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Academic literature on the topic 'Novel of sentiment'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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Journal articles on the topic "Novel of sentiment"

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Wang, Xinzhi, Hui Zhang, and Zheng Xu. "Public Sentiments Analysis Based on Fuzzy Logic for Text." International Journal of Software Engineering and Knowledge Engineering 26, no. 09n10 (November 2016): 1341–60. http://dx.doi.org/10.1142/s0218194016400076.

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Sentiment analysis from microblog platform has received an increasing interest from web mining community in recent years. Current sentiment analysis methods are mainly based on the hypothesis that each word expresses only one sentiment. However, human sentiment are prototyped and fuzzy-confined as declared in social psychology, which is conflicting with the hypothesis. This is one of the barriers that impede the computation of complex public sentiment of web events in microblog. Therefore, how to find a reasonable computational model, combining learning technology and human sentiment cognition theory, is a novel idea in event sentiment analysis of microblog. In this paper, a new sentiment computation approach, which is defined as public sentiments discriminator (PSD), considering both fuzzy logic and sentiment complexity, is proposed. Unlike traditional machine learning methods, PSD is based on the rational hypothesis that sentiments are correlated with each other. A three-level computing structure, sentiment-term level, microblog level and public sentiment level, is employed. Experiments show that the proposed approach, PSD, can achieve similar accuracy and [Formula: see text]1-measure but more cognitive results when compared with traditional well-known machine learning methods. These experimental studies have confirmed that PSD can generate an interpretable result with no restriction among sentiments.

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Hung, Chihli, and You-Xin Cao. "Sentiment classification of Chinese cosmetic reviews based on integration of collocations and concepts." Electronic Library 38, no. 1 (November 25, 2019): 155–69. http://dx.doi.org/10.1108/el-04-2019-0093.

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Purpose This paper aims to propose a novel approach which integrates collocations and domain concepts for Chinese cosmetic word of mouth (WOM) sentiment classification. Most sentiment analysis works by collecting sentiment scores from each unigram or bigram. However, not every unigram or bigram in a WOM document contains sentiments. Chinese collocations consist of the main sentiments of WOM. This paper reduces the complexity of the document dimensionality and makes an improvement for sentiment classification. Design/methodology/approach This paper builds two contextual lexicons for feature words and sentiment words, respectively. Based on these contextual lexicons, this paper uses the techniques of associated rules and mutual information to build possible Chinese collocation sets. This paper applies preference vector modelling as the vector representation approach to catch the relationship between Chinese collocations and their associated concepts. Findings This paper compares the proposed preference vector models with benchmarks, using three classification techniques (i.e. support vector machine, J48 decision tree and multilayer perceptron). According to the experimental results, the proposed models outperform all benchmarks evaluated by the criterion of accuracy. Originality/value This paper focuses on Chinese collocations and proposes a novel research approach for sentiment classification. The Chinese collocations used in this paper are adaptable to the content and domains. Finally, this paper integrates collocations with the preference vector modelling approach, which not only achieves a better sentiment classification performance for Chinese WOM documents but also avoids the curse of dimensionality.

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Chandra, Rohitash, and Aswin Krishna. "COVID-19 sentiment analysis via deep learning during the rise of novel cases." PLOS ONE 16, no. 8 (August 19, 2021): e0255615. http://dx.doi.org/10.1371/journal.pone.0255615.

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Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic event that has raised a number of psychological issues such as depression given abrupt social changes and lack of employment. Advancements of deep learning-based language models have been promising for sentiment analysis with data from social networks such as Twitter. Given the situation with COVID-19 pandemic, different countries had different peaks where rise and fall of new cases affected lock-downs which directly affected the economy and employment. During the rise of COVID-19 cases with stricter lock-downs, people have been expressing their sentiments in social media. This can provide a deep understanding of human psychology during catastrophic events. In this paper, we present a framework that employs deep learning-based language models via long short-term memory (LSTM) recurrent neural networks for sentiment analysis during the rise of novel COVID-19 cases in India. The framework features LSTM language model with a global vector embedding and state-of-art BERT language model. We review the sentiments expressed for selective months in 2020 which covers the major peak of novel cases in India. Our framework utilises multi-label sentiment classification where more than one sentiment can be expressed at once. Our results indicate that the majority of the tweets have been positive with high levels of optimism during the rise of the novel COVID-19 cases and the number of tweets significantly lowered towards the peak. We find that the optimistic, annoyed and joking tweets mostly dominate the monthly tweets with much lower portion of negative sentiments. The predictions generally indicate that although the majority have been optimistic, a significant group of population has been annoyed towards the way the pandemic was handled by the authorities.

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Prasad, Guru, Amith K. Jain, Prithviraj Jain, and Nagesh H. R. "A Novel Approach to Optimize the Performance of Hadoop Frameworks for Sentiment Analysis." International Journal of Open Source Software and Processes 10, no. 4 (October 2019): 44–59. http://dx.doi.org/10.4018/ijossp.2019100103.

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Twitter is one among most popular micro blogging services with millions of active users. It is a hub of massive collection of data arriving from various sources. In Twitter, users most often express their views, opinions, thoughts, emotions or feelings about a particular topic, product or service, of their interest, choice or concern. This makes twitter a hub of gargantuan amount of data, and at the same time a useful platform in getting to know and understand the underlying sentiment behind a particular product or for that matter anything expressed in twitter as tweets. It is important to note here that aforesaid massive collection of data is not just any redundant data, but one which contains useful information as noted earlier. In view of aforesaid context, Sentiment analysis in relation to twitter data gains enormous importance. Sentiment analysis offers itself as a good approach in classifying the opinions formulated by individuals (tweeters) into different sentiments such as, positive, negative, or neutral. Implementing Sentiment analysis algorithms using conventional tools leads to high computation time, and thus are less effective. Hence, there is a need for state-of-the-art tools and techniques to be developed for sentiment analysis making it the need of the hour to facilitate faster computation. An Apache Hadoop framework is one such option that supports distributed data computing and has been commonly adopted for a variety of use-cases. In this article, the author identifies factors affecting the performance of sentiment analysis algorithms based on Hadoop framework and proposes an approach for optimizing the performance of sentiment analysis. The experimental results depict the potential of the proposed approach.

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Sharayu, Athare, and Rathod Vijay. "Novel Sentiment Analysis using Twitter." International Journal of Computer Applications 182, no. 40 (February 15, 2019): 7–9. http://dx.doi.org/10.5120/ijca2019918429.

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Orr, Leah. "Defoe, Sentiment, and the Novel." Eighteenth-Century Life 42, no. 3 (September 1, 2018): 37–41. http://dx.doi.org/10.1215/00982601-6988718.

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Gong, Vincent X., Winnie Daamen, Alessandro Bozzon, and Serge P. Hoogendoorn. "Estimate Sentiment of Crowds from Social Media during City Events." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (June 21, 2019): 836–50. http://dx.doi.org/10.1177/0361198119846461.

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City events are being organized more frequently, and with larger crowds, in urban areas. There is an increased need for novel methods and tools that can provide information on the sentiments of crowds as an input for crowd management. Previous work has explored sentiment analysis and a large number of methods have been proposed relating to various contexts. None of them, however, aimed at deriving the sentiments of crowds using social media in city events, and no existing event-based dataset is available for such studies. This paper investigates how social media can be used to estimate the sentiments of crowds in city events. First, some lexicon-based and machine learning-based methods were selected to perform sentiment analyses, then an event-based sentiment annotated dataset was constructed. The performance of the selected methods was trained and tested in an experiment using common and event-based datasets. Results show that the machine learning method LinearSVC achieves the lowest estimation error for sentiment analysis on social media in city events. The proposed event-based dataset is essential for training methods to reduce estimation error in such contexts.

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Jha, Vandana, Savitha R, P. Deepa Shenoy, Venugopal K R, and Arun Kumar Sangaiah. "A novel sentiment aware dictionary for multi-domain sentiment classification." Computers & Electrical Engineering 69 (July 2018): 585–97. http://dx.doi.org/10.1016/j.compeleceng.2017.10.015.

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Aiyanyo, Imatitikua D., Hamman Samuel, and Heuiseok Lim. "Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning." Sustainability 13, no. 9 (April 29, 2021): 4986. http://dx.doi.org/10.3390/su13094986.

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In this study, we qualitatively and quantitatively examine the effects of COVID-19 on classrooms, students, and educators. Using a new Twitter dataset specific to South Korea during the pandemic, we sample the sentiment and strain on students and educators using applied machine learning techniques in order to identify various topical pain points emerging during the pandemic. Our contributions include a novel and open source geo-fenced dataset on student and educator opinion within South Korea that we are making available to other researchers as well. We also identify trends in sentiment and polarity over the pandemic timeline, as well as key drivers behind the sentiments. Moreover, we provide a comparative analysis of two widely used pre-trained sentiment analysis approaches with TextBlob and VADER using statistical significance tests. Ultimately, we analyze how public opinion shifted on the pandemic in terms of positive sentiments about accessing course materials, online support communities, access to classes, and creativity, to negative sentiments about mental fatigue, job loss, student concerns, and overwhelmed institutions. We also initiate initial discussions about the concept of actionable sentiment analysis by overlapping polarity with the concept of trigger management to assist users in coping with negative emotions. We hope that insights from this preliminary study can promote further utilization of social media datasets to evaluate government messaging, population sentiment, and multi-dimensional analysis of pandemics.

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Murfi, Hendri, Furida Lusi Siagian, and Yudi Satria. "Topic features for machine learning-based sentiment analysis in Indonesian tweets." International Journal of Intelligent Computing and Cybernetics 12, no. 1 (February 28, 2019): 70–81. http://dx.doi.org/10.1108/ijicc-04-2018-0057.

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Purpose The purpose of this paper is to analyze topics as alternative features for sentiment analysis in Indonesian tweets. Design/methodology/approach Given Indonesian tweets, the processes of sentiment analysis start by extracting features from the tweets. The features are words or topics. The authors use non-negative matrix factorization to extract the topics and apply a support vector machine to classify the tweets into its sentiment class. Findings The authors analyze the accuracy using the two-class and three-class sentiment analysis data sets. Both data sets are about sentiments of candidates for Indonesian presidential election. The experiments show that the standard word features give better accuracies than the topics features for the two-class sentiment analysis. Moreover, the topic features can slightly improve the accuracy of the standard word features. The topic features can also improve the accuracy of the standard word features for the three-class sentiment analysis. Originality/value The standard textual data representation for sentiment analysis using machine learning is bag of word and its extensions mainly created by natural language processing. This paper applies topics as novel features for the machine learning-based sentiment analysis in Indonesian tweets.

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Dissertations / Theses on the topic "Novel of sentiment"

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Sawyer, Octavia Cathryn. "Reinventing Virtue: Sensibility and Sentiment in the Works of Maria Edgeworth." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd2845.pdf.

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Upton, Creon. "Narrating Sentiment in Mason & Dixon: A Modernist Novel of Feeling." Thesis, University of Canterbury. English, 2007. http://hdl.handle.net/10092/2591.

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This thesis approaches Thomas Pynchon's novel, Mason & Dixon, in terms of its narrative structure and sentimental content. Pynchon is generally regarded as a challenging and innovative writer, so narrative is an unsurprising subject for a study of his most recent work; sentimentalism, on the other hand, is a far cry from traditional approaches to his writing. Despite this, however, as I outline in my introduction, sentimentalism has long hovered around the edges of Pynchon's work. In Mason & Dixon it takes a privileged role as the dominating mood of the novel's final section, "Last Transit." This sentimentalism, far from being the retrogressive move that the term might imply, is bound up in a radically reconceived approach to the narrating voice of novelistic discourse, whence comes the unifying feature of my study. In Mason & Dixon, I identify this unity in the novel's referencing of film, long-established as one of Pynchon's major cultural influences. In my first chapter, I outline my approach to sentimentalism and narrative-in the modern and, specifically, modernist novel, as well as in contemporary film. In chapter two I outline my conception of Mason & Dixon's narrator as emulating film's visual representations; in chapter three, I explore this narrator as a "radically underdetermined" identity, who represents, not a linguistically embodied subjectivity, but rather representation as its own agent, as representation itself. In my fourth and final chapter, I examine how this narrator manages the sentimental content of the novel, concentrating on the character of Mason.

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Shi, Tian. "Novel Algorithms for Understanding Online Reviews." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104998.

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This dissertation focuses on the review understanding problem, which has gained attention from both industry and academia, and has found applications in many downstream tasks, such as recommendation, information retrieval and review summarization. In this dissertation, we aim to develop machine learning and natural language processing tools to understand and learn structured knowledge from unstructured reviews, which can be investigated in three research directions, including understanding review corpora, understanding review documents, and understanding review segments. For the corpus-level review understanding, we have focused on discovering knowledge from corpora that consist of short texts. Since they have limited contextual information, automatically learning topics from them remains a challenging problem. We propose a semantics-assisted non-negative matrix factorization model to deal with this problem. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of a corpus. We conduct extensive sets of experiments on several short text corpora to demonstrate the proposed model can discover meaningful and coherent topics. For document-level review understanding, we have focused on building interpretable and reliable models for the document-level multi-aspect sentiment analysis (DMSA) task, which can help us to not only recover missing aspect-level ratings and analyze sentiment of customers, but also detect aspect and opinion terms from reviews. We conduct three studies in this research direction. In the first study, we collect a new DMSA dataset in the healthcare domain and systematically investigate reviews in this dataset, including a comprehensive statistical analysis and topic modeling to discover aspects. We also propose a multi-task learning framework with self-attention networks to predict sentiment and ratings for given aspects. In the second study, we propose corpus-level and concept-based explanation methods to interpret attention-based deep learning models for text classification, including sentiment classification. The proposed corpus-level explanation approach aims to capture causal relationships between keywords and model predictions via learning importance of keywords for predicted labels across a training corpus based on attention weights. We also propose a concept-based explanation method that can automatically learn higher level concepts and their importance to model predictions. We apply these methods to the classification task and show that they are powerful in extracting semantically meaningful keywords and concepts, and explaining model predictions. In the third study, we propose an interpretable and uncertainty aware multi-task learning framework for DMSA, which can achieve competitive performance while also being able to interpret the predictions made. Based on the corpus-level explanation method, we propose an attention-driven keywords ranking method, which can automatically discover aspect terms and aspect-level opinion terms from a review corpus using the attention weights. In addition, we propose a lecture-audience strategy to estimate model uncertainty in the context of multi-task learning. For the segment-level review understanding, we have focused on the unsupervised aspect detection task, which aims to automatically extract interpretable aspects and identify aspect-specific segments from online reviews. The existing deep learning-based topic models suffer from several problems such as extracting noisy aspects and poorly mapping aspects discovered by models to the aspects of interest. To deal with these problems, we propose a self-supervised contrastive learning framework in order to learn better representations for aspects and review segments. We also introduce a high-resolution selective mapping method to efficiently assign aspects discovered by the model to the aspects of interest. In addition, we propose using a knowledge distillation technique to further improve the aspect detection performance.
Doctor of Philosophy
Nowadays, online reviews are playing an important role in our daily lives. They are also critical to the success of many e-commerce and local businesses because they can help people build trust in brands and businesses, provide insights into products and services, and improve consumers' confidence. As a large number of reviews accumulate every day, a central research problem is to build an artificial intelligence system that can understand and interact with these reviews, and further use them to offer customers better support and services. In order to tackle challenges in these applications, we first have to get an in-depth understanding of online reviews. In this dissertation, we focus on the review understanding problem and develop machine learning and natural language processing tools to understand reviews and learn structured knowledge from unstructured reviews. We have addressed the review understanding problem in three directions, including understanding a collection of reviews, understanding a single review, and understanding a piece of a review segment. In the first direction, we proposed a short-text topic modeling method to extract topics from review corpora that consist of primary complaints of consumers. In the second direction, we focused on building sentiment analysis models to predict the opinions of consumers from their reviews. Our deep learning models can provide good prediction accuracy as well as a human-understandable explanation for the prediction. In the third direction, we develop an aspect detection method to automatically extract sentences that mention certain features consumers are interested in, from reviews, which can help customers efficiently navigate through reviews and help businesses identify the advantages and disadvantages of their products.

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Minton, Duygu. "Re-working Novelistic Sentiment: Barbauld, Smith, Edgeworth, and the Politics of Children's Fiction." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/dissertations/727.

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Despite the recognized importance of Anna Letitia Barbauld, Maria Edgeworth, and Charlotte Smith as commentators on 1790s radicalism, pedagogy, and novel conventions, their writings for children and for adults tend to be studied separately. Indeed, despite each writer's familiarity with the others' work, these figures are rarely discussed together. I argue that studying these authors' cross-generic works using a comparative approach reveals the ways in which novels and children's books have informed and influenced each other, both in their reciprocal developments and as distinct genres. I further argue that even as the juvenile fiction of Barbauld, Edgeworth, and Smith seems rather tamely oriented toward the integration of natural history with conduct lessons, the genre was in fact a vital means by which each writer weighed her own social-welfare and aesthetic priorities within contexts of political upheaval.

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Poria, Soujanya. "Novel symbolic and machine-learning approaches for text-based and multimodal sentiment analysis." Thesis, University of Stirling, 2017. http://hdl.handle.net/1893/25396.

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Emotions and sentiments play a crucial role in our everyday lives. They aid decision-making, learning, communication, and situation awareness in human-centric environments. Over the past two decades, researchers in artificial intelligence have been attempting to endow machines with cognitive capabilities to recognize, infer, interpret and express emotions and sentiments. All such efforts can be attributed to affective computing, an interdisciplinary field spanning computer science, psychology, social sciences and cognitive science. Sentiment analysis and emotion recognition has also become a new trend in social media, avidly helping users understand opinions being expressed on different platforms in the web. In this thesis, we focus on developing novel methods for text-based sentiment analysis. As an application of the developed methods, we employ them to improve multimodal polarity detection and emotion recognition. Specifically, we develop innovative text and visual-based sentiment-analysis engines and use them to improve the performance of multimodal sentiment analysis. We begin by discussing challenges involved in both text-based and multimodal sentiment analysis. Next, we present a number of novel techniques to address these challenges. In particular, in the context of concept-based sentiment analysis, a paradigm gaining increasing interest recently, it is important to identify concepts in text; accordingly, we design a syntaxbased concept-extraction engine. We then exploit the extracted concepts to develop conceptbased affective vector space which we term, EmoSenticSpace. We then use this for deep learning-based sentiment analysis, in combination with our novel linguistic pattern-based affective reasoning method termed sentiment flow. Finally, we integrate all our text-based techniques and combine them with a novel deep learning-based visual feature extractor for multimodal sentiment analysis and emotion recognition. Comparative experimental results using a range of benchmark datasets have demonstrated the effectiveness of the proposed approach.

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Akay, Altug. "A Novel Method to Intelligently Mine Social Media to Assess Consumer Sentiment of Pharmaceutical Drugs." Doctoral thesis, KTH, Systemsäkerhet och organisation, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-203119.

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This thesis focuses on the development of novel data mining techniques that convert user interactions in social media networks into readable data that would benefit users, companies, and governments. The readable data can either warn of dangerous side effects of pharmaceutical drugs or improve intervention strategies. A weighted model enabled us to represent user activity in the network, that allowed us to reflect user sentiment of a pharmaceutical drug and/or service. The result is an accurate representation of user sentiment. This approach, when modified for specific diseases, drugs, and services, can enable rapid user feedback that can be converted into rapid responses from consumers to industry and government to withdraw possibly dangerous drugs and services from the market or improve said drugs and services. Our approach monitors social media networks in real-time, enabling government and industry to rapidly respond to consumer sentiment of pharmaceutical drugs and services.

QC 20170314

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Morgan, George MacGregor. "London! O Melancholy! : the eloquence of the body in the town in the English novel of sentiment." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2573.

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Morgan reads the treatment of gesture in Clarissa (Richardson, 1747 - 48), Amelia (Fielding,1 751), and Cecilia (Burney, 1782) to study the capacity the sentimental novel attributes to physical forms of eloquence to generate sociability and moderate selfishness in London. He argues that the eighteenth-century English novel of sentiment adopts a physiology derived from Descartes's theory of the body-machine to construct sentimental protagonists whose gestures bear witness against Bernard Mandeville's assertions that people are not naturally sociable, and that self-interest, rather than sympathy, determines absolutely every aspect of human behaviour. However, when studied in the context of sentimental fiction set in the cruel and unsociable metropolis of London, the action of this eloquent body proved relatively ineffectual in changing its spectators for the better. In the English novelistic tradition that stems from Samuel Richardson's Clarissa (1747 - 48), selfishness lies at the roots of civilization, and inculcates modern urban people with instinctively theatrical mores: metropolitan theatricality, marked out in the gestures of the polite body, works to vitiate the sociability that might naturally animate everyday human intercourse. Clarissa responds to the dilemma of the intrinsic theatricality and self-interestedness of modern civil society with a heroine whose gestures (that is, whose physical states) demonstrate an eloquence that partially counteracts some of the effects self-love has upon the metropolis. But while sympathy and natural eloquence do little to diminish London's submission to selfishness, they remain, in Clarissa, unequivocally good. In contrast with Clarissa, Henry Fielding's Amelia (1751) and Frances Burney's Cecilia (1782) criticize both phenomena. In these novels, both by written by socially conservative authors, natural eloquence and sympathy do not generate sociability in London at all and do not even ensure personal virtue unless they are tempered by the discipline of some kind of theatricality. For Fielding and for Burney, unregulated sympathy becomes a problem to which the best remedy is a modicum of stage-craft. But, strangely enough, all three novels indirectly licence the principles of the self-interest they ostensibly attack. Ultimately, these novels of sentiment self-consciously position sympathy and natural eloquence as supplemental discourses that might protest against the dominant practices of Mandevillian self-interest that produce the social order of the metropolis. The net result is that the novel of sentiment implicitly tolerates the dominance of self-interest in the areas of public activity that lie mostly outside the subject-matter with which sentimental fiction principally concerns itself.

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Kim, Seungyeon. "Novel document representations based on labels and sequential information." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53946.

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A wide variety of text analysis applications are based on statistical machine learning techniques. The success of those applications is critically affected by how we represent a document. Learning an efficient document representation has two major challenges: sparsity and sequentiality. The sparsity often causes high estimation error, and text's sequential nature, interdependency between words, causes even more complication. This thesis presents novel document representations to overcome the two challenges. First, I employ label characteristics to estimate a compact document representation. Because label attributes implicitly describe the geometry of dense subspace that has substantial impact, I can effectively resolve the sparsity issue while only focusing the compact subspace. Second, while modeling a document as a joint or conditional distribution between words and their sequential information, I can efficiently reflect sequential nature of text in my document representations. Lastly, the thesis is concluded with a document representation that employs both labels and sequential information in a unified formulation. The following four criteria are utilized to evaluate the goodness of representations: how close a representation is to its original data, how strongly a representation can be distinguished from each other, how easy to interpret a representation by a human, and how much computational effort is needed for a representation. While pursuing those good representation criteria, I was able to obtain document representations that are closer to the original data, stronger in discrimination, and easier to be understood than traditional document representations. Efficient computation algorithms make the proposed approaches largely scalable. This thesis examines emotion prediction, temporal emotion analysis, modeling documents with edit histories, locally coherent topic modeling, and text categorization tasks for possible applications.

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Erik, Cambria. "Application of common sense computing for the development of a novel knowledge-based opinion mining engine." Thesis, University of Stirling, 2011. http://hdl.handle.net/1893/6497.

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The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience.

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Taylor, Anne. "Sentimental Journey/Winter Journey: Araki Nobuyoshi's Contemporary Shishōsetsu." Thesis, University of Oregon, 2013. http://hdl.handle.net/1794/13310.

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Senchimentaru na tabi fuyu no tabi or Sentimental Journey/Winter Journey, a photobook created and published by photographer Araki Nobuyoshi in 1991, documented two highly personal events of the photographer's life. The first section consists of twenty-two images of Araki's 1971 honeymoon with his wife Yōko Aoki, while the second section features ninety-one images and an essay documenting the last six months of Yōko's life in 1989-90. This thesis measures SJ/WJ against a Japanese literary tradition invoked by Araki in his opening manifesto: the shishōsetsu. A genre of writing from the early 1900's that read like a confessional or personal diary, the shishōsetsu was regarded as a `true' story insofar as it revealed a totally transparent `author' within a totally transparent `text.' Given these criteria, this thesis determines the success of Araki's SJ/WJ as a true-to-life autobiography.

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Books on the topic "Novel of sentiment"

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Barnett, Jill. Sentimental journey: A novel. New York: Pocket Star Books, 2002.

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Las ingratas: Novela sentimental. Buenos Aires: Clarín/Aguilar, 2002.

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Cohen, Margaret. The sentimental education of the novel. Princeton, N.J: Princeton University Press, 1999.

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Brink, Gabriël. Moral Sentiments in Modern Society. Translated by Gioia Marini. NL Amsterdam: Amsterdam University Press, 2016. http://dx.doi.org/10.5117/9789089647757.

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Since the time of Adam Smith, scholars have tried to understand the role moral sentiments play in modern life, an issue that became especially urgent during and after the 2008 global financial crisis. Previous explanations have ranged from the idea that modern society is built on moral values to the notion that modernisation results in moral decay. The essays in this interdisciplinary volume use the example of Dutch society and a wealth of empirical data to propose a novel theory about the ambivalent relation between contemporary life and human nature. In the process, the contributors argue for the need to reject simplistic explanations and reinvent civil society.

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Duarte, Manuel Dias. O professor Simão Botelho: Novela sentimental. Lisboa: Fonte da Palavra, 2013.

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Cavendish, Devonshire Georgiana Spencer. Emma, or, The unfortunate attachment: A sentimental novel. Albany: State University of New York Press, 2004.

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El canalla sentimental. Barcelona: Planeta, 2008.

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Clio, Eros, Thanatos: The "novela sentimental" in context. New York: P. Lang, 2001.

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Escudero, Carmen. La novela sentimental española: Formas y recursos expresivos. Murcia: Diego Marín, 1989.

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Zwinger, Lynda. Daughters, fathers, and the novel: The sentimental romance of heterosexuality. Madison, Wis: University of Wisconsin Press, 1991.

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Book chapters on the topic "Novel of sentiment"

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Pavan Kumar, C. S., and L. D. Dhinesh Babu. "Novel Text Preprocessing Framework for Sentiment Analysis." In Smart Intelligent Computing and Applications, 309–17. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1927-3_33.

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Tan, Kye Lok, Jer Lang Hong, and Ee Xion Tan. "A Novel Ontological Technique for Sentiment Analysis." In Neural Information Processing, 339–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34475-6_41.

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Nguyen, Cuong V., Khiem H. Le, and Binh T. Nguyen. "A Novel Approach for Enhancing Vietnamese Sentiment Classification." In Lecture Notes in Computer Science, 99–111. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79463-7_9.

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Devi, J. Sirisha, Siva Prasad Nandyala, and P. Vijaya Bhaskar Reddy. "A Novel Approach for Sentiment Analysis of Public Posts." In Innovations in Computer Science and Engineering, 161–67. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8201-6_18.

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Liu, Yangcheng, and Fawaz E. Alsaadi. "A Novel Way to Build Stock Market Sentiment Lexicon." In Communications in Computer and Information Science, 350–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2810-1_34.

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Rao, Himanshu Singh, Jagdish Chandra Menaria, and Satyendra Singh Chouhan. "A Novel Approach for Sentiment Analysis of Hinglish Text." In Advances in Intelligent Systems and Computing, 229–40. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9953-8_20.

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Saravanan, Vijayalakshmi, Ishpreet Singh, Emanuel Szarek, Jereon Hak, and Anju S. Pillai. "A Novel Implementation of Sentiment Analysis Toward Data Science." In Applied Learning Algorithms for Intelligent IoT, 175–92. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9781003119838-8.

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Murugeshan, Meenakshi Sundaram, and Saswati Mukherjee. "Novel Relevance Model for Sentiment Classification Based on Collision Theory." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 417–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35615-5_67.

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Feng, Shi, Daling Wang, Ge Yu, Chao Yang, and Nan Yang. "Sentiment Clustering: A Novel Method to Explore in the Blogosphere." In Advances in Data and Web Management, 332–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00672-2_30.

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Zhang, Jianwei, Yukiko Kawai, Tadahiko Kumamoto, and Katsumi Tanaka. "A Novel Visualization Method for Distinction of Web News Sentiment." In Web Information Systems Engineering - WISE 2009, 181–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04409-0_22.

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Conference papers on the topic "Novel of sentiment"

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Chen, Huimin, Xiaoyuan Yi, Maosong Sun, Wenhao Li, Cheng Yang, and Zhipeng Guo. "Sentiment-Controllable Chinese Poetry Generation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/684.

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Expressing diverse sentiments is one of the main purposes of human poetry creation. Existing Chinese poetry generation models have made great progress in poetry quality, but they all neglected to endow generated poems with specific sentiments. Such defect leads to strong sentiment collapse or bias and thus hurts the diversity and semantics of generated poems. Meanwhile, there are few sentimental Chinese poetry resources for studying. To address this problem, we first collect a manually-labelled sentimental poetry corpus with fine-grained sentiment labels. Then we propose a novel semi-supervised conditional Variational Auto-Encoder model for sentiment-controllable poetry generation. Besides, since poetry is discourse-level text where the polarity and intensity of sentiment could transfer among lines, we incorporate a temporal module to capture sentiment transition patterns among different lines. Experimental results show our model can control the sentiment of not only a whole poem but also each line, and improve the poetry diversity against the state-of-the-art models without losing quality.

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R. Hodeghatta, Umesh, and Sanath V. Haritsa. "Covid-19 Twitter Sentiments Across the United States in August 2020." In International Conference on AI, Machine Learning and Applications (AIMLA 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111305.

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COVID-19 has drastically affected the entire nation. This study involved collecting tweets and analyzing the COVID tweets for August 2020. The aim was to understand whether people have expressed sentiments related to COVID-19 across all the states of the United States and find any correlation between the sentiment tweets and the number of actual cases reported. Around 400000 COVID-19 Twitter data was collected for August 2020 from the primary Twitter database. A simple NLP-based unigram sentiment analyser, a novel approach different from the traditional machine learning approach, was adopted to identify twitter sentiments. The results indicate that tweets related to COVID demonstrate the two types of sentiments, one related to the deaths and the other about the COVID symptoms. Furthermore, the results show that the sentiments for each category vary from State to State. For example, states of New York, California, Texas are higher tweets sentiments regarding expressing death sentiment, and states of New York, California, Nevada, are higher regarding sentiments of expressing COVID-19 symptoms with an accuracy of 83%. As a part of the research, a new sentiment scorecard was created to provide a sentiment score based on the sentiments of the tweets expressed to the actual reported death cases. The sentiment scores for the ‘symptoms’ class are higher for Maryland, New Jersey, and Oregon, whereas sentiment scores for the 'death' class are higher for Virginia, Delaware, and Hawaii. These sentiment scores indicate that the Twitter users of these states are actively tweeting about symptoms and deaths even though the actual reported cases are less in these states. The analysis results also found no or little correlation between the COVID Tweets and the number of COVID death cases reported across all the states.

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Govind, B. S. Sachin, Ramakrishnudu Tene, and K. Lakshmi Saideep. "Novel Recommender Systems Using Personalized Sentiment Mining." In 2018 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2018. http://dx.doi.org/10.1109/conecct.2018.8482394.

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EL MRABTI, Soufiane, Mohamed LAZAAR, Mohammed AL ACHHAB, and Hicham OMARA. "Novel Convex Polyhedron Classifier for Sentiment Analysis." In 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech). IEEE, 2020. http://dx.doi.org/10.1109/cloudtech49835.2020.9365906.

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Ghosh, Rahul, Kumar Ravi, and Vadlamani Ravi. "A novel deep learning architecture for sentiment classification." In 2016 3rd International Conference on Recent Advances in Information Technology (RAIT). IEEE, 2016. http://dx.doi.org/10.1109/rait.2016.7507953.

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Sun, Mengtao, Ibrahim A. Hameed, and Hao Wang. "A Novel Ensemble Representation Framework for Sentiment Classification." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207194.

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EMRE ISIK, Yunus, Yasin GORMEZ, Oguz KAYNAR, and Zafer AYDIN. "NSEM: Novel Stacked Ensemble Method for Sentiment Analysis." In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018. http://dx.doi.org/10.1109/idap.2018.8620913.

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Pahwa, Bhumika, S. Taruna, and Neeti Kasliwal. "A Novel Approach for Aspect Level Sentiment Analysis." In 2018 International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, 2018. http://dx.doi.org/10.1109/gucon.2018.8674949.

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Chen, Huajie, Eric Ke Wang, Feng Li, and Wenli Yu. "A Novel Teacher-Student Network for Sentiment Classification." In 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/aiie-16.2016.118.

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Priyadarshana, Y. H. P. P., L. Ranathunga, and P. M. Karunaratne. "Sentiment negation: A novel approach in measuring negation score." In 2016 Future Technologies Conference (FTC). IEEE, 2016. http://dx.doi.org/10.1109/ftc.2016.7821679.

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Reports on the topic "Novel of sentiment"

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Hassan, Tarek A., Jesse Schreger, Markus Schwedeler, and Ahmed Tahoun. Country Risk. Institute for New Economic Thinking Working Paper Series, March 2021. http://dx.doi.org/10.36687/inetwp157.

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We construct new measures of country risk and sentiment as perceived by global investors and executives using textual analysis of the quarterly earnings calls of publicly listed firms around the world. Our quarterly measures cover 45 countries from 2002-2020. We use our measures to provide a novel characterization of country risk and to provide a harmonized definition of crises. We demonstrate that elevated perceptions of a country's riskiness are associated with significant falls in local asset prices and capital outflows, even after global financial conditions are controlled for. Increases in country risk are associated with reductions in firm-level investment and employment. We also show direct evidence of a novel type of contagion, where foreign risk is transmitted across borders through firm-level exposures. Exposed firms suffer falling market valuations and significantly retrench their hiring and investment in response to crises abroad. Finally, we provide direct evidence that heterogeneous currency loadings on global risk help explain the cross-country pattern of interest rates and currency risk premia.

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