Optimizing Categorical Boosting Using Modified Metaheuristic for Review Sentiment Analysis

To analyze public perceptions of over-the-counter (OTC) hearing aids through sentiment and thematic analysis of online consumer reviews and their changes over time. Sentiment and thematic analysis. Online reviews from third-party and product websites. All English online consumer reviews posted between 2016 and 2024 for OTC hearing aids (83 models) were recorded (n = 21,727). Sentiment analysis was performed using Valence Aware Dictionary and Sentiment Reasoner (VADER), a rule-based sentiment analysis tool incorporating natural language processing. VADER provides scores for each review ranging from -1 (most negative), 0 (neutral), to 1 (most positive). Additional thematic analysis was performed for the top 100 most positive, neutral, and negative reviews (n = 300). Overall, mean (SD) VADER sentiment score of online reviews was generally positive at 0.587 (0.411). Multivariable regression analysis showed that higher VADER scores were associated with higher-priced and behind-the-ear (BTE) type hearing aids. Although there was a significant increase in a number of reviews after the Food and Drug Administration's new establishment of the OTC hearing aid category in 2022, the mean sentiment scores slightly decreased (β =-.10, [95%CI: -0.12 to -0.09]). Thematic analysis revealed that positive sentiments highlighted the affordability and time-saving benefits of OTC hearing aids as alternatives to prescription models. Negative sentiments centered on sound quality, challenges with customer service, and inadequate amplification for those with severe hearing loss. Customers generally viewed OTC hearing aids positively, while mixed experiences were present. When used as indicated for adults with mild to moderate hearing loss, OTC hearing aids may offer a viable alternative to prescription devices, improving accessibility and affordability.

Sentiment Analysis is a method to identify, extract, and quantify people's feelings, opinions, or attitudes. The wealth of online data motivates organizations to keep tabs on customers' opinions and feelings by turning to sentiment analysis tasks. Along with the sentiment analysis, the emotion analysis of written reviews is also essential to improve customer satisfaction with restaurant service. Due to the availability of massive online data, various computerized methods are proposed in the literature to decipher text sentiments. The majority of current methods rely on machine learning, which necessitates the pre-training of large datasets and incurs substantial space and time complexity. To address this issue, we propose a novel unsupervised sentiment classification model. This study presents an unsupervised mathematical optimization framework to perform sentiment and emotion analysis of reviews. The proposed model performs two tasks. First, it identifies a review's positive and negative sentiment polarities, and second, it determines customer satisfaction as either satisfactory or unsatisfactory based on a review. The framework consists of two stages. In the first stage, each review's context, rating, and emotion scores are combined to generate performance scores. In the second stage, we apply a non-cooperative game on performance scores and achieve Nash Equilibrium. The output from this step is the deduced sentiment of the review and the customer's satisfaction feedback. The experiments were performed on two restaurant review datasets and achieved state-of-the-art results. We validated and established the significance of the results through statistical analysis. The proposed model is domain and language-independent. The proposed model ensures rational and consistent results.

Sentiment analysis, or opinion mining, is a key area of natural language processing that identifies sentiments in free text. As digital business services grow and user-generated content increases, analyzing sentiments in online reviews is vital for enhancing business operations and customer satisfaction. This study focuses on sentiment analysis of user reviews from Google Reviews for Public Health Centers (PHCs) in Bali, Indonesia, using five machine learning models: Logistic Regression, Support Vector Machine (SVM), XGBoost, Naive Bayes, and Random Forest. These models classified sentiments into positive and negative categories using a dataset balanced with SMOTE to improve accuracy. We divided a total of 1.834 reviews, using 20% for testing and 80% for training, to ensure a thorough evaluation under real-world conditions. Logistic Regression and Naive Bayes performed best, both achieving an accuracy of 0.89, with Logistic Regression providing a balanced precision and recall. The study enhances academic understanding of sentiment analysis in healthcare and offers insights for business administrators on handling online customer feedback. The findings stress the importance of choosing suitable machine learning techniques based on specific data characteristics and project requirements to optimize both technological and business outcomes.

A movie review can be considered as an essential data source for both movie producers and potential consumers. The reviews are usually used as a benchmark for knowing the movie’s quality and are utilized as references to whether the film is worth watching. The study of sentiment analysis has received more attention from researchers. This current study aims to classify the sentiment analysis of movie reviews obtained from the IMDb site. The support vector machine (SVM) method was employed to classify the movie review’s sentiments. Meanwhile, the radial basis function (RBF) kernel and information gain (IG) were used to enhance classification. Feature selection was conducted by removing irrelevant features and selecting features with a strong correlation to classification. The IG algorithm was used for feature selection. The current study revealed that the classification accuracy of movie review sentiment analysis using the SVM algorithm, SVM + RBF kernel, and SVM+RBF Kernel+IG are 81.50%, 82.25%, and 87.25%, respectively.

Sentiment analysis of product reviews plays a crucial role in understanding consumer feedback, improving customer experience and making informed business decisions. This paper explores the application of machine learning and deep learning algorithms to effectively classify and analyse the sentiment of product reviews. Traditional machine learning techniques, such as Naïve Bayes, Support Vector Machines (SVM) and Random Forests are employed for sentiment classification based on manually engineered features. Simultaneously, deep learning approaches like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are leveraged to automatically learn complex representations from raw text data. The study compares the performance of these methods in terms of accuracy, precision, recall and F1-score. Additionally, pre-trained language models such as BERT are incorporated to enhance contextual understanding. Experimental results demonstrate that deep learning models particularly LSTM and BERT, outperform traditional machine learning techniques in capturing sentiments. This analysis provides valuable insights into the effectiveness of different algorithms in sentiment analysis tasks, paving the way for more advanced applications in natural language processing and customer sentiment evaluation.

Technological advances in online transportation services such as Grab facilitated user mobility. User reviews of the application were a valuable source of information for developers to improve service quality and for users to make decisions regarding service use. This research aimed to analyze the sentiment of Grab application user reviews using a machine learning approach. The system development method used in this research was the Agile method with the stages of Planning, Iterative Development, and Testing. The machine learning algorithms applied were Random Forest, Support Vector Machine (SVM), and Naive Bayes. The results of sentiment analysis of Grab application reviews were in the form of classification of reviews into positive, neutral, and negative sentiments. The test results showed that the Random Forest algorithm had the highest accuracy rate of 95.14%. This indicated that Random Forest was effective in identifying sentiment patterns in review data.

This study focuses on sentiment analysis of Spotify application reviews on Google Play Store using the Naive Bayes algorithm. As a leading music streaming platform, Spotify receives diverse user feedback that reflects their experiences, complaints, and satisfaction. Sentiment analysis aids in understanding user opinions, enhancing services, and innovating features. The research involves collecting user reviews via web scraping, followed by preprocessing steps such as text cleaning, tokenization, normalization, stopword removal, and stemming. The Term Frequency-Inverse Document Frequency (TF-IDF) method is employed to assign weights to words, highlighting their significance in reviews. The Naive Bayes algorithm categorizes sentiments into positive, negative, and neutral classes. Performance evaluation uses a confusion matrix to measure accuracy, precision, recall, and F1-score. Results indicate that Naive Bayes effectively classifies large volumes of unstructured data with high accuracy and efficiency. This study contributes practically by offering actionable insights to improve Spotify's services and theoretically by advancing sentiment analysis methodologies using machine learning. The findings highlight the algorithm's potential to understand user needs and address issues, reinforcing its value in text analytics for mobile applications.

Sentiment analysis of customer reviews has a crucial impact on a business's development strategy. Despite the fact that a repository of reviews evolves over time, sentiment analysis often relies on offline solutions where training data is collected before the model is built. If we want to avoid retraining the entire model from time to time, incremental learning becomes the best alternative solution for this task. In this work, we present a variant of online random forests to perform sentiment analysis on customers' reviews. Our model is able to achieve accuracy similar to offline methods and comparable to other online models.

The plethora of online reviews available on the internet has great insights about various products or entities. However, selecting or recommending a product by sifting through these reviews is a tedious and time consuming task. Sentiment analysis of such reviews helps understand the pros or cons of the product. In recent years researchers and industry people have leveraged such reviews to rank or recommend the products to new users. Ranking or recommending products using text reviews is a challenging task. In this study, we propose a framework to get a ranking of products using aspect-based sentiment analysis (ABSA). ABSA is a fine-grained sentiment analysis task that is useful for extracting opinions about the different aspects of a product. To facilitate automated ABSA, a novel approach for aspect sentiment classification is proposed in this study. Further to rank the products based on aspect-specific performance, a framework is developed using Multi-criteria decision-making (MCDM) techniques. A case study is performed in the automobile domain to rank various cars using the ABSA approach. To validate the effectiveness of the proposed approach, the ranking results are compared with the numeric ratings available for various aspects of the car. The experimental results show that the ranking obtained by the proposed approach matches the numerical ratings. Therefore, the car selection problem or recommendation of cars in the automobile domain can be effectively handled using the proposed framework.

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.

A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach

In today’s online services, users’ feedback such as numerical rating, textual review, time of purchase and so on for each product is often encouraged to provide. Many recommender systems predict the products that the users may like and purchase in the future using users’ historical ratings. With the increase of user data in the systems, more detailed and interpretable information about product features and user sentiments can be extracted from textual reviews that are relative to ratings. In this paper, we propose a novel topic and sentiment matrix factorization model, which leverages both topic and sentiment drawn from the reviews simultaneously. First, we conduct topic analysis and sentiment analysis on reviews using Latent Dirichlet Allocation (LDA) and a lexicon construction technique, respectively. Second, we combine the user consistency, which is calculated from his/her reviews and ratings, and helpful votes from other users on reviews to obtain a reliability measure to weight the ratings. Third, we integrate the obtained reliability measure and the results of the topic and sentiment analysis of reviews into the matrix factorization framework for prediction. Our experimental comparison using eight Amazon datasets indicates that the proposed method significantly improves performance compared to traditional matrix factorization up to 20.82%.

Information and communications technologies have enabled the rise of the phenomenon named sharing economy, which represents activities between people, coordinated by online platforms, to obtain, provide, or share access to goods and services. In hosting services of the sharing economy, it is common to have a personal contact between the host and guest, and this may affect users’ decision to do negative reviews, as negative reviews can damage the offered services. To evaluate this issue, we collected reviews from two sharing economy platforms, Airbnb and Couchsurfing, and from one platform that works mostly with hotels (traditional economy), Booking.com, for some cities in Brazil and the USA. Through a sentiment analysis, we found that reviews in the sharing economy tend to be considerably more positive than those in the traditional economy. This can represent a problem in those systems, as an experiment with volunteers performed in this study suggests. In addition, we discuss how to exploit the results obtained to help improve users’ decision making.

Sentiment analysis is a popular technique to identify the sentiment of a piece of text. Several different domains have been targeted by sentiment analysis research, such as Twitter, movie reviews, and mobile app reviews. Although several techniques have been proposed, the performance of current sentiment analysis techniques is still far from acceptable, mainly when applied in domains on which they were not trained. In addition, the causes of wrong classifications are not clear. In this article, we study how sentiment analysis performs on game reviews. We first report the results of a large-scale empirical study on the performance of widely used sentiment classifiers on game reviews. Then, we investigate the root causes for the wrong classifications and quantify the impact of each cause on the overall performance. We study three existing classifiers: <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stanford CoreNLP</monospace> , <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NLTK</monospace> , and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SentiStrength</monospace> . Our results show that most classifiers do not perform well on game reviews, with the best one being <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NLTK</monospace> (with an AUC of 0.70). We also identified four main causes for wrong classifications, such as reviews that point out advantages and disadvantages of the game, which might confuse the classifier. The identified causes are not trivial to be resolved and we call upon sentiment analysis and game researchers and developers to prioritize a research agenda that investigates how the performance of sentiment analysis of game reviews can be improved, for instance by developing techniques that can automatically deal with specific game-related issues of reviews (e.g., reviews with advantages and disadvantages). Finally, we show that training sentiment classifiers on reviews that are stratified by the game genre is effective.

of the Thesis Sideffective System to Mine Patient Reviews: Sentiment Analysis by Deepak Yalamanchi Thesis Director: Prof. Tomasz Imielinski Sideffective is the system to crawl, rank and analyze patient testimonials about side effects from common medications. Since the wealth of any mining model is the Data corpus, the data collection phase involved extensive crawling of massive medical websites comprised of user forums from the internet. Subsequently, the raw files were subjected to certain site-specific parsing routines, yielding outputs conforming to a well-defined data model. Currently, the system holds close to 400,000 user testimonials pertaining to more than 2500 drugs/medicines. Sideffective aims at gathering and aggregating this wealth of information, build useful associations and present interesting observations and numeric validations, all in a user-friendly interface. The important issues that we have tried to tackle are: Extracting side effects without relying on pre-built lists, aggregating distribution of different side effect for a give drug, site-specific search, ranking and determining the negativity of reviews. The system has been jointly built by Deepak Yalamanchi and Sangeetha Rajagopalan under the guidance of Prof. Tomasz Imielinski. This thesis focuses mainly on Sentiment Analysis of patient reviews. While most existing sentiment analysis systems are predicated by POS (parts of speech) tagging or Bayesian sentiment analysis methods, the same cannot be applied to medical reviews as they generally carry a negative flavor in them. We thereby approached the problem by identifying the features in the sentence and calibrating the sentiment on a Negativity Meter based on their relation to sentiment words. A