介绍并解读澳大利亚循证方法学组织(Joanna Briggs Institute,JBI)研发的基于文本和专家意见的系统综述(the systematic review of text and opinion,Sr TO)流程,分别对纳入标准、检索策略、真实性/严格性评价、文本信息提取、文本信息...介绍并解读澳大利亚循证方法学组织(Joanna Briggs Institute,JBI)研发的基于文本和专家意见的系统综述(the systematic review of text and opinion,Sr TO)流程,分别对纳入标准、检索策略、真实性/严格性评价、文本信息提取、文本信息综合5项内容观点进行概括总结,并在其中适时融入中医经验类证据。形成围绕“PICO纳入标准”“检索策略”“资料提取表”“质量评估清单”“信息综合与评级”5项流程的Sr TO,并对中医学领域中医学经验类证据(古籍、医案医话、专家经验等)的系统综述提出了意见和建议。在中医药领域推广应用Sr TO方法,具有较好的方法学研究价值,为中医药领域医案医话相关研究的证据整合提供思路与方向。对中医经验类证据评价和分析路径的标准化和规范化处理,有利于提高此类证据利用度,进而用于解答特定情境下的临床问题。展开更多
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.Soc...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.展开更多
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 f...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.展开更多
文摘介绍并解读澳大利亚循证方法学组织(Joanna Briggs Institute,JBI)研发的基于文本和专家意见的系统综述(the systematic review of text and opinion,Sr TO)流程,分别对纳入标准、检索策略、真实性/严格性评价、文本信息提取、文本信息综合5项内容观点进行概括总结,并在其中适时融入中医经验类证据。形成围绕“PICO纳入标准”“检索策略”“资料提取表”“质量评估清单”“信息综合与评级”5项流程的Sr TO,并对中医学领域中医学经验类证据(古籍、医案医话、专家经验等)的系统综述提出了意见和建议。在中医药领域推广应用Sr TO方法,具有较好的方法学研究价值,为中医药领域医案医话相关研究的证据整合提供思路与方向。对中医经验类证据评价和分析路径的标准化和规范化处理,有利于提高此类证据利用度,进而用于解答特定情境下的临床问题。
文摘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.
文摘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.
