Sentiment Analysis Of Reviews Research Articles

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.

The world from the perspective of digital nomads: exploring sentiments in destination reviews

PurposeThis study aims to explore the emotional landscapes and spatial preferences of digital nomads, focusing on how sentiments expressed in destination reviews influence their mobility and destination choices.Design/methodology/approachEmploying a lexicon-based sentiment analysis of social media comments and reviews, alongside advanced geographical information systems (GIS) mapping techniques, the study analyzes the emotional tones that digital nomads associate with various destinations worldwide.FindingsThe analysis reveals significant patterns of emotional sentiments, with trust and joy being predominant in preferred destinations. Spatial patterns identified through GIS mapping highlight the global distribution of these sentiments, underscoring the importance of emotional well-being in destination choice.Practical implicationsInsights from this study offer valuable guidance for Destination Management Organizations (DMOs) in strategic planning, enhancing destination appeal through targeted marketing strategies that resonate with the emotional preferences of digital nomads.Originality/valueThis research introduces a novel approach by integrating sentiment analysis with GIS to map the emotional and spatial dynamics of digital nomadism, contributing a new perspective to the literature on tourism and mobility.

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.

Analyzing Amazon Products Sentiment: A Comparative Study of Machine and Deep Learning, and Transformer-Based Techniques

In recent years, online shopping has surged in popularity, with customer reviews becoming a crucial aspect of the decision-making process. Reviews not only help potential customers make informed choices, but also provide businesses with valuable feedback and build trust. In this study, we conducted a thorough analysis of the Amazon reviews dataset, which includes several product categories. Our primary objective was to accurately classify sentiments using natural language processing, machine learning, ensemble learning, and deep learning techniques. Our research workflow encompassed several crucial steps. We explore data collection procedures; preprocessing steps, including normalization and tokenization; and feature extraction, utilizing the Bag-of-Words and TF–IDF methods. We conducted experiments employing a variety of machine learning algorithms, including Multinomial Naive Bayes, Random Forest, Decision Tree, and Logistic Regression. Additionally, we harnessed Bagging as an ensemble learning technique. Furthermore, we explored deep learning-based algorithms, such as CNNs, Bidirectional LSTM, and transformer-based models, like XLNet and BERT. Our comprehensive evaluations, utilizing metrics such as accuracy, precision, recall, and F1 score, revealed that the BERT algorithm outperformed others, achieving an impressive accuracy rate of 89%. This research provides valuable insights into the sentiment analysis of Amazon reviews, aiding both consumers and businesses in making informed decisions and enhancing product and service quality.

Sentiment Analysis of Mobile Provider Application Reviews Using Naive Bayes Algorithm and Support Vector Machine

To choose a mobile provider to use, prospective users often rely on reviews left by previous users of the mobile provider application. One source of information for finding reviews of cellular provider applications is the Google Play Store. The purpose of this research is to analyze user reviews of cellular provider applications and find out the comparison of the accuracy levels of the two algorithms to be used, namely the Naïve Bayes Classification (NBC) and Support Vector Machine (SVM) algorithms. The object of this research is focused on the three most popular applications in Indonesia, according to the Goodstate website, namely Telkomsel, IM3, and XL Axiata. After testing using the Naïve Bayes Clasification method, the accuracy value obtained in the MyTelkomsel application is 75%, MyIM3 is 80%, and MyXL is 72%. While the Support Vector Machine method obtained an accuracy value of 77% for MyTelkomsel, 80% for MyIM3, and 76% for MyXL.

Comparison of Naive Bayes Classifier and K-Nearest Neighbor Algorithms with Information Gain and Adaptive Boosting for Sentiment Analysis of Spotify App Reviews

Abstract. At this time, the development of technology are increase rapidly. One of the issue that appear with advance technology is data volume in the world has increase too. With the large data volumes that exist in the world it can be used to some purpose in many field. Entertainment is one of the field that have many interest from user in this world. Spotify is the example of entertainment apps that provided by Google Play Store to give online music streams to their users. Because that apps is provided by Google Play Store, many reviews of the user about the apps it can be classified to know the positive, negative, or neutral. One way to classified the review of user is make sentiment analysis. In this paper, to classify the review we use naïve Bayes classifier and k-nearest neighbors that will be compared with adding Information gain as feature selection and adaptive boosting as boosting algorithm of each classification algorithm that we used. The result of classification using naïve Bayes classifier with adding Information gain and adaptive boosting is 87.28% and k-nearest neighbor with adding information gain and adaptive boosting can perform accuracy of 80.35%. Purpose: Knowing the result each of accuracy from the naïve Bayes classifier and k-nearest neighbor algorithm with adding information gain and adaptive boosting that we used and know how to doing the sentiment analysis step by step with the methods that chosen in this study. Methods/Study design/approach: This study applied data preprocessing, lexicon based labelling with TextBlob, Normalization, Word Vectorization using TF-IDF, and classification with naïve Bayes classifier and k-nearest neighbor, information gain as feature selection, and adaptive boosting as boosting algorithm to boost the accuracy of classification result. Result/Findings: The accuracy of naïve Bayes classifier with adding information gain and adaptive boosting is 87.28%. Meanwhile, by k-nearest neighbor with adding information gain and adaptive boosting reach the accuracy of 80.35%. This result obtained by using 60.000 dataset with data splitting 80% as data training and 20% as data testing. Novelty/Originality/Value: Implementing information gain as feature selection and adaptive boosting as boosting algorithm to naïve Bayes classifier is prove that it can be increase the accuracy of classification, but not same when implementing in k-nearest neighbor. So, for the future research can applied another classification algorithm or feature selection to get better result.

Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment

Sentiment analysis of product reviews is critical in understanding customer views and satisfaction, especially in the context of e-commerce applications. A marketplace provides channels where users can submit reviews of the products they purchase. However, due to the large number of reviews in a marketplace, analyzing them is no longer feasible to be performed manually. This research proposes a machine learning implementation to perform sentiment analysis on product reviews. In this research, the product review dataset on Shopee marketplace is used for sentiment analysis by comparing TF-IDF and TF-RF feature extraction using the SVM algorithm with stages of dataset, labeling, feature extraction and accuracy results. The importance of the comparison between TF-IDF and TF-RF feature extraction in this research is related to the need to evaluate and determine which feature extraction method is most effective in increasing the accuracy of sentiment analysis. TF-IDF and TF-RF are two methods commonly used in text analysis, and a comparison of their performance can provide deep insight into the effectiveness of each in the context of product sentiment analysis.Thus, through this comparison, this research aims to determine the best approach that can provide the highest accuracy results, so that the results can serve as a guide for further research. Based on the evaluation, the highest accuracy value is achieved at 92.87% by using TF-IDF and SVM classifiers which outperformed previous research.

Movie Review Sentiment Analysis and AI-Story Generation Web Application

Abstract: This paper presents a web application designed to enhance the interaction between users and multimedia content through two innovative platforms: "MOVIE REVIEW SENTIMENT ANALYSIS" and "AI-STORY GENERATION." The first platform leverages the Fastai library and LSTM networks to perform sentiment analysis on user-submitted movie reviews, determining their polarity as positive or negative. This analysis directly influences the movie's aggregate rating, with the system capable of processing extensive textual input with high accuracy. Ratings are dynamically updated and stored in JSON format. The second platform, AI-STORY GENERATION, introduces a creative avenue for generating novel stories based on user prompts. It utilizes Natural Language Processing (NLP) techniques and a Generative Pretrained Transformer (GPT-2 Large) model, trained on tokenized movie scripts, to produce coherent and engaging narratives. The Flask web framework supports the application's backend, providing a robust and scalable foundation for user interaction and content delivery. This dual-platform approach not only demonstrates the practical application of advanced machine learning and NLP techniques in enhancing digital entertainment experiences but also showcases the potential for AI-driven tools to foster creative storytelling. The integration of sentiment analysis and story generation within a single application illustrates a novel use case of AI in the context of web-based entertainment, potentially paving the way for more personalized and interactive user experiences

Sentiment Analysis of Application Reviews using the K-Nearest Neighbors (KNN) Algorithm

Sentiment Analysis of Application Reviews using the K-Nearest Neighbors (KNN) Algorithm

Machine learning and deep learning-based sentiment analysis of IMDB user reviews

Movie reviews have always been a popular and enduring subject of interest among researchers. Sentiment analysis plays a significant role in this domain. The utilization of machine learning and natural language processing techniques can provide valuable insights into the emotional responses of audiences towards movies, as well as facilitate the appraisal of their reputation and market potential. This is achieved through the analysis of sentiment expressed in movie reviews. Furthermore, this approach is highly valuable in various application domains such as data mining, web mining, and social media analysis. This paper aims to conduct a comparative analysis by utilizing typical models based on machine learning and neural networks, along with the integration of natural language processing techniques. The IMDB database, which contains 50,000 reviews, will be used, and data preprocessing will be performed before applying these models. By comparing the accuracy of each model, insights regarding movie reviews can be derived.