Sideffective - system to mine patient reviews: sentiment analysis

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.

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.

Effects of self energy of the ions on the double layer structure and properties at the dielectric interface

The desire for comprehending user behavior is significant, since social media data is growing quickly owing to user contributions, especially in light of the recent coronavirus outbreak. In the literature works, several semantic analysis methodologies are used for examining the user behavior and emotions from the corpus data. They continue to deal with the main issues of poor accuracy, a complex system, inadequate emotion recognition, and ineffectiveness. The main objective of this paper is to build a new model for sentiment analysis and opinion mining. When compared to the conventional works, the novel concept behind this work is, it adopts some unique computational methodologies for accurate sentiment prediction. Here, a new Cycle Generative Adversarial Network (GAN) based Term Memory integrated Sentiment Analysis (CyG-TMSA) framework is developed for analyzing and classifying user emotions from corpus data. The most accurate identification of emotions is made possible by the efficient generation of augmented data using the Cycle Generative Adversarial Network (GAN) model. Moreover, a classification technique based on Term Memory Allocation (TMA) is applied to accurately predict opinions derived from the data supplementation. The best way to approximate the weight value for classifying data is to use the Elite Golden Jackle Optimization (EGJO) technique, which enhances the classifier’s decision-making ability. To verify and contrast the effectiveness and results of the suggested CyG-TAMS model, multiple corpus data sets extracted from public repositories are employed. The proposed CyG-TMSA framework demonstrated great effectiveness in sentiment analysis via CycleGAN-based data augmentation, TMA for precise classification, and optimization of parameter tuning through GJO. In experimental evaluations conducted on datasets extracted from Amazon and Twitter, the model obtained high accuracy at 98% within a very short processing time of 10.12 s, significantly outclassing other conventional methods in terms of accuracy and computational efficiency.

The Impact of Arabic Part of Speech Tagging on Sentiment Analysis: A New Corpus and Deep Learning Approach

Sentiment analysis has been approached from a spectrum of methodologies, including statistical learning using labelled corpus and rule-based approach where rules may be constructed based on the observations on the lexicons as well as the output from natural language processing tools. In this paper, the experiments to transform labelled datasets by using NLP tools and subsequently performing sentiment analysis via statistical learning algorithms are detailed. In addition to the common data pre-processing prior to sentiment analysis, we represent the tokens in the datasets using Part-Of-Speech (POS) tags. The aim of the experiments is to investigate the impact of POS tags on sentiment analysis, particularly on both short texts and regular texts. The experimental results on short texts show that the combination of adjective and adverb predicts the sentiment of short texts the best. While noun is generally deemed to be neutral in sentiment polarity, the experimental results show that it helps to increase the accuracy of sentiment analysis on regular texts. Besides, the role of negation analysis in the datasets has also been investigated and reported based on the experimental results obtained.

Sentiment Analysis (SA) is a computational study that examines people’s opinions, attitudes, and opinions based on their written text. Keralites’ mother tongue, Malayalam, is the most often used language to express themselves on Twitter. As there is no automatic Sentiment Analyzer in Malayalam, the SA of Twitter messages is necessary. In this research work, a Malayalam sentiment analysis model is introduced. The input raw data in the form of reviews are fed for the pre-processing stage. The pre-processing module includes sentence tokenization via Sandi Splitting and parts of speech (POS) tagging. Subsequently, Bag of Word, the Proposed weightage-based Term Frequency-Inverse Document Frequency, Unigram with the dictionary and Unigram with dictionary including negation words have been considered for feature vector formation. Finally, the review classification is undergone via the proposed hybrid classifier, which is constructed by hybridizing the Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), respectively. To enhance the classification performance, the weight of the CNN classifier will be fine-tuned via an Improved Bumble bee mating Optimization (IBBMO), which is an advanced version of standard BBMO. The performance of the proposed work is compared over other conventional models in terms of positive, negative, convergence, and other measures. The accuracy of the proposed method is 95.436, which is much better than the existing works like DBN[Formula: see text]86.25, NN[Formula: see text]87.080, SVM[Formula: see text]87.234, WOA+Hybrid Classifier[Formula: see text]88.440, LA+Hybrid Classifier[Formula: see text]89.35285, CSO+Hybrid Classifier[Formula: see text]92.021, BBMO+Hybrid Classifier[Formula: see text]93.4248, and ML techniques[Formula: see text]94.014, at the 90th training percentage.

Based on Web 2.0 technology, more and more people tend to express their attitude or opinions on the Internet. Radical ideas, rumors, terrorism, or violent contents are also propagated on the Internet, causing several incidents of social panic every year in China. In fact, most of this content comprises joking or emotional catharsis. To detect this with conventional techniques usually incurs a large false alarm rate. To address this problem, this paper introduces a technique that combines sentiment analysis with background checks. State-of-the-art sentiment analysis usually depends on training datasets in a specific topic area. Unfortunately, for some domains, such as violence risk speech detection, there is no definitive training data. In particular, topic-independent sentiment analysis of short Chinese text has been rarely reported in the literature. In this paper, the violence risk of the Chinese microblogs is calculated from multiple perspectives. First, a lexicon-based method is used to retrieve violence-related microblogs, and then a similarity-based method is used to extract sentiment words. Semantic rules and emoticons are employed to obtain the sentiment polarity and sentiment strength of short texts. Second, the activity risk is calculated based on the characteristics of part of speech (PoS) sequence and by semantic rules, and then a threshold is set to capture the key users. Finally, the risk is confirmed by historical speeches and the opinions of the friend-circle of the key users. The experimental results show that the proposed approach outperforms the support vector machine (SVM) method on a topic-independent corpus and can effectively reduce the false alarm rate.

Single molecule detection and fluorescence correlation spectroscopy on surfaces

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.

The POS (part of speech) tagging is the basic work in natural language processing. The tagging precision will have an important effect on the result of latter process, such as syntax analysis. In this paper, a Chinese POS tagger based on the maximum entropy model is presented, which trains from a large corpus annotated with Chinese POS tags and assigns the best tag sequence to the Chinese sentence to be annotated. In this model, all the features that are useful to predicate the POS tags are mined to make the model closer to the real case. In addition, for the problem of overfitting, a smoothing method and a POS dictionary are maintained to reduce the model's dependence to training data and improve the efficiency of the search process. Open test results shows that the Chinese POS tagging with this method can achieve an accuracy of 96.8%.

Text emanated from users’ posts and comments on social media constitutes important piece of information for wide ranging Natural Language Processing (NLP) applications, such as Sentiment Analysis, Sarcasm Detection, Named Entity Identification, Question Answering and Information Retrieval (IR). Part–of–Speech (POS) tagging, a prerequisite for all such applications, augments tag information to the raw text. However, an inherent tendency of social media users to include multilingual contents in their posts, called code-mixing, poses challenge to POS tagging. Besides, intricate and free style writing add to the complexity of problem. To cope with the issue, a Hidden Markov Model (HMM) based supervised algorithm has been introduced for POS tagging of code–mixed Indian social media text. Publicly available social media text of Indian Languages (ILs), particularly English, Hindi, Bengali and Telugu, have been used to train and test the proposed system. Correctness of system annotated tags has been evaluated on ground of F-measure.

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.

Mobile eCommerce applications are increasingly becoming popular for shopping online in Africa, since the means of accessing the Internet is mostly through mobile devices. This presents an opportunity to explore and understand the key issues affecting African mobile eCommerce applications by performing sentiment analysis of users reviews from seven top African mobile eCommerce applications (i.e., Jiji Nigeria, Jumia, Jiji Kenya, Konga, Takealot, KiliMall and Jiji Uganda). We implement two sentiment analysis approaches, which are Linguistic Inquiry Word Count (LIWC) and Machine Learning (ML), to classify user reviews into positive or negative sentiment polarity. Specifically, we compare five ML algorithms and the LIWC with respect to their performance, and also conduct thematic analysis to uncover the positive and negative factors affecting African mobile eCommerce. Our results show that LIWC is the best performing method with 86.7% F1-score. Our thematic analysis reveals various business, legal, and technology issues, as well as positive factors such as ease of use, fast delivery time, and affordable items. Finally, we offer recommendations on how to tackle the negative issues.

Local area network and wide area network are different network protocols, small and medium enterprises due to the geographical distribution of discrete and interface diversity, leading to its needs to be compatible with different network protocols, it is difficult to use a single network protocol to realize network architecture, and due to the ipv4 address scarcity, making its application or maintenance cost greatly increased, based on this, this paper presents the design of multi-protocol network architecture based on point-to-point protocol and frame relay, and with the advantages of the router is compatible with a variety of interfaces and protocols to connect different network interfaces, the paper also gives the configuration of routing information protocol and static routing through local area network, the paper presents the core information of frame relay and point-to-point protocol among the routers, and the paper gives the realization of the network address translation in the end.