mechanisms in Transformers and combines models through Gradient Boosting to enhance performance and generalization with minimal labeled data. Evaluated on the UIT-VSFC dataset, comprising Vietnamese student feedback, the framework achieved superior F1-scores in sentiment and topic classification tasks, outperforming individual models. Results demonstrate their potential for extracting actionable insights to enhance educational experiences. Despite its effectiveness, the approach faces limitations such as reliance on pre-trained models and computational complexity. Future work could optimize lightweight models and explore applications in other domains like healthcare and finance.

Audio sentiment analysis is a rapidly evolving field that seeks to uncover emotional insights from audio signals, with broad applications in technology, healthcare, and user experience design. This paper presents a detailed review of recent advancements, highlighting key techniques, ongoing challenges, and real-world applications that illustrate the potential of this technology. The Literature Review provides an integrated analysis of significant findings across various studies, identifying core trends and unique contributions. Finally, the Conclusion explores promising future research directions, laying the groundwork for continued innovation in extracting sentiment from sound. Keywords Audio Sentiment Analysis, Sentiment Classification, Deep Learning Techniques, Feature Extraction, Transfer Learning Ensemble Methods, Multi-Task Learning, Challenges in Sentiment Analysis, Applications of Sentiment Analysis, Literature Review of Audio Sentiment Analysis