Aspect Level Songs Rating Based Upon Reviews in English

With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.

Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

Movies are the better source of entertainment.Every year,a great percentage of movies are released.People comment on movies in the form of reviews after watching them.Since it is difficult to read all of the reviews for a movie,summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews.Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data.Opinion mining involves identifying and extracting the opinions of individuals,which can be positive,neutral,or negative.The task of opinion mining also called sentiment analysis is performed to understand people’s emotions and attitudes in movie reviews.Movie reviews are an important source of opinion data because they provide insight into the general public’s opinions about a particular movie.The summary of all reviews can give a general idea about the movie.This study compares baseline techniques,Logistic Regression,Random Forest Classifier,Decision Tree,K-Nearest Neighbor,Gradient Boosting Classifier,and Passive Aggressive Classifier with Linear Support Vector Machines and Multinomial Naïve Bayes on the IMDB Dataset of 50K reviews and Sentiment Polarity Dataset Version 2.0.Before applying these classifiers,in pre-processing both datasets are cleaned,duplicate data is dropped and chat words are treated for better results.On the IMDB Dataset of 50K reviews,Linear Support Vector Machines achieve the highest accuracy of 89.48%,and after hyperparameter tuning,the Passive Aggressive Classifier achieves the highest accuracy of 90.27%,while Multinomial Nave Bayes achieves the highest accuracy of 70.69%and 71.04%after hyperparameter tuning on the Sentiment Polarity Dataset Version 2.0.This study highlights the importance of sentiment analysis as a tool for understanding the emotions and attitudes in movie reviews and predicts the performance of a movie based on the average sentiment of all the reviews.

An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier

The task of classifying opinions conveyed in any form of text online is referred to as sentiment analysis.The emergence of social media usage and its spread has given room for sentiment analysis in our daily lives.Social media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various fields.Various subject matter can be encountered on social media platforms,such as movie product reviews,consumer opinions,and testimonies,among others,which can be used for sentiment analysis.The rapid uncovering of these web contents contains divergence of many benefits like profit-making,which is one of the most vital of them all.According to a recent study,81%of consumers conduct online research prior to making a purchase.But the reviews available online are too huge and numerous for human brains to process and analyze.Hence,machine learning classifiers are one of the prominent tools used to classify sentiment in order to get valuable information for use in companies like hotels,game companies,and so on.Understanding the sentiments of people towards different commodities helps to improve the services for contextual promotions,referral systems,and market research.Therefore,this study proposes a sentiment-based framework detection to enable the rapid uncovering of opinionated contents of hotel reviews.A Naive Bayes classifier was used to process and analyze the dataset for the detection of the polarity of the words.The dataset from Datafiniti’s Business Database obtained from Kaggle was used for the experiments in this study.The performance evaluation of the model shows a test accuracy of 96.08%,an F1-score of 96.00%,a precision of 96.00%,and a recall of 96.00%.The results were compared with state-of-the-art classifiers and showed a promising performance andmuch better in terms of performancemetrics.

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

With the increasing usage of drugs to remedy different diseases,drug safety has become crucial over the past few years.Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae.Such diversification is both helpful and danger-ous as such medicine proves to be more effective or shows side effects to different patients.Despite clinical trials,side effects are reported when the medicine is used by the mass public,of which several such experiences are shared on social media platforms.A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed.Sentiment analysis of drug reviews has a large poten-tial for providing valuable insights into these cases.Therefore,this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques.A dataset acquired from the‘Drugs.Com’contain-ing reviews of drug-related side effects and reactions,is used for experiments.A lexicon-based approach,Textblob is used to extract the positive,negative or neu-tral sentiment from the review text.Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory(CNN-LSTM)network.The CNN is used at thefirst level to extract the appropriate features while LSTM is used at the second level.Several well-known machine learning models including logistic regression,random for-est,decision tree,and AdaBoost are evaluated using term frequency-inverse docu-ment frequency(TF-IDF),a bag of words(BoW),feature union of(TF-IDF+BoW),and lexicon-based methods.Performance analysis with machine learning models,long short term memory and convolutional neural network models,and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy.We also performed a statistical sig-nificance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.

Quality assessment of systematic reviews assessing the effects of traditional Chinese exercises in the control of diabetes mellitus

Objective:To assess the quality of systematic reviews assessing the effects of traditional Chinese exercises on controlling blood indices,anthropometric indices,psychological indices,and quality of life in patients with diabetes.Methods:Systematic studies/meta-analyses of intervention with traditional Chinese exercises on diabetes mellitus were searched in the Pub Med,Web of Science,the China National Knowledge Infrastructure Databases(CNKI),Wan Fang Database,and Chinese Scientific Journal Database(VIP).Two researchers independently screened the studies and extracted the data.The methodology and quality of evidence of the included studies were assessed using A Measurement Tool to Assess Systematic Reviews2(AMSTAR-2)and Grading of Recommendations Assessment,Development and Evaluation(GRADE)criteria,respectively.Results:A total of 32 systematic reviews/meta-analyses were included in the present study.Of them,the methodological quality of 4 studies was graded as low,while that of the other 28 was graded as extremely low.The most common quality flaws in key items included a lack of preliminary proposals for systematic reviews,failure to explain the reasons for the inclusion criteria,failure to provide a list of excluded studies and reasons for exclusion,failure to report potential conflicts of interest,and inadequate assessment of publication bias.The quality of evidence for most of the 18 outcomes was subsequently graded as medium or low.Overall,the results of these studies indicated that Tai Chi,health qigong,and other traditional Chinese exercises lowered fasting blood glucose(FBG),2-h postprandial blood glucose(2hPBG),hemoglobin A_(1c)(HbA_(1c)),and body mass index,and relieved anxiety and depression in patients with diabetes.Conclusion:Our findings indicate that the methodological quality of systematic reviews related to traditional Chinese exercises in the diabetic population is generally low,and the quality of evidence is also relatively poor.Therefore,we suggest that the quality of systematic reviews and meta-analyses on traditional Chinese exercises for controlling diabetes mellitus needs to be improved.In the future,researchers should conduct higher-quality clinical studies with reference to the AMSTAR2 checklist and GRADE system.

Data Analytics for the Identification of Fake Reviews Using Supervised Learning

Fake reviews,also known as deceptive opinions,are used to mislead people and have gained more importance recently.This is due to the rapid increase in online marketing transactions,such as selling and purchasing.E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased.New customers usually go through the posted reviews or comments on the website before making a purchase decision.However,the current challenge is how new individuals can distinguish truthful reviews from fake ones,which later deceives customers,inflicts losses,and tarnishes the reputation of companies.The present paper attempts to develop an intelligent system that can detect fake reviews on ecommerce platforms using n-grams of the review text and sentiment scores given by the reviewer.The proposed methodology adopted in this study used a standard fake hotel review dataset for experimenting and data preprocessing methods and a term frequency-Inverse document frequency(TF-IDF)approach for extracting features and their representation.For detection and classification,n-grams of review texts were inputted into the constructed models to be classified as fake or truthful.However,the experiments were carried out using four different supervised machine-learning techniques and were trained and tested on a dataset collected from the Trip Advisor website.The classification results of these experiments showed that na飗e Bayes(NB),support vector machine(SVM),adaptive boosting(AB),and random forest(RF)received 88%,93%,94%,and 95%,respectively,based on testing accuracy and tje F1-score.The obtained results were compared with existing works that used the same dataset,and the proposed methods outperformed the comparable methods in terms of accuracy.

国际地学SCI期刊《Quaternary Science Reviews》导航

《Quaternary Science Reviews》（ISSN：0277—3791），1982年创刊，月刊，由Elservier公司出版，是国际第四纪研究领域内的多学科研究与综述期刊，涵盖了几乎所有的第四纪科学研究内容，包括：地质、地貌、地理、考古、土壤科学、古植物学和古生物学、古气候，以及所有第四纪定年方法。2012年JCR影响因子为4．076，近5年的平均影响因子为5．040。现任主编是澳大利亚伍伦贡大学（University of Wollongong，Wollongong）的C．V．Murray Wallace教授；副主编6位，包括中国科学院地质与地球物理研究所的杨小平研究员。

BIOSIS PREVIEWS数据库的检索特色

主要介绍BIOSIS Previews数据库的检索特点和方法,重点介绍其不同于其它数据库的特色检索,为广大医务人员使用该数据库进行快速、准确的信息检索提供帮助。

Effects of Online English Movie Reviews on Improving Students' Writing

This study aims to study the effectiveness of online English movie reviews for improving university students’English writing in China.English movie reviews can be profitably undertaken to improve university students’writing ability by reading English movie reviews online,discussing topics related to English movie reviews and writing English movie reviews collaboratively.University students have easy access to English movie reviews massively available on the Internet,which renders it possible and feasible for English teachers to use them to improve students’English writing.Online English movie reviews provide students with enough input of model texts,hence they can acquire some appropriate expressions before writing within a short period of time.In addition,discussion on English movie reviews through Emails and QQ platform can activate students’critical thinking to stimulate their original ideas for English movie review writing.Writing English movie reviews collaboratively with the help of Internet can develop students’confidence in writing because of peer feedback and less pressure.

Aspect Based User Reviews Classification

At present online shopping is very popular as it is very convenient for the customers.However,selecting smartphones from online shops is bit difficult only from the pictures and a short description about the item,and hence,the customers refer user reviews and star rating.Since user reviews are represented in human languages,sometimes the real semantic of the reviews and satisfaction of the customers are different than what the star rating shows.Also,reading all the reviews are not possible as typically,a smartphone gets thousands of reviews in popular online shopping platform like Amazon.Hence,this work aims to develop a recommended system for smartphones based on aspects of the phones such as screen size,resolution,camera quality,battery life etc.reviewed by users.To that end we apply hybrid approach,which includes three lexicon-based methods and three machine learning modals to analyze specific aspects of user reviews and classify the reviews into six categories--best,better,good or somewhat for positive comments and for negative comments bad or not recommended--.The lexicon-based tool called AFINN together with Random Forest prediction model provides the best classification F1-score 0.95.This system can be customized according to the required aspects of smartphones and the classification of reviews can be done accordingly.

Acupuncture and/or moxibustion for the treatment of lumbar disc herniation:quality assessment of systematic reviews

Objective:In the current systematic review on acupuncture and/or moxibustion for lumbar disc herniation(LDH),we evaluated the methodology and quality of evidence and reports to provide necessary information for accurate clinical decision-making regarding acupuncture and/or moxibustion for LDH.Methods:From databases such as CBM(Chinese biomedical literature database),VIP(China science and technology journal database),CNKI(China national knowledge infrastructure),WF(Wanfang database),Web of Science,Embase,Medline,and Cochrane Library,systematic reviews on acupuncture and/or moxibustion for LDH were retrieved,and the methodological quality of the literature was evaluated according to the assessment of multiple systematic reviews(AMSTAR)list.Furthermore,the grading of recommendations assessment,development and evaluation(GRADE)system was used to grade the quality of evidence and the preferred reporting items for systematic reviews and meta-analyses(PRISMA)statement to evaluate the quality of the report.Results:A total of 18 systematic reviews were included,and the conclusion is that acupuncture and/or moxibustion have some advantages in terms of efficacy and safety with regard to LDH treatment.According to the AMSTAR score,there were 4 high-quality studies,13 moderate-quality studies,and 1 low-quality study.GRADE showed that quality of evidence such as total effective rate of LDH and VAS was low and that of other forms of evidence was lower.The PRISMA statement showed that 8 articles were in line with 20 or more of the 27 items,and 10 articles were in line with 10-19 of the 27 items.Conclusion:At present,acupuncture and/or moxibustion for LDH has a good curative effect.More importantly,its methodological quality was of moderate level and the report quality was generally good and relatively complete.However,the poor quality of the original research results was reflected in the quality of evidence.More studies are needed to make sure whether acupuncture is more effective than other treatment methods.

Sentiment Analysis of Japanese Tourism Online Reviews

Purpose: Online reviews on tourism attractions provide important references for potential tourists to choose tourism spots. The main goal of this study is conducting sentiment analysis to facilitate users comprehending the large scale of the reviews, based on the comments about Chinese attractions from Japanese tourism website 4 Travel.Design/methodology/approach: Different statistics-and rule-based methods are used to analyze the sentiment of the reviews. Three groups of novel statistics-based methods combining feature selection functions and the traditional term frequency-inverse document frequency(TF-IDF) method are proposed. We also make seven groups of different rulesbased methods. The macro-average and micro-average values for the best classification results of the methods are calculated respectively and the performance of the methods are shown.Findings: We compare the statistics-based and rule-based methods separately and compare the overall performance of the two method. According to the results, it is concluded that the combination of feature selection functions and weightings can strongly improve the overall performance. The emotional vocabulary in the field of tourism(EVT), kaomojis, negative and transitional words can notably improve the performance in all of three categories. The rule-based methods outperform the statistics-based ones with a narrow advantage.Research limitation: Two limitations can be addressed: 1) the empirical studies to verify the validity of the proposed methods are only conducted on Japanese languages; and 2) the deep learning technology is not been incorporated in the methods.Practical implications: The results help to elucidate the intrinsic characteristics of the Japanese language and the influence on sentiment analysis. These findings also provide practical usage guidelines within the field of sentiment analysis of Japanese online tourism reviews.Originality/value: Our research is of practicability. Currently, there are no studies that focus on the sentiment analysis of Japanese reviews about Chinese attractions.

Overviews of reviews on patient compliance with medication protocols used in highly active antiretroviral therapy

Purpose:To provide evidence support for the development of clinical practice guidelines regarding patient adherence to medication protocols used in highly active antiretroviral therapy(HAART)in China.Methods:We analyzed information contained in recent systematic reviews and metaanalyses regarding patient compliance with medication protocols used in HAART.Results:Nine systematic reviews and one meta-analysis were included in our study which involved three different aspects of patient compliance:influencing factors,assessment methods,and interventions.Conclusions:The high quality data obtained from our study was suitable for use in developing clinically useful guidelines for patent compliance with HAART medication protocols.

A Study on Fraud Reviews: Incentives to Manipulate and Effect on Sales

With the broad reach of Internet, online reviews have become an important source of electronic Word-of-Mouth. Fraud reviews that are deliberately posted by businesses are a type of online reviews. This paper discusses the incentives of fraud reviews and the effect of fraud reviews on consumer behavior through empirical research. Using book download data at Amazon, we find that a book is more likely to manipulate fraud reviews when it has few online reviews posted by real consumers, higher proportion of negative reviews, longer average length of negative reviews, lower average rating scored by real users and higher price. And fraud reviews change the review environment and have a significant impact on the consumer purchasing decisions. More number, higher proportion, longer word count and higher promotion of rating of fraud reviews lead to higher sales. The results also show consumers can discern the manipulation of fraud reviews to a certain extent.

《Chemical Reviews》刊发《农药学学报》编委刘长令教授论文

2014年5月29日，由美国化学会主办的《Chemical Reviews》（2012年影响因子为41．298）网站刊登了《农药学学报》编委、沈阳化工研究院国家新农药创制与开发重点实验室刘长令教授等撰写的论文“Application of the Intermediate Derivatization Approach in Agrochemical Discovery”（新农药创新策略“中间体衍生化策略在农用化学品创制中的应用”）。该杂志副主编Robert D． Kuchta教授对该文给予了很高的评价“It is clearly an outstanding manuscript， and we are pleased to have the opportunity of publishing it”。

An Analysis of Attitude Resources in English Film Reviews from the Perspective of Appraisal Theory

The film review is a kind of discourse which analyses and makes comments on the film's director, actors, plots and so on, aiming to analyse, appreciate, and judge the aesthetic value, cognitive value and social meanings, further have impact on the audience's comprehension and appreciation for the film. This paper attempts to examine attitude resources of film review under the framework of appraisal theory. It can be concluded from the case study that there are abundant attitude resources in the film review but film reviewers prefer to employ resources of appreciation so that their reviews are more persuasive and convincing.

东南大学化学化工学院代云茜副教授有关抗烧结催化剂的综述被Chemical Society Reviews接受并已在线发表

东南大学化学化工学院代云茜副教授、美国佐治亚理工大学Younan Xia教授和美国西雅图华盛顿大学Charles T．Campbell教授应邀在国际权威学术期刊Chemical Society Reviews上以“Thephysical chemistry and materials science behinds inter-resistant catalysts”为题发表Tutorial Review论文，综述抗烧结催化剂的最新研究进展。

Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter

Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some sentiment.Therefore,sentiment analysis is currently a hot topic of research.In particular,aspect-based sentiment analysis concerns the exploration of emotions,opinions and facts that are expressed by people,usually in the form of polarity.It is crucial to consider polarity calculations and not simply categorize reviews as positive,negative,or neutral.Currently,the available lexicon-based method accuracy is affected by limited coverage.Several of the available polarity estimation techniques are too general and may not reect the aspect/topic in question if reviews contain a wide range of information about different topics.This paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify ABSA-PER.Experimental outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.

Systematic reviews and meta-analyses: Why are they clinically significant?

This review aims to clarify the clinical significance of systematic reviews and meta-analyses by illustrating several classical examples.Firstly,systematic reviews can provide the highest level of evidence for clinical decisions.Secondly,systematic reviews can propose unresolved issues and future directions.Thirdly,systematic reviews can avoid harm to the human body.Fourthly,systematic reviews can prevent a waste of resources.Generally speaking,clinical researchers should be encouraged to perform systematic reviews and metaanalyses.

Acupuncture for post-stroke dysphagia: An overview of systematic reviews

Objective: To overview the systematic reviews of acupuncture for Post-stroke Dysphagia. Methods: The Chinese and English databases were searched for the systematic reviews of dysphagia after acupuncture treatment. The retrieval time was until October 30, 2019. The final literature was evaluated for bias risk, methodology quality and evidence quality by using ROBIS tool, AMSTER-2 scale and GRADE method. Results: 9 systematic reviews and 36 outcomes were included. ROBIS bias risk assessment results show that all the studies are high bias risk;AMSTER-2 methodology quality assessment results show that all the systems are of very low quality;GRADE grading shows that there are only 7 intermediate evidences in 36 clinical evidences, the rest are low-level evidences or very low-level evidences, and there are multiple intermediate evidences in the clinical efficacy of acupuncture in the treatment of dysphagia after stroke. In addition, acupuncture can significantly improve the scores of all kinds of swallowing function related scales, such as Tengdao food intake swallowing function grade, swallowing disorder specific quality of life score, standard swallowing function evaluation scale, and the incidence of adverse reactions is low. Conclusion: Acupuncture is effective in the treatment of dysphagia after stroke. It can improve the scores of various swallowing function scales and has high safety. However, the risk of systematic evaluation bias is high, the quality of methodology is low, and the level of clinical evidence is low as a whole. In the future, the relevant research design should be more rigorous, and the research report should be written in strict accordance with the PRISMA statement.