Sentiment Analysis Of Reviews Research Articles

Evaluating customer emotions in coffee shop reviews using machine learning

Sentiment analysis is a crucial technique for understanding and classifying the emotions expressed in written content. Given the widespread use of social media, these platforms have become an essential method for gathering customer feedback in several industries, including the café sector. This research introduces a method that uses the Soft K-means clustering algorithm to automatically evaluate sentiment in response to the growing online presence of coffee establishments. This approach is used to categorize customer evaluations on a famous coffee shop review website. The sentiment analysis of coffee shop reviews utilizes many machines learning methods, including Naïve Bayes, Gradient Boosting Machines (GBM), K-Nearest Neighbor, Support Vector Machines, Logistic Regression, and Random Forest. Furthermore, this paper proposes an innovative ensemble learning technique that integrates these six classifiers to enhance forecast accuracy. The efficacy of various tactics in efficiently capturing the ideas of coffee shop patrons is thoroughly compared.

A novel automated framework for fine-grained sentiment analysis of application reviews using deep neural networks

A novel automated framework for fine-grained sentiment analysis of application reviews using deep neural networks

Sentiment analysis of online reviews of energy-saving products based on transfer learning and LBBA model

With the exacerbation of global climate change and the growing environmental awareness among the general public, the concept of green consumption has gained significant attention across various sectors of society. As a representative example of green consumer products, energy-saving products play a crucial role in the timely realization of dual carbon goals. However, an analysis of online comments regarding energy-saving products reveals that the majority of these products still exhibit shortcomings in terms of efficacy, noise level, cost-effectiveness, and particularly, energy-saving appliances. This study focuses on the user-generated online comments data from the Taobao e-commerce platform for Grade 1 energy-saving refrigerators. By employing text mining techniques, the study aims to extract the essential information and sentiments expressed in the comments, in order to explore the consumption characteristics of Grade 1 energy-saving refrigerators. Moreover, the LBBA (LDA-Bert-BiLSTM-Attention) model is utilized to investigate the consumer topics of interest and emotional features. Initially, the LDA model is adopted to identify the attributes and weights of consumer concerns. Subsequently, the Bert model is pre-trained with the online comment data, and combined with the BiLSTM algorithm and Attention mechanism to predict sentiment categories. Finally, a transfer learning approach is utilized to determine the sentiment inclination of user-generated online comments and to identify the primary driving factors behind each sentiment category. This research employs sentiment analysis on online comments data regarding energy-saving products to uncover consumer sentiment attributes and emotional characteristics. It provides decision-makers with a comprehensive and systematic understanding of public consumption intentions, offering decision support for the efficient operation and management of the energy-saving product market.

EXPRESS: Gender and Serious Drug Recalls: a Textual Sentiment Analysis of Drug Reviews on WebMD

Consumers taking prescription drugs have limited ability to ascertain drug quality before taking the drug. After drug use, however, consumers frequently report their personal experiences with prescription drugs on one of the world’s largest medical websites: WebMD. Drug reviews on WebMD are a potentially rich source of free-form text that can be utilized to inform firms, consumers, researchers, and the Food and Drug Administration (FDA) about the quality and safety of prescription drugs. Additionally, because men and women communicate in starkly different ways, the gender of the reviewer may play a key role in drug reviews signaling drug quality problems. We examine if drug review textual sentiment is associated with the hazard of a serious drug recall and whether this relationship varies depending on the gender of the reviewer. We analyze textual sentiment on drug reviews from WebMD along with 13 years of drug recall data using several hazard models. We find that the more negative the drug review sentiment, the greater the hazard of a serious recall on that drug. This relationship is completely explained by drug reviews written by females; reviews written by males have no explanatory power. Our findings are confirmed by numerous robustness checks. In post-hoc analysis, we explore possible mechanisms by comparing female and male adverse events on the recalled drugs in our study. Our contributions to gender diversity and drug quality literature leads to implications for the FDA, WebMD, and firms that manufacture prescription drugs.

Theme Exploration and Sentiment Analysis of Online Reviews of Wuyishan National Park

The study aimed to explore the relationship and interaction between humans and nature in specific areas. Latent Dirichlet allocation topic recognition and SnowNLP sentiment analysis were used to extract the topics and analyze the sentiments from visitors’ online reviews of Wuyishan National Park. The conclusions were as follows: (1) The tourists mainly expressed positive emotions toward Wuyishan National Park, and the tourists acknowledged its ecological environment and natural and cultural heritage value. (2) The tourists’ comments focused on four themes: tourism activities and facilities, natural and cultural heritage value, characteristic tourism products, and tourism management and services. Natural experience was the main tourism activity in Wuyishan National Park, while cultural activities were related to the tea culture. (3) The tourist facilities, ticket and reservation mechanism, and management and services of Wuyishan National Park were the main concerns of the tourists. The study suggested that Wuyishan National Park could be transformed from a tourist destination into a comprehensive national park that provides recreational experiences and environmental education. This should be conducted by (1) developing detailed natural and cultural education and experience products and (2) improving public service functions and enhancing the public welfare of the national park.

Drug Recommendation System Based on Sentiment Analysis of Drug Reviews using Machine Learning

People are self-medicating more during the COVID-19 pandemic because they can't get to good medical tools. This makes their health situations worse. This study suggests a drug suggestion system that uses machine learning and emotion analysis of patient reviews to make the job of healthcare workers easier. We use different vectorization methods, like Bag of Words (BoW), TF-IDF, Word2Vec, and Manual Feature Analysis, to guess how people feel about certain diseases and suggest the best drugs for them. We use classification methods, such as LinearSVC, to rate emotions based on their accuracy, F1-score, precision, and AUC score. The results show that LinearSVC with TF-IDF vectorization works well, as it achieved 93% accuracy, which was better than other models. By making drug suggestions, this system aims to make it easier for people to get medical care when they need it most, especially during emergencies.

Hybrid Framework Integrating Lexicon and Learning Methods for Enhancing Sentiment Analysis Based on Patients' Tweets on Medicines

- Sentiment analysis is the act of locating and categorizing the emotions conveyed in text data using text analysis tools. Previous studies have demonstrated the great potential for sentiment analysis of medicine reviews to provide useful data that will aid healthcare professionals and businesses in assessing the safety of pharmaceuticals after they have been sold. These details protect patients and strengthen their confidence in healthcare professionals. Existing frameworks for opinion investigation within the restorative field either take two types of methods one is lexicon methods and the other one is machine learning models. Learning-based approaches need annotated data, whereas lexicon-based approaches are domain-specific and have a smaller range of applications. To improve outcomes, this study employs a hybrid methodology that fusion machine and deep learning models with lexicon techniques. The reviews are annotated using all-purpose emotion lexicons like TextBlob and SenticNet. To extract meaningful features, we are using feature engineering methods TF and TF- IDF. Last but not least, classification tasks are carried out using learning models such as machine learning models and deep learning models for biomedical text. The execution of the prospective combined technique is appraised using performance metrics. According to experimental findings, combining lexicon- and learning-based techniques yields superior outcomes over using them alone. Additionally, TextBlob has demonstrated impressive results, providing an accuracy of 97% with LSTM-CNN and the Bio Bert model when used to a dataset of medication reviews, as well as 95% accuracy when used with TF and the logistic regression model. Additionally, TextBlob provides an accuracy of 94% when combined with TF and LSTM, and provides an accuracy of 97% when using with Bio Bert Model on a dataset including tweets.

Sentiment Analysis Siap Kerja Training Services with Naive Models Bayes and SVM

Training service Siap Kerja has an important function in improving the quality of human resources and developing a competent workforce in various industrial sectors. To ensure trainee success and satisfaction, sentiment analysis in trainee reviews is essential to evaluate the strengths and weaknesses of the service offered. This research intends to compare the performance of algorithms Support Vector Machine (SVM) and Naive Bayes in sentiment analysis of trainee reviews on pages service training Siap Kerja. Review data was collected using web scraping on the Siap Kerja training service page by collecting ratings and reviews of training participants after completing the training they attended on the page training service Siap Kerja. Methods of data pre-processing, data sharing, feature extraction, model training, model evaluation, and results analysis were used in this research. The research results show that the SVM model has higher accuracy (0.93) compared to the Naive model Bayes (0.80). Additionally, positive sentiment was found at 99%, negative at 0.3%, and neutral at 0.7%. ased on the results of this research, it is recommended that the SVM model be used in sentiment analysis in the context of Siap Kerja training services and that the model be optimized to improve performance in classifying reviews with negative or neutral feelings.

Deep learning-based method for sentiment analysis for patients' drug reviews.

This article explores the application of deep learning techniques for sentiment analysis of patients' drug reviews. The main focus is to evaluate the effectiveness of bidirectional long-short-term memory (LSTM) and a hybrid model (bidirectional LSTM-CNN) for sentiment classification based on the entire review text, medical conditions, and rating scores. This study also investigates the impact of using GloVe word embeddings on the model's performance. Two different drug review datasets were used to train and test the models. The proposed methodology involves the implementation and evaluation of both deep learning models with the GloVe word embeddings for sentiment analysis of drug reviews. The experimental results indicate that Model A (Bi-LSTM-CNN) achieved an accuracy of 96% and Model B (Bi-LSTM-CNN) performs consistently at 87% for accuracy. Notably, the incorporation of GloVe word representations improves the overall performance of the models, as supported by Cohen's Kappa coefficient, indicating a high level of agreement. These findings showed the efficacy of deep learning-based approaches, particularly bidirectional LSTM and bidirectional LSTM-CNN, for sentiment analysis of patients' drug reviews.

SENTIMENTAL ANALYSIS ON TOURISM REVIEWS

Sentiment analysis plays a pivotal role in understanding the sentiments and opinions expressed in textual data, offering valuable insights into various domains, including tourism. In this study, we present a comprehensive review of sentiment analysis techniques applied to tourism reviews using machine learning algorithms. The abundance of user-generated content on tourism platforms has made sentiment analysis an indispensable tool for businesses and researchers alike. By leveraging machine learning algorithms, researchers can extract sentiments from vast amounts of textual data efficiently and accurately. This review outlines the key methodologies and approaches utilized in sentiment analysis of tourism reviews. It discusses preprocessing techniques such as text tokenization, stop-word removal, and stemming, which are crucial for preparing textual data for analysis. Furthermore, it examines various machine learning algorithms employed for sentiment classification, including Naive Bayes, Support Vector Machines, and Recurrent Neural Networks. Additionally, the review delves into feature extraction methods such as bag-of-words, TF-IDF, and word embeddings, highlighting their impact on sentiment analysis accuracy. Moreover, it explores the challenges and limitations associated with sentiment analysis in the tourism domain, such as sarcasm detection and language nuances.

Aspect-Level Sentiment Analysis of Customer Reviews Based on Neural Multi-task Learning

In the era of big data, major e-commerce platforms are facing the challenge of an exponential growth in the number of user comments. Effectively utilizing these comments has become an urgent issue. Traditional manual statistical methods are no longer able to meet the demands for accuracy and real-time analysis. The rise of artificial intelligence-based text mining techniques provides a new approach to address this problem. By building deep learning analysis models, it is possible to uncover user preferences and product characteristics, helping businesses proactively adjust sales strategies, improve product and service quality, and achieve precise marketing. Text sentiment analysis is one of the important research directions in the field of text mining. Its basic idea is to transform subjective texts with emotional color into structured data, and then use machine learning, deep learning, and other artificial intelligence technologies to extract emotional features and discover knowledge patterns. This article proposes a deep learning-based sentiment analysis model, which includes a shared sentiment prediction layer used to transfer emotional knowledge between different aspect categories and alleviate the problem of insufficient data. The model consists of two parts: the Aspect Category Detection (ACD) classifier based on attention mechanism and the Aspect Category Sentiment Analysis (ACSA) classifier. The ACD part generates word weights using attention mechanism, while the ACSA part predicts the sentiment of words and combines weights to determine the sentiment of aspect categories within sentences.

Airline reviews processing: Abstractive summarization and rating-based sentiment classification using deep transfer learning

Opinion summarization and sentiment classification are key processes for understanding, analyzing, and leveraging information from customer opinions. The rapid and ceaseless increase in big data of reviews on e-commerce platforms, social media, or review portals becomes a stimulus for the automation of these processes. In recent years, deep transfer learning has opted to solve many challenging tasks in Natural Language Processing (NLP) relieving the hassles of exhaustive training and the requirement of extensive labelled datasets. In this work, we propose frameworks for Abstractive Summarization (ABS) and Sentiment Analysis (SA) of airline reviews using Pretrained Language Models (PLM). The abstractive summarization model goes through two finetuning stages, the first one, for domain adaptation and the second one, for final task learning. Several studies in the literature empirically demonstrate that review rating has a positive correlation with sentiment valence. For the sentiment classification framework, we used the rating value as a signal to determine the review sentiment, and the model is built on top of BERT (Bidirectional Encoder Representations from Transformers) architecture. We evaluated our models comprehensively with multiple metrics. Our results indicate competitive performance of the models in terms of most of the evaluation metrics.

Sentiment analysis of movie reviews based on NB approaches using TF–IDF and count vectorizer

Sentiment analysis of movie reviews based on NB approaches using TF–IDF and count vectorizer

Attribute-level Sentiment Analysis Incorporating Auxiliary Information

In order to obtain the sentiment tendency of multiple attributes contained in the data reviews, this paper uses an attribute-level sentiment analysis model RBT-BiAtt-GCN, which incorporates auxiliary information and attention mechanism, to analyze the attribute-level sentiment analysis of e-commerce reviews of citrus. The model incorporates auxiliary information, attention mechanism, and GCN network on the basis of the previous one, and the final macro F1 value obtained can reach 78.21%, which achieves good results in comparison with other models.

Features level sentiment mining in enterprise systems from informal text corpus using machine learning techniques

ABSTRACT This study explores feature-level sentiment analysis of Hindi blog reviews in enterprise systems, a significant area in the Indian context yet understudied. By applying machine learning techniques like SVM across unigram, bigram, trigram, and n-gram models, and combining Lexicon-based methods with machine learning algorithms, we aim to enhance sentiment classification for better customer relationship management and product development. Contrasting with document-level approaches, our method focusing on bigrams achieves a test accuracy of 75%, offering a scalable model for enterprises to extract detailed customer insights from informal text, thereby aiding informed decision-making in a multicultural environment.

Intelligent Analysis of E-commerce Reviews based on Complex-Valued Spatio-temporal Graph Convolutional Neural Network Optimized Tyrannosaurus Optimization Algorithm

Intelligent analysis of E-commerce reviews based on multimodal social media big data using applies the benefit of innovative artificial intelligence method, especially deep learning algorithms, to study and realize web-based product consider and social network data connected to e-commerce. In this manuscript, Intelligent Analysis of E-commerce reviews based on Complex-Valued Spatio-temporal Graph Convolutional Neural Network Optimized Tyrannosaurus Optimization Algorithm (IAER-CVSGCNN-TOA)is proposed for Intelligent Analysis of E-commerce Reviews. The input data are collected from E-commerce big Dataset. Then data are pre-processing utilizing federated neural collaborative filtering (FNCF) to remove the noise and clean the data. The pre-processed data is provided to the CVSGCNN is used for intelligent analysis of e-commerce reviews. In general, CVSGCNN does no express adapting optimization approaches to determine optimal parameters to ensure accurate prediction. Hence, proposed to utilize the Tyrannosaurus Optimization Algorithm enhancement CVSGCNN for intelligent analysis of e-commerce reviews. The proposed IAER-CVSGCNN-TOA method is implemented on python. Then performance of proposed technique is analyzed with other existing techniques. The proposed technique attains 30.26%, 28.70% and 46.25% higher accuracy, 20.53%, 32.60%, and 53.43% higher precision, 25.23%, 23.15%, and 22.33%higher recall, 25.75%, 44.30% and 53.70%higher specificity comparing with the existing methods such as a Retracted: Online Troll Reviewer Detection Utilizing Deep Learning Methods (IAER-CNN), Developing an Intelligent System and DL Algorithms for Sentiment Analysis of E-Commerce Product Reviews(IAER-LSTM), Intelligent Perception System of Big Data Decision in Cross-Border e-Commerce Depend on Data Fusion(IAER-DFN) respectively.

Online Reviews Sentiment Analysis Based on High-Rise Residential Projects in Malaysia

The Malaysian property industry has suffered due to increasing numbers of unsold properties, especially high-rise residential types. Industry practitioners believed the absence of accurate market data for developers caused a mismatch where the developer failed to provide the right properties for the market demand. Thus, a crucial need to understand public opinion on sentiment and preferences for high-rise property. This study derived an online-reviews-based process for evaluating public sentiment and preferences to understand the reason behind the imbalances of supply and demand currently faced by the property industry in Malaysia. The present study introduced an alternative approach to the evaluation of public sentiments and preferences towards high-rise residential via sentiment analysis and interpreted the cause of positive and negative opinions via Term Frequency-Inverse Document Frequency (TF-IDF) analysis. To demonstrate the applicability of the proposed approach, an experiment based on multiple high-rise properties as a case study. The findings are expected to be employed by developers/government in the evaluation of their weaknesses and strengths on the high-rise properties project and how developers/government can improve certain areas.

Comparison of Naïve Bayes and SVM in Sentiment Analysis of Product Reviews on Marketplaces

At this time more and more people are switching to shopping online in existing marketplaces such as Shopee. Marketplaces provide various advantages and disadvantages to customers such as lower costs and goods sent not according to orders. Product reviews from customers greatly affect the sales level of business people so that sentiment analysis is carried out. The importance of conducting sentiment analysis of product reviews in the marketplace is to add an overview of how the product is received by users. This research uses Naïve Bayes and SVM algorithms for sentiment analysis of beauty care product review datasets obtained from Shopee scraping results. This research implements k fold cross validation for data splitting process of 10 folds. The Naïve Bayes algorithm obtained the highest accuracy value of 85.53% on fold 2 and the lowest accuracy value of 77.16% on fold 3. While the SVM algorithm obtained the highest accuracy value of 88.58% on fold 2 and the lowest accuracy value of 82.99% on fold 7. With this it is stated that SVM can work better for sentiment analysis of beauty care product reviews on the Shopee marketplace because it gets a higher average accuracy value of 86.14% compared to the Naïve Bayes algorithm.

Drug Recommendation System on Sentiment Analysis of Drug Reviews by Using Machine learning

Abstract: With the healthcare system facing increased challenges due to the COVID-19 pandemic, there's a big demand for new ideas to help doctors and nurses. This paper suggests a new way of using computers to help doctors decide which medicines to give to patients. By using smart computer programs, we can make it easier for healthcare workers to handle their workload and provide better care for patients. By analyzing patient reviews, we employ sentiment analysis using advanced vectorization methods like Bag of Words, Term Frequency-Inverse Document Frequency (TF-IDF), and Manual Feature Analysis. These techniques allow for the identification of subtle sentiment nuances crucial for tailoring personalized drug recommendations. We utilize various classification algorithms such as Naive Bayes, Support Vector Classifier (SVC), and Random Forest to predict sentiments. Among these, the LinearSVC classifier coupled with TF-IDF vectorization demonstrates superior performance in sentiment prediction. We tested our system using various measures like precision, recall, F1-score, and Area Under the Curve (AUC) to ensure its effectiveness. By incorporating advanced machine learning techniques, our system offers a strong foundation for enhancing the accuracy of drug prescriptions, which is a key issue in today's healthcare. Through better drug recommendations, our goal is to ultimately enhance patient health outcomes and play a part in advancing healthcare practices, especially in the face of complex and evolving healthcare challenges.

Positive or Negative Sentence Feedback Identifier

Abstract: The "Positive or Negative Sentence Feedback Identifier Project" is a natural language processing (NLP) initiative designed to automatically classify and analyse sentences or text- based feedback as either positive or negative. This project aims to streamline sentiment analysis and opinion categorization by employing machine learning and NLP techniques. Its potential applications include sentiment analysis in customer reviews, social media monitoring, and opinion mining, ultimately providing valuable insights to businesses and individuals. It's crucial to gauge the overall sentiment and tone of the feedback. Positive feedback typically conveys satisfaction, using enthusiastic language and specific compliments about at's working well. In contrast, negative feedback often carries a critical tone, highlighting issues or areas of concern with the project. Pay attention to emotional indicators, the specificity of comments, and whether the feedback offers constructive suggestions for improvement. Evaluating feedback within the context of the project's goals and objectives is essential to understanding its impact and guiding necessary actions for project enhancement. Enhance security with two-factor authentication and encryption, while implementing user authentication and authorization controls. Integrate third-party services for expanded functionality and ensure mobile responsiveness for an improved user experience. Explore AI for automation and data analysis, offer robust analytics, and gather user feedback. Prioritise accessibility, optimise performance, and gamify the project with badges and leaderboards. Enable offline functionality, foster community engagement, and align features with userneeds and scalability goals. The project's development encompasses a comprehensive range of solutions, including the implementation of a sophisticated sentiment analysis model, efficient data collection and preprocessing methods, and the incorporation of scalable real-time processingarchitecture. Furthermore, the project's innovative facets encompass a dynamic blend of emotion analysis, contextual analysis, and predictive sentiment analysis, which significantlyaugment its adaptability and effectiveness across diverse domains and use cases.