目的对第二版范德堡头颈症状调查量表(Vanderbilt head and neck symptom survey,Version 2.0,VHNSS 2.0)进行汉化,并对其信度、效度进行初步检验。方法采用Brislin翻译和回译法,经5位专家咨询,对VHNSS 2.0进行汉化和跨文化调适,形成中...目的对第二版范德堡头颈症状调查量表(Vanderbilt head and neck symptom survey,Version 2.0,VHNSS 2.0)进行汉化,并对其信度、效度进行初步检验。方法采用Brislin翻译和回译法,经5位专家咨询,对VHNSS 2.0进行汉化和跨文化调适,形成中文版量表,将量表应用在450例头颈癌(head and neck cancer,HNC)放疗患者中进行调查,采用项目分析、内容效度、探索性因子分析、Cronbachα系数、折半信度、反应度对量表信度、效度进行检验。结果翻译后中文版VHNSS 2.0量表共有50个条目(13个维度),416例患者完成调查研究,其中量表中条目27(使用止疼药)与条目43(假牙)2个条目由于调查对象没有使用止疼药、假牙者,条目无计分,只有48个条目进入项目与信度、效度分析。相关系数结果显示,各条目与总分的相关系数,除条目42的相关系数值为0.242(删除该条目),其余各条目相关系数值在0.318~0.735,均有统计学意义(均P<0.05)。条目水平内容效度指数为0.800~1.000,量表水平内容效度指数为0.932;探索性因子分析共提取10个公因子,累计方差贡献率73.303%;量表的Cronbachα系数为0.958,折半信度为0.865。中文版VHNSS 2.0量表最后保留47个条目(10个维度)。结论汉化版VHNSS 2.0具有良好的信度、效度,可以用于中国HNC放疗患者的治疗相关症状发生情况和严重程度的评估。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
文摘目的对第二版范德堡头颈症状调查量表(Vanderbilt head and neck symptom survey,Version 2.0,VHNSS 2.0)进行汉化,并对其信度、效度进行初步检验。方法采用Brislin翻译和回译法,经5位专家咨询,对VHNSS 2.0进行汉化和跨文化调适,形成中文版量表,将量表应用在450例头颈癌(head and neck cancer,HNC)放疗患者中进行调查,采用项目分析、内容效度、探索性因子分析、Cronbachα系数、折半信度、反应度对量表信度、效度进行检验。结果翻译后中文版VHNSS 2.0量表共有50个条目(13个维度),416例患者完成调查研究,其中量表中条目27(使用止疼药)与条目43(假牙)2个条目由于调查对象没有使用止疼药、假牙者,条目无计分,只有48个条目进入项目与信度、效度分析。相关系数结果显示,各条目与总分的相关系数,除条目42的相关系数值为0.242(删除该条目),其余各条目相关系数值在0.318~0.735,均有统计学意义(均P<0.05)。条目水平内容效度指数为0.800~1.000,量表水平内容效度指数为0.932;探索性因子分析共提取10个公因子,累计方差贡献率73.303%;量表的Cronbachα系数为0.958,折半信度为0.865。中文版VHNSS 2.0量表最后保留47个条目(10个维度)。结论汉化版VHNSS 2.0具有良好的信度、效度,可以用于中国HNC放疗患者的治疗相关症状发生情况和严重程度的评估。
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.
