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Sentiment analysis has gained widespread adoption in many fields, but not-until now-in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has been quantitative data to help the scholar choose between the many models. Which model is best for which narrative, and why? By comparing over three dozen models, including the latest Deep Learning AI, the author details how to choose the correct model-or set of models-depending on the unique affective fingerprint of a narrative. The author also demonstrates how to combine a clustered close reading of textual cruxes in order to interpret a narrative. By analyzing a diverse and cross-cultural range of texts in a series of case studies, the Element highlights new insights into the many shapes of stories.

Criticism --- Sentiment analysis. --- Data processing. --- Analysis, Sentiment --- Extraction, Opinion --- Mining, Opinion --- Mining, Sentiment --- Opinion extraction --- Opinion mining --- Sentiment mining --- Computational linguistics --- Data mining --- Evaluation of literature --- Literary criticism --- Literature --- Rhetoric --- Aesthetics --- Technique --- Evaluation

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Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent financial technology (FinTech) development, some interdisciplinary researchers start to involve in the in-depth analysis of investors' opinions. These works indicate the trend toward fine-grained opinion mining in the financial domain. When expressing opinions in finance, terms like bullish/bearish often spring to mind. However, the market sentiment of the financial instrument is just one type of opinion in the financial industry. Like other industries such as manufacturing and textiles, the financial industry also has a large number of products. Financial services are also a major business for many financial companies, especially in the context of the recent FinTech trend. For instance, many commercial banks focus on loans and credit cards. Although there are a variety of issues that could be explored in the financial domain, most researchers in the AI and NLP communities only focus on the market sentiment of the stock or foreign exchange. This open access book addresses several research issues that can broaden the research topics in the AI community. It also provides an overview of the status quo in fine-grained financial opinion mining to offer insights into the futures goals. For a better understanding of the past and the current research, it also discusses the components of financial opinions one-by-one with the related works and highlights some possible research avenues, providing a research agenda with both micro- and macro-views toward financial opinions.

Natural language & machine translation --- Data mining --- Algorithms & data structures --- Artificial intelligence --- Information technology: general issues --- Natural Language Processing (NLP) --- Data Mining and Knowledge Discovery --- Data Structures and Information Theory --- Artificial Intelligence --- Computer Applications --- Data Science --- Computer and Information Systems Applications --- Open Access --- financial opinion mining --- text mining in finance --- financial technology application --- FinTech --- argument mining in finance --- opinion quality evaluation --- numeral understanding --- Expert systems / knowledge-based systems --- Information theory

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Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection.

opinion mining --- affect computing --- health insurance --- Twitter --- hybrid vectorization --- violence against women --- word association --- collaborative schemes of sentiment analysis and sentiment systems --- random forest --- cyber-aggression --- deep learning --- online review --- emotion analysis --- lexicon construction --- provider networks --- text mining --- sentiment lexicon --- social media --- sentiment-aware word embedding --- psychographic segmentation --- medical web forum --- gender classification --- racism --- sentiment analysis --- sentiment classification --- sentiment word analysis --- social networks --- convolutional neural network --- review data mining --- machine learning --- emotion classification --- big data-driven marketing --- text feature representation --- recommender system --- user preference prediction --- violence based on sexual orientation --- semantic networks

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Devoted to research and scholarship in digital editions of texts, digital libraries; digital archives and memory; examination and analysis of multimedia resources; text mining and data mining, stylometry, topic modeling, sentiment analysis; georeferencing, maps, visualization tools ; corpus linguistics, Natural Language Processing (NLP) ; digital media, digitization, curatorship and preservation of digital objects.

digital humanities --- computing technologies --- digital technologies --- Electronic journals --- Humanities --- Digital libraries --- Text data mining --- Sentiment analysis --- Corpora (Linguistics) --- Natural language processing (Computer science) --- Digital preservation --- Periodicals --- Spain --- digital archives --- digital media --- digitization --- Text analysis (Data mining) --- Text analytics --- Text mining --- Computational linguistics --- Data mining --- Digital curation --- Digital media collections --- Digital media libraries --- Digital repositories --- Electronic libraries --- Electronic publication collections --- Electronic publication libraries --- Electronic text collections --- Repositories, Digital --- Virtual libraries --- Libraries --- Information storage and retrieval systems --- Web archives --- NLP (Computer science) --- Artificial intelligence --- Electronic data processing --- Human-computer interaction --- Semantic computing --- Corpus-based analysis (Linguistics) --- Corpus linguistics --- Linguistic analysis (Linguistics) --- Analysis, Sentiment --- Extraction, Opinion --- Mining, Opinion --- Mining, Sentiment --- Opinion extraction --- Opinion mining --- Sentiment mining --- Cyber journals --- Cyber magazines --- Cyber periodicals --- Cyber serials --- E-journals --- Ejournals --- Electronic magazines --- Electronic periodicals --- Electronic serials --- Internet journals (Electronic publications) --- Internet magazines (Electronic publications) --- Internet periodicals (Electronic publications) --- Internet serials (Electronic publications) --- Online journals --- Online magazines --- Online periodicals --- Online serials --- Periodicals in machine-readable form --- Web journals (Electronic publications) --- Web magazines (Electronic publications) --- Web periodicals (Electronic publications) --- Web serials (Electronic publications) --- World Wide Web journals (Electronic publications) --- World Wide Web magazines (Electronic publications) --- World Wide Web periodicals (Electronic publications) --- World Wide Web serials (Electronic publications) --- Electronic publications --- Espagne --- Espainiako Erresuma --- España --- Espanha --- Espanja --- Espanya --- Estado Español --- Hispania --- Hiszpania --- Isupania --- Kingdom of Spain --- Regne d'Espanya --- Reiaume d'Espanha --- Reino de España --- Reino d'Espanya --- Reinu d'España --- Sefarad --- Sepharad --- Shpanie --- Shpanye --- Spanien --- Spanish State --- Supein