Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspe...Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.展开更多
The Lyric Collection on Objects of the Tea Smoke Pavilion is an anthology that Zhu Yizun compiled in his later years by carefully selecting object-depicting lyrics he composed throughout his life.Approximately 70 perc...The Lyric Collection on Objects of the Tea Smoke Pavilion is an anthology that Zhu Yizun compiled in his later years by carefully selecting object-depicting lyrics he composed throughout his life.Approximately 70 percent of the works were created before he participated in the imperial examination in 1679.This indicates that this anthology does not fully reflect the characteristics of the lyrics he penned in his later years.The collection title"Tea Smoke Pavilion,"as revealed in The Lyric Collection of the Serenity Dwelling,reflects the poet's distinctive approach to expressing emotions through depicting objects.A detailed examination of Zhu's works from the perspectives of textual structure,language,tone pattern and rhyming demonstrates that Zhu has consistently adhered to the same guiding principle when composing object-depicting lyrics.He has elevated the technique of“objectification,”pioneered by the poets of the Southern Song Dynasty to a new realm,crafting a unique beauty in object-depicting lyrics,which is different from the beauty of Song lyrics.His success is rooted in his emotional experiences depicted in The Serenity Dwelling Collection.His relentless exploration in artistic forms has provided a source of inspiration for scholars of later generations to explore how to convey personal sentiments.It also offers an opportunity to reflect on the merits and demerits regarding the development of object-depicting lyrics during the Qing Dynasty.展开更多
The feedback collection and analysis has remained an important subject matter for long.The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis.However,the...The feedback collection and analysis has remained an important subject matter for long.The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis.However,the student expresses their feedback opinions on online social media sites,which need to be analyzed.This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews.Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level.The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.展开更多
People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various ...People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.展开更多
The term sentiment analysis deals with sentiment classification based on the review made by the user in a social network.The sentiment classification accuracy is evaluated using various selection methods,especially thos...The term sentiment analysis deals with sentiment classification based on the review made by the user in a social network.The sentiment classification accuracy is evaluated using various selection methods,especially those that deal with algorithm selection.In this work,every sentiment received through user expressions is ranked in order to categorise sentiments as informative and non-informative.In order to do so,the work focus on Query Expansion Ranking(QER)algorithm that takes user text as input and process for sentiment analysis andfinally produces the results as informative or non-informative.The challenge is to convert non-informative into informative using the concepts of classifiers like Bayes multinomial,entropy modelling along with the traditional sentimental analysis algorithm like Support Vector Machine(SVM)and decision trees.The work also addresses simulated annealing along with QER to classify data based on sentiment analysis.As the input volume is very fast,the work also addresses the concept of big data for information retrieval and processing.The result com-parison shows that the QER algorithm proved to be versatile when compared with the result of SVM.This work uses Twitter user comments for evaluating senti-ment analysis.展开更多
亚当·斯密是一位特别注重修辞的学者,要想准确把握其道德理论核心,需要认真分析其代表作《道德情操论》的文本。The Theory of Moral Sentiments(TMS)标题中的moral sentiments是指人类在道德判断上的一种基本能力,是包含同情、良...亚当·斯密是一位特别注重修辞的学者,要想准确把握其道德理论核心,需要认真分析其代表作《道德情操论》的文本。The Theory of Moral Sentiments(TMS)标题中的moral sentiments是指人类在道德判断上的一种基本能力,是包含同情、良知、审美以及道德推理等多方面内容的,其根源在于人类以自己同情共感的能力经验到各种道德实践,又通过归纳、反思和推理来将其一般化,最后上升为指导道德抉择和道德行为的原理。斯密道德论的核心绝非"道德情操"本身,而是各种道德情感得以形成的同情共感机制。现在被广泛接受的中文翻译书名《道德情操论》容易误导读者,而翻译成《道德情感论》更符合斯密道德理论的核心内涵。展开更多
James Joyce's short story "The Dead was one of the most popular short stories in Modern English Literature.To a certain degree,the hero Gabriel is the maturity Joyce.Gabriel's epiphany to life and death i...James Joyce's short story "The Dead was one of the most popular short stories in Modern English Literature.To a certain degree,the hero Gabriel is the maturity Joyce.Gabriel's epiphany to life and death in the Christmas midnight is also the author's "the adventures of the mind".The paper attempts to explain the similarities between Gabriel and Joyce in terms of their complex national sentiments,political views and thei r relationship with their own wife.It also analysis the device of symbolism used in the short story.展开更多
文摘Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.
文摘The Lyric Collection on Objects of the Tea Smoke Pavilion is an anthology that Zhu Yizun compiled in his later years by carefully selecting object-depicting lyrics he composed throughout his life.Approximately 70 percent of the works were created before he participated in the imperial examination in 1679.This indicates that this anthology does not fully reflect the characteristics of the lyrics he penned in his later years.The collection title"Tea Smoke Pavilion,"as revealed in The Lyric Collection of the Serenity Dwelling,reflects the poet's distinctive approach to expressing emotions through depicting objects.A detailed examination of Zhu's works from the perspectives of textual structure,language,tone pattern and rhyming demonstrates that Zhu has consistently adhered to the same guiding principle when composing object-depicting lyrics.He has elevated the technique of“objectification,”pioneered by the poets of the Southern Song Dynasty to a new realm,crafting a unique beauty in object-depicting lyrics,which is different from the beauty of Song lyrics.His success is rooted in his emotional experiences depicted in The Serenity Dwelling Collection.His relentless exploration in artistic forms has provided a source of inspiration for scholars of later generations to explore how to convey personal sentiments.It also offers an opportunity to reflect on the merits and demerits regarding the development of object-depicting lyrics during the Qing Dynasty.
文摘The feedback collection and analysis has remained an important subject matter for long.The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis.However,the student expresses their feedback opinions on online social media sites,which need to be analyzed.This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews.Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level.The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.
文摘People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.
文摘The term sentiment analysis deals with sentiment classification based on the review made by the user in a social network.The sentiment classification accuracy is evaluated using various selection methods,especially those that deal with algorithm selection.In this work,every sentiment received through user expressions is ranked in order to categorise sentiments as informative and non-informative.In order to do so,the work focus on Query Expansion Ranking(QER)algorithm that takes user text as input and process for sentiment analysis andfinally produces the results as informative or non-informative.The challenge is to convert non-informative into informative using the concepts of classifiers like Bayes multinomial,entropy modelling along with the traditional sentimental analysis algorithm like Support Vector Machine(SVM)and decision trees.The work also addresses simulated annealing along with QER to classify data based on sentiment analysis.As the input volume is very fast,the work also addresses the concept of big data for information retrieval and processing.The result com-parison shows that the QER algorithm proved to be versatile when compared with the result of SVM.This work uses Twitter user comments for evaluating senti-ment analysis.
文摘亚当·斯密是一位特别注重修辞的学者,要想准确把握其道德理论核心,需要认真分析其代表作《道德情操论》的文本。The Theory of Moral Sentiments(TMS)标题中的moral sentiments是指人类在道德判断上的一种基本能力,是包含同情、良知、审美以及道德推理等多方面内容的,其根源在于人类以自己同情共感的能力经验到各种道德实践,又通过归纳、反思和推理来将其一般化,最后上升为指导道德抉择和道德行为的原理。斯密道德论的核心绝非"道德情操"本身,而是各种道德情感得以形成的同情共感机制。现在被广泛接受的中文翻译书名《道德情操论》容易误导读者,而翻译成《道德情感论》更符合斯密道德理论的核心内涵。
文摘James Joyce's short story "The Dead was one of the most popular short stories in Modern English Literature.To a certain degree,the hero Gabriel is the maturity Joyce.Gabriel's epiphany to life and death in the Christmas midnight is also the author's "the adventures of the mind".The paper attempts to explain the similarities between Gabriel and Joyce in terms of their complex national sentiments,political views and thei r relationship with their own wife.It also analysis the device of symbolism used in the short story.
