Deep Learning For Sentiment Analysis Research Articles

Application of Deep Learning in Sentiment Analysis Based on Movie Review Datasets

Abstract. With the rapid development of the Internet and social networks. user-generated content (UGC) has exploded on the Internet as an important form of user feedback, especially movie reviews, which are of great value to the film industry and market analysis, and can help relevant groups understand the reputation and evaluation of movies. As a natural language processing technology, sentiment analysis can effectively identify and extract emotional biases in text, providing a new perspective and method for sentiment analysis from film criticism. The results show that using deep learning can significantly speed up this work and make sentiment analysis more efficient and faster. The purpose of this article is to explore the application and specific process of deep learning in sentiment analysis of film criticism datasets, to find the most appropriate model by comparing and analyzing multiple models, and to present the rating data of some films from the perspective of display technology.

Aspect Category Detection Using Bi-LSTM-Based Deep Learning for Sentiment Analysis for Hindi

Aspect Category Detection Using Bi-LSTM-Based Deep Learning for Sentiment Analysis for Hindi

Leveraging Social Media and Deep Learning for Sentiment Analysis for Smart Governance: A Case Study of Public Reactions to Educational Reforms in Saudi Arabia

The Saudi government’s educational reforms aim to align the system with market needs and promote economic opportunities. However, a lack of credible data makes assessing public sentiment towards these reforms challenging. This research develops a sentiment analysis application to analyze public emotional reactions to educational reforms in Saudi Arabia using AraBERT, an Arabic language model. We constructed a unique Arabic dataset of 216,858 tweets related to the reforms, with 2000 manually labeled for public sentiment. To establish a robust evaluation framework, we employed random forests, support vector machines, and logistic regression as baseline models alongside AraBERT. We also compared the fine-tuned AraBERT Sentiment Classification model with CAMeLBERT, MARBERT, and LLM (GPT) models. The fine-tuned AraBERT model had an F1 score of 0.89, which was above the baseline models by 5% and demonstrated a 4% improvement compared to other pre-trained transformer models applied to this task. This highlights the advantage of transformer models specifically trained for the target language and domain (Arabic). Arabic-specific sentiment analysis models outperform multilingual models for this task. Overall, this study demonstrates the effectiveness of AraBERT in analyzing Arabic sentiment on social media. This approach has the potential to inform educational reform evaluation in Saudi Arabia and potentially other Arabic-speaking regions.

Tourist attraction reviews based on deep learning Sentiment Analysis System

In recent years, with the rapid development of information technology, online travel reviews have become a key source of information for tourists to understand destinations. In the face of massive and unstructured travel review data, this paper innovatively proposes a sentiment analysis system for tourist attraction reviews based on deep learning. The system cleverly uses the BERT pre-trained model, and combines Bi-LSTM and attention mechanism to significantly improve the accuracy of sentiment classification. In order to verify its effectiveness, this paper conducts experiments on the evaluation data of scenic spots in Hubei Province. The experimental results are very impressive through multi-dimensional indicators such as accuracy, precision, recall, and F1-score. The accuracy rate is as high as 93.5%, the accuracy rate is 94.1% for positive reviews, the accuracy rate for negative reviews is 92.8%, the recall rate is 92.1%, and the F1-score is 92.8%. This fully shows that the system can accurately identify the emotional tendency of tourist attraction reviews, provide a strong basis for scenic spot management, help it adjust service strategies in a timely manner, and also assist tourists to make more informed decisions, providing a reliable technical reference for the digital transformation and intelligent management of the tourism industry.

Social media in healthcare emergency management: insights from Spanish hospitals during the COVID-19 pandemic

PurposeThis study aims to explore the Facebook communication strategies of Spanish hospitals during health emergencies, focusing on their role in crisis management and public information dissemination.Design/methodology/approachAutomatic topic modelling and deep learning sentiment analysis were applied to analyse 151,738 posts from 274 hospital Facebook pages (March 2020–Feb 2022). Regression analyses were used to explore the relationships between topics, sentiment scores and hospital characteristics.FindingsThe analysis revealed nine main topics, with the three most prevalent related to COVID-19: vaccine information, security measures and situational updates. This indicates that Spanish hospitals significantly relied on Facebook to manage the emergency. The communication strategies dynamically adapted to the intensity of the pandemic and varied across hospital types. Sentiment analysis showed a negative tone for posts about security measures and situational updates. These findings align with the Agenda-Setting Theory, suggesting that hospitals influenced public discourse. Vaccine information posts were more positive, resonating with the Uses and Gratifications Theory by fulfilling the audience’s need for reassurance and guidance.Originality/valueUsing replicable machine learning techniques, this study elucidates the communication strategies employed by Spanish hospitals to manage healthcare emergencies, such as the COVID-19 pandemic. It highlights factors that potentially influence these strategies and provides theoretical justifications for them. The variation in communication strategies on Facebook among different hospital categories underscores the imperative for stricter guidelines and regulations to guarantee consistent and reliable communication during emergencies. This research provides valuable insights for practitioners and policymakers aimed at developing effective health communication strategies on social media.

AN EMOTION ANALYSIS METHOD THAT INTEGRATES EEG SIGNALS AND MUSIC THERAPY USING A TRANSFORMER MODEL

The need for sentiment analysis in the mental health field is increasing, and electroencephalogram (EEG) signals and music therapy have attracted extensive attention from researchers as breakthrough ideas. However, the existing methods still face the challenge of integrating temporal and spatial features when combining these two types, especially when considering the volume conduction differences among multichannel EEG signals and the different response speeds of subjects; moreover, the precision and accuracy of emotion analysis have yet to be improved. To solve this problem, we integrate the idea of top-k selection into the classic transformer model and construct a novel top-k sparse transformer model. This model captures emotion-related information in a finer way by selecting k data segments from an EEG signal with distinct signal features. However, this optimization process is not without its challenges, and we need to balance the selected k values to ensure that the important features are preserved while avoiding excessive information loss. Experiments conducted on the DEAP dataset demonstrate that our approach achieves significant improvements over other models. By enhancing the sensitivity of the model to the emotion-related information contained in EEG signals, our method achieves an overall emotion classification accuracy improvement and obtains satisfactory results when classifying different emotion dimensions. This study fills a research gap in the field of sentiment analysis involving EEG signals and music therapy, provides a novel and effective method, and is expected to lead to new ideas regarding the application of deep learning in sentiment analysis.

Sentiment to Stocks: Rule-Based and Deep Learning Sentiment Analysis for LSTM-Driven Stock Prediction

How to accurately predict stock prices is a persistent problem in the financial realm. According to behavioral economics, human actions in the stock market tend to be irrational, emotional, and easily misled. In such context, this papers approach involves adopting various sentiment analysis models in subsequent sections of the paper, which aims to synthesize a precise methodology for forecasting stock prices aligned with the available data, namely structured technical analysis, while considering and quantifying investor sentiment, namely unstructured fundamental analysis. This work points out that by adopting sentiment analysis using the VADER and FinBERT models separately, there are accuracy improvements in both integration of VADER or FinBERT and transactional data compared with merely doing stock price prediction by forecasting using historical data alone. This outcome resemble an valuable proposition of modeling and prediction on irrational human behavior. Nevertheless, this paper provides insights on future possible enhancements in this area, which analyzing additional emotional dimensions in textual data and recognizing the multifaceted nature of human emotions is needed.

인천 관광지의 속성별 평가를 위한 딥러닝 기반 감성분석

This study applied the IPA(importance-performance analysis) methodology to unstructured text data to evaluate tourists' satisfaction levels with Incheon attractions. Focusing on attractions within the maintenance and improvement-needed areas identified by IPA, it extracted key positive and negative keywords and sentences directly from tourists' opinions. By applying a text-based approach to evolving IPA research, this exploratory study assessed the concrete satisfaction and dissatisfaction elements felt by tourists. The study limited its scope to 15 Incheon attractions and analyzed 114,007 sentences from Naver blogs written by tourists. Satisfaction levels were derived through deep learning sentiment analysis of the text, while the importance of each attraction was determined by the TF-IDF(term frequency-inverse document frequency) ratio of the attraction names in the text categorized by attributes such as attraction, transportation, activities, and food/drink. Key positive and negative keywords and sentences were extracted using the TextRank algorithm, focusing on attractions in the maintenance and improvementneeded areas. The findings reveal that experiential elements of attractions act as differentiating factors, while their absence can provoke negative reactions from tourists. Moreover, content reflecting local culture and history, the development of family-friendly experiential attractions, parking space expansion, and food service improvements emerged as crucial elements for attraction development.

A Consumer Trust Assessment Model for Online Shopping Based on Fuzzy Fusion Decision-Making

With the rapid development of e-commerce, online shopping has become an indispensable part of people's daily lives. However, consumers often face trust issues during online shopping, such as product quality and seller integrity, which directly impact their shopping experience and purchasing decisions. Therefore, accurately assessing consumer trust has become a crucial task. This study first constructs a consumer trust assessment system, analyzing and selecting key factors related to consumer trust, and establishes a model for assessing consumer trust for online shopping. Subsequently, we propose an assessment method based on text mining and deep learning sentiment analysis techniques to extract consumer sentiment information from specified consumer reviews. Furthermore, through fuzzy decision-making fusion strategy, we integrate sentiment information from the dimensions of quality assurance, reliability, and responsiveness to enhance the accuracy of the assessment.

Intelligent Text Sentiment Analysis of Emotional Beauty in the English Translation: Based on TB-LSTM Algorithm

In the existing deep learning sentiment analysis methods, text is directly processed into a sequence of words, which often ignores the important components of the text and the topic of the text. To solve these problems, the topic of text is extracted through Latent Dirichlet Allocation model, and then a method for integrating topic information into neural networks is proposed. Furthermore, an emotion classification model that integrates the topic information is constructed. An attention mechanism is used at the lexical level and the sentence level, paying attention to the more and less important content separately when building the document presentation. The results show that the proposed model has excellent performance in all four kinds of data, among which the accuracy rate, recall rate and F1-score of CCF-BDC are the best, reaching 0.63, 0.72 and 0.7 respectively. The translation accuracy also reaches 0.85. These results show that the model proposed in this study has better performance in sentiment analysis. These research results will help improve the user's reading experience and make machine translation better meet the needs of human language expression.

The social media sentiment analysis framework: deep learning for sentiment analysis on social media

Researching public opinion can help us learn important facts. People may quickly and easily express their thoughts and feelings on any subject using social media, which creates a deluge of unorganized data. Sentiment analysis on social media platforms like Twitter and Facebook has developed into a potent tool for gathering insights into users' perspectives. However, difficulties in interpreting natural language limit the effectiveness and precision of sentiment analysis. This research focuses on developing a social media sentiment analysis (SMSA) framework, incorporating a custom-built emotion thesaurus to enhance the precision of sentiment analysis. It delves into the efficacy of various deep learning algorithms, under different parameter calibrations, for sentiment extraction from social media. The study distinguishes itself by its unique approach towards sentiment dictionary creation and its application to deep learning models. It contributes new insights into sentiment analysis, particularly in social media contexts, showcasing notable advancements over previous methodologies. The results demonstrate improved accuracy and deeper understanding of social media sentiment, opening avenues for future research and applications in diverse fields.

A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models

In recent years, the use of deep learning models for deploying sentiment analysis systems has become a widespread topic due to their processing capacity and superior results on large volumes of information. However, after several years’ research, previous works have demonstrated that deep learning models are vulnerable to strategically modified inputs called adversarial examples. Adversarial examples are generated by performing perturbations on data input that are imperceptible to humans but that can fool deep learning models’ understanding of the inputs and lead to false predictions being generated. In this work, we collect, select, summarize, discuss, and comprehensively analyze research works to generate textual adversarial examples. There are already a number of reviews in the existing literature concerning attacks on deep learning models for text applications; in contrast to previous works, however, we review works mainly oriented to sentiment analysis tasks. Further, we cover the related information concerning generation of adversarial examples to make this work self-contained. Finally, we draw on the reviewed literature to discuss adversarial example design in the context of sentiment analysis tasks.

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

This article presents a comprehensive evaluation of traditional machine learning and deep learning models in analyzing sentiment trends within the SENT-COVID Twitter corpus, curated during the COVID-19 pandemic. The corpus, filtered by COVID-19 related keywords and manually annotated for polarity, is a pivotal resource for conducting sentiment analysis experiments. Our study investigates various approaches, including classic vector-based systems such as word2vec, doc2vec, and diverse phrase modeling techniques, alongside Spanish pre-trained BERT models. We assess the performance of readily available sentiment analysis libraries for Python users, including TextBlob, VADER, and Pysentimiento. Additionally, we implement and evaluate traditional classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, and simple neural networks like Multilayer Perceptron. Throughout the research, we explore different dimensionality reduction techniques. This methodology enables a precise comparison among classification methods, with BETO-uncased achieving the highest accuracy of 0.73 on the test set. Our findings underscore the efficacy and applicability of traditional machine learning and deep learning models in analyzing sentiment trends within the context of low-resource Spanish language scenarios and emerging topics like COVID-19.

Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model

This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytic policy-making procedures. The automation of legal document extraction takes center stage, underscoring the vital role sentiment analysis plays at the document level. Therefore, this study introduces a novel strategy for sentiment analysis in Canadian maritime case law, combining sentiment case law approaches with state-of-the-art deep learning techniques. The overarching goal is to systematically unearth hidden biases within case law and investigate their impact on legal outcomes. Employing Convolutional Neural Network (CNN)- and long short-term memory (LSTM)-based models, this research achieves a remarkable accuracy of 98.05% for categorizing instances. In contrast, conventional machine learning techniques such as support vector machine (SVM) yield an accuracy rate of 52.57%, naïve Bayes at 57.44%, and logistic regression at 61.86%. The superior accuracy of the CNN and LSTM model combination underscores its usefulness in legal sentiment analysis, offering promising future applications in diverse fields like legal analytics and policy design. These findings mark a significant choice for AI-powered legal tools, presenting more sophisticated and sentiment-aware options for the legal profession.

"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"

Social media is used to categorise products or services, but analysing vast comments is time-consuming. Researchers use sentiment analysis via natural language processing, evaluating methods and results conventionally through literature reviews and assessments. However, our approach diverges by offering a thorough analytical perspective with critical analysis, research findings, identified gaps, limitations, challenges and future prospects specific to deep learning-based sentiment analysis in recent times. Furthermore, we provide in-depth investigation into sentiment analysis, categorizing prevalent data, pre-processing methods, text representations, learning models, and applications. We conduct a thorough evaluation of recent advances in deep learning architectures, assessing their pros and cons. Additionally, we offer a meticulous analysis of deep learning methodologies, integrating insights on applied tools, strengths, weaknesses, performance results, research gaps, and a detailed feature-based examination. Furthermore, we present in a thorough discussion of the challenges, drawbacks, and factors contributing to the successful enhancement of accuracy within the realm of sentiment analysis. A critical comparative analysis of our article clearly shows that capsule-based RNN approaches give the best results with an accuracy of 98.02% which is the CNN or RNN-based models. We implemented various advanced deep-learning models across four benchmarks to identify the top performers. Additionally, we introduced the innovative CRDC (Capsule with Deep CNN and Bi structured RNN) model, which demonstrated superior performance compared to other methods. Our proposed approach achieved remarkable accuracy across different databases: IMDB (88.15%), Toxic (98.28%), CrowdFlower (92.34%), and ER (95.48%). Hence, this method holds promise for automated sentiment analysis and potential deployment.

Deep learning for sentiment analysis on IMDB movie reviews using N-gram features

In the rapidly evolving digital landscape, the synergy of deep learning techniques and abundant datasets has opened new frontiers in various domains. This research delves into the film industry, specifically harnessing the potential of the International Movie DataBase (IMDB) dataset for sentiment analysis. Through a deep learning paradigm, we embark on sentiment classification of movie reviews, discerning between positive and negative sentiments. By navigating data preprocessing and N-gram feature extraction, we engineer a deep learning model comprising embedding, global average pooling, and multi-layer dense architectures. The experimental results underscore the model's prowess in sentiment analysis, emphasizing its capacity to empower informed decision-making within the film industry.

Analyzing sentiment and its application in deep learning: Consistent behavior across multiple occasions

This article offers a systematic review of the evolution in sentiment analysis techniques, moving from unimodal to multimodal to multi-occasion methodologies, with an emphasis on the integration and application of deep learning in sentiment analysis. Firstly, the paper presents the theoretical foundation of sentiment analysis, including the definition and classification of affect and emotion. It then delves into the pivotal technologies used in unimodal sentiment analysis, specifically within the domains of text, speech, and image analysis, examining feature extraction, representation, and classification models. Subsequently, the focus shifts to multimodal sentiment analysis. The paper offers a survey of widely utilized multimodal sentiment datasets, feature representation and fusion techniques, as well as deep learning-based multimodal sentiment analysis models such as attention networks and graph neural networks. It further addresses the application of these multimodal sentiment analysis techniques in social media, product reviews, and public opinion monitoring. Lastly, the paper underscores that challenges persist in the area of multimodal sentiment fusion, including data imbalance and disparities in feature expression. It calls for further research into cross-modal feature expression, dataset augmentation, and explainable modeling to enhance the performance of complex sentiment analysis across multiple occasions.

Artificial intelligence, machine learning, and deep learning for sentiment analysis in business to enhance customer experience, loyalty, and satisfaction

Artificial intelligence, machine learning, and deep learning for sentiment analysis in business to enhance customer experience, loyalty, and satisfaction

Aspect-based Sentiment Analysis Model for Evaluating Teachers' Performance from Students' Feedback

Evaluating teachers' performance is a fundamental pillar of educational enhancement, guiding the evolution of pedagogical practices and fostering enriched learning environments. This study pioneers an innovative approach by harnessing sentiment analysis within an aspect-based framework to decipher the intricate emotional nuances embedded within students' feedback. By categorizing sentiments as positive, negative, and neutral, we delve into the diverse perceptions of teaching aspects, offering a multifaceted portrait of educators' contributions. Through meticulous data collection, preprocessing, and a deep learning sentiment analysis model, we dissected student comments into distinct teaching aspects. The subsequent sentiment analysis unearthed positive, negative, and neutral sentiments. Positive sentiments highlighted strengths and effective communication, while negative sentiments illuminated areas for growth. Neutral sentiments provided contextual equilibrium, forming a holistic tapestry of teachers' performance. The proposed model achieved 86\% F1 score for classifying sentiments into three classes.

Advances in Sentiment Analysis in Deep Learning Models and Techniques

The article investigates the advantages, disadvantages, and areas of research that need more exploration regarding deep learning architectures used in sentiment analysis. These architectures let models learn complex language features from data without explicit feature engineering, changing sentiment analysis. The models' capacity to capture long-range dependencies has improved their context and nuanced expression interpretation, especially in long or metaphorical texts. Deep learning sentiment analysis algorithms have improved, yet they still face obstacles. The complexity of these models raises ethical questions about bias and transparency. They also require huge, annotated datasets and computational resources, which limits their use in resource-constrained contexts. Adopting deep learning models requires balancing performance and practicality. Explore critical deep learning sentiment analysis research gaps. Cross-domain and cross-lingual sentiment analysis requires context- and language-specific models. Textual and non-textual multimodal sentiment analysis offers untapped potential for complex sentiment interpretation. Responsible AI deployment requires model interpretability, robustness against adversarial assaults, and domain consistency. Finally, deep learning and sentiment analysis have changed our knowledge of human emotion. Accuracy and contextual comprehension have improved, but model transparency, data prerequisites, and practical applicability remain issues. Overcoming these restrictions and exploring research gaps will enable responsible sentiment analysis AI innovation.