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Learning sentiment classification model from labeled features
No full textYulan he Dashuan
No full text李德潤等著.Cover title.書內三個歌劇本原載河北文藝.與下列同本: 妯娌倆過年 / 唐山市文工團集體創作 -- 玉蘭和大拴 / 安平師範第3班第17組原作 ; 張蓮德等改寫.本電子書乃根據《香港版權條例(第528章)》而複製, 並只可在大學圖書館系統內的獨立電子書系統上使用.Li Derun deng zhu.Ben dian zi shu nai gen ju "Xianggang ban quan tiao li (Di 528 zhang)" er fu zhi, bing zhi ke zai da xue tu shu guan xi tong nei de du li dian zi shu xi tong shang shi yong.Shu nei san ge ge ju ben yuan zai Hebei wen yi.With: Zhou li liang guo nian / Tangshan shi wen gong tuan ji ti chuang zuo -- Yulan he Dashuan / Anping shi fan di 3 ban di 17 zu yuan zuo ; Zhang Liande deng gai xie
Magnolia Yulan
No full textLa magnolia de Yulan, es un proyecto de novela corta que nace de dos hechos históricos: la
diáspora china de mediados del siglo XIX –que trajo al Perú a migrantes chinos en condiciones
de explotación–, y en la rebelión Ti Ping, que marcó el inicio del fin de los períodos dinásticos
en China. Ambos hechos se unen para dar vida a He Sunmin, un adolescente de la élite china,
que viaja a Perú en 1863, huyendo de una sentencia a muerte, producto de la traición del
patriarca familiar contra el emperador. Como todo migrante, de entonces y de hoy, debe
enfrentar la dura experiencia de perderlo todo y dejar de ser lo que ha sido. El escenario
principal es un infernal viaje de tres meses en barco, durante los cuales el protagonista se debate
entre el principio confuciano de la lealtad filial, tanto a su abuelo como a su padre, mientras
lucha por su supervivencia en el mundo occidental. Como soporte del proceso creativo se ha
recurrido a trabajos de investigación sobre el fenómeno migratorio chino de los autores
Humberto Rodríguez Pastor, Fernando de Trazegnies, Patricia Castro Obando y otros, así como
la lectura de obras literarias de Mo Yan, Premio Nobel de Literatura 2012, Haruki Murakami,
Siu Kam Wen, Yi Mun Yol, Chimamanda Ngozi Adichie, Lisa See, Joseph Conrad, Gabriel
García Márquez, Kurt Singer & Jane Sherrod, Hermann Hesse
Mining a Web citation database for author co-citation analysis
No full textAuthor co-citation analysis (ACA) has been widely used in bibliometrics as an analytical method in analyzing the intellectual structure of science studies. It can be used to identify authors fromthe same or similar research fields. However, such analysis method relies heavily on statistical tools to perform the analysis and requires human interpretation. Web Citation Database is a data warehouse used for storing citation indices of Web publications. In this paper, we propose a mining process to automate the ACA based on the Web Citation Database. The mining process uses agglomerative hierarchical clustering (AHC) as the mining technique for author clustering and multidimensional scaling (MDS) for displaying author cluster maps. The clustering results and author cluster map have been incorporated into a citation-base
Protecting Animals 36: Author Witi Ihimaera
No full textIn this very special episode of Knowing Animals I am joined by beloved New Zealand author Witi Ihimaera. Witi has written many books featuring nonhuman animals. He offers us a non-colonial lens through which to think about the human/nonhuman relationship
Scene graph modification based on Natural Language Commands
No full textStructured representations like graphs and parse trees play a crucial role in many Natural Language Processing systems. In recent years, the advancements in multi-turn user interfaces necessitate the need for controlling and updating these structured representations given new sources of information. Although there have been many efforts focusing on improving the performance of the parsers that map text to graphs or parse trees, very few have explored the problem of directly manipulating these representations. In this paper, we explore the novel problem of graph modification, where the systems need to learn how to update an existing scene graph given a new user’s command. Our novel models based on graph-based sparse transformer and cross attention information fusion outperform previous systems adapted from the machine translation and graph generation literature. We further contribute our large graph modification datasets to the research community to encourage future research for this new problem.</p
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Online sentiment and topic dynamics tracking over the streaming data
No full textWe propose a dynamic joint sentiment-topic model (dJST) which is able to effectively track sentiment and topic dynamics over the streaming data. Both topic and sentiment dynamics are captured by assuming that the current sentiment topic specific word distributions are generated according to the word distributions at previous epochs. We study three different ways of accounting for such dependency information, (1) Sliding window where the current sentiment-topic-word distributions are dependent on the previous sentiment topic specific word distributions in the last S epochs; (2) Skip model where history sentiment topic-word distributions are considered by skipping some epochs in between; and (3) Multiscale model where previous long- and short- timescale distributions are taken into consideration. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011
Probabilistic topic models for sentiment analysis on the Web
No full textSentiment analysis aims to use automated tools to detect subjective information such as
opinions, attitudes, and feelings expressed in text, and has received a rapid growth of
interest in natural language processing in recent years. Probabilistic topic models, on
the other hand, are capable of discovering hidden thematic structure in large archives of
documents, and have been an active research area in the field of information retrieval.
The work in this thesis focuses on developing topic models for automatic sentiment
analysis of web data, by combining the ideas from both research domains.
One noticeable issue of most previous work in sentiment analysis is that the trained
classifier is domain dependent, and the labelled corpora required for training could be
difficult to acquire in real world applications. Another issue is that the dependencies
between sentiment/subjectivity and topics are not taken into consideration. The main
contribution of this thesis is therefore the introduction of three probabilistic topic
models, which address the above concerns by modelling sentiment/subjectivity and topic
simultaneously.
The first model is called the joint sentiment-topic (JST) model based on latent Dirichlet
allocation (LDA), which detects sentiment and topic simultaneously from text. Unlike
supervised approaches to sentiment classification which often fail to produce
satisfactory performance when applied to new domains, the weakly-supervised nature of JST
makes it highly portable to other domains, where the only supervision information
required is a domain-independent sentiment lexicon. Apart from document-level sentiment
classification results, JST can also extract sentiment-bearing topics automatically,
which is a distinct feature compared to the existing sentiment analysis approaches.
The second model is a dynamic version of JST called the dynamic joint sentiment-topic
(dJST) model. dJST respects the ordering of documents, and allows the analysis of topic
and sentiment evolution of document archives that are collected over a long time span. By
accounting for the historical dependencies of documents from the past epochs in the
generative process, dJST gives a richer posterior topical structure than JST, and can
better respond to the permutations of topic prominence. We also derive online inference
procedures based on a stochastic EM algorithm for efficiently updating the model
parameters.
The third model is called the subjectivity detection LDA (subjLDA) model for
sentence-level subjectivity detection. Two sets of latent variables were introduced in
subjLDA. One is the subjectivity label for each sentence; another is the sentiment label
for each word token. By viewing the subjectivity detection problem as weakly-supervised
generative model learning, subjLDA significantly outperforms the baseline and is
comparable to the supervised approach which relies on much larger amounts of data for
training.
These models have been evaluated on real world datasets, demonstrating that joint
sentiment topic modelling is indeed an important and useful research area with much to
offer in the way of good results
Implicit emotion detection in text
No full textIn text, emotion can be expressed explicitly, using emotion-bearing words (e.g. happy, guilty) or implicitly without emotion-bearing words. Existing approaches focus on the detection of explicitly expressed emotion in text. However, there are various ways to express and convey emotions without the use of these emotion-bearing words. For example, given two sentences: “The outcome of my exam makes me happy” and “I passed my exam”, both sentences express happiness, with the first expressing it explicitly and the other implying it. In this thesis, we investigate implicit emotion detection in text. We propose a rule-based approach for implicit emotion detection, which can be used without labeled corpora for training. Our results show that our approach outperforms the lexicon matching method consistently and gives competitive performance in comparison to supervised classifiers. Given that emotions such as guilt and admiration which often require the identification of blameworthiness and praiseworthiness, we also propose an approach for the detection of blame and praise in text, using an adapted psychology model, Path model to blame. Lack of benchmarking dataset led us to construct a corpus containing comments of individuals’ emotional experiences annotated as blame, praise or others. Since implicit emotion detection might be useful for conflict-of-interest (CoI) detection in Wikipedia articles, we built a CoI corpus and explored various features including linguistic and stylometric, presentation, bias and emotion features. Our results show that emotion features are important when using Nave Bayes, but the best performance is obtained with SVM on linguistic and stylometric features only. Overall, we show that a rule-based approach can be used to detect implicit emotion in the absence of labelled data; it is feasible to adopt the psychology path model to blame for blame/praise detection from text, and implicit emotion detection is beneficial for CoI detection in Wikipedia articles
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