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Today we have access to unprecedented amounts of literary texts. However,
search still relies heavily on key words. In this paper, we show how sentiment
analysis can be used in tandem with effective visualizations to quantify and
track emotions in both individual books and across very large collections. We
introduce the concept of emotion word dens...
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Citations
... These methods, along with classical stylometric techniques, expand the range of quantitative methods available for philological analysis. Successful examples of sentiment analysis applied to literary texts include the following works: [10], [11], [12], [13], [14]. ...
Sentiment analysis is a relevant task in natural language processing, which is often conducted on Internet texts to analyze reviews of products, services, posts, and comments on social media. Unlike most studies, our work focuses on literary texts in the Russian language written during the emotionally charged period of the early 20th century, when a change in the Russian political system and a fundamental shift in society's way of life occurred as a result of socio-economic upheavals such as wars and revolutions. It is assumed that literary texts from this period should also be rich in emotional vocabulary. The main goal of the research described in this article is to study the correlation between the results of sentiment analysis, performed on the material of Russian short stories using several automatic methods, and the average expert evaluation of the emotions that these same texts evoke in modern readers. The results of the study contribute to understanding the evaluative component of literary works vocabulary and can also be used to build recommendation systems aimed at selecting literary texts for readers.
... A common approach to analyzing literary texts (novels, plays, poetry, etc.) is to model the change in emotion words over whole texts [19][20][21][22]. Inferences can then be made as to whether stories adhere to prototypical "story shapes" or emotional arcs [6,7,23]. ...
Emotion dynamics is a framework for measuring how an individual’s emotions change over time. It is a powerful tool for understanding how we behave and interact with the world. In this paper, we introduce a framework to track emotion dynamics through one’s utterances. Specifically we introduce a number of utterance emotion dynamics (UED) metrics inspired by work in Psychology. We use this approach to trace emotional arcs of movie characters. We analyze thousands of such character arcs to test hypotheses that inform our broader understanding of stories. Notably, we show that there is a tendency for characters to use increasingly more negative words and become increasingly emotionally discordant with each other until about 90% of the narrative length. UED also has applications in behavior studies, social sciences, and public health.
... Visualization of emotional analysis is used in various domains such as sports [17,40,42], storytelling [23] and event-detection [26,34]. Most systems focus on visualizing positive vs. negative sentiment that can more easily be extracted from text. ...
... The use of the NRC lexicon for emotion detection in a text is based on the premise that the emotion expressed in the text is the aggregate of the emotions of the words comprising it. This approach was proven to work very well on different domains [18,23]. To attain an emotional signature of a movie, we first aggregated all emotional words in each of the eight Plutchik categories from all reviews of that movie. ...
We present Movie Emotion Map - a novel system that enables to view and browse through a large collection of movies according to the movies’ emotional characteristics. The system enables to view both the high-level structure of the movies emotional space and the low-level details of a single movie. We create an eight-dimensional emotional signature for each movie based on Plutchik’s theory of emotions, according to its reviews obtained from IMDb. We projected glyphs representing emotional signatures of the movies on a 2D plane using dimension reduction, thus, providing a topology of emotions for easy browsing and exploring. Results from a qualitative evaluation with 18 participants indicate that users could easily browse through movies according to the visualized landscape and that the tool enabled them to search, filter and find movies based on their emotional characteristics.
... Maharjan et al. (2017) explore a wide variety of features (including "readability") that can be used to classify texts in terms of "likability." Other standard methods of computational linguistics used in this context include sentiment and emotion analysis (Alm and Sproat, 2005;Francisco and Gervás, 2006;Kakkonen and Galić Kakkonen, 2011;Mohammad, 2011;Reagan et al., 2016;Maharjan et al., 2018). Global statistical properties such as complexity and entropy have been used to study the regularity (Mehri and Lashkari, 2016;Hernández-Gómez et al., 2017) and the quality of texts (Febres and Jaffe, 2017). ...
This study investigates global properties of three categories of English text: canonical fiction, non-canonical fiction, and non-fictional texts. The central hypothesis of the study is that there are systematic differences with respect to structural design features between canonical and non-canonical fiction, and between fictional and non-fictional texts. To investigate these differences, we compiled a corpus containing texts of the three categories of interest, the Jena Corpus of Expository and Fictional Prose (JEFP Corpus). Two aspects of global structure are investigated, variability and self-similar (fractal) patterns, which reflect long-range correlations along texts. We use four types of basic observations, (i) the frequency of POS-tags per sentence, (ii) sentence length, (iii) lexical diversity, and (iv) the distribution of topic probabilities in segments of texts. These basic observations are grouped into two more general categories, (a) the lower-level properties (i) and (ii), which are observed at the level of the sentence (reflecting linguistic decoding), and (b) the higher-level properties (iii) and (iv), which are observed at the textual level (reflecting comprehension/integration). The observations for each property are transformed into series, which are analyzed in terms of variance and subjected to Multi-Fractal Detrended Fluctuation Analysis (MFDFA), giving rise to three statistics: (i) the degree of fractality (
H
), (ii) the degree of multifractality (
D
), i.e., the width of the fractal spectrum, and (iii) the degree of asymmetry (
A
) of the fractal spectrum. The statistics thus obtained are compared individually across text categories and jointly fed into a classification model (Support Vector Machine). Our results show that there are in fact differences between the three text categories of interest. In general, lower-level text properties are better discriminators than higher-level text properties. Canonical fictional texts differ from non-canonical ones primarily in terms of variability in lower-level text properties. Fractality seems to be a universal feature of text, slightly more pronounced in non-fictional than in fictional texts. On the basis of our results obtained on the basis of corpus data we point out some avenues for future research leading toward a more comprehensive analysis of textual aesthetics, e.g., using experimental methodologies.
... • Literary Analysis and Digital Humanities: There is growing interest in using automatic natural language processing techniques to analyze large collections of literary texts. Specifically with respect to emotions, there is work on tracking the flow of emotions in novels, plays, and movie scripts, detecting patterns of sentiment common to large collections of texts, and tracking emotions of plot characters (Mohammad, 2011(Mohammad, , 2012bHartner, 2013). There is also work in generating music that captures the emotions in text (Davis & Mohammad, 2014). ...
Recent advances in machine learning have led to computer systems that are humanlike in behavior. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behavior. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis.
... Grounded theory is a method of analysing texts that includes Table 1 General ecological characteristics of wetlands, and corresponding resources and hazards for human culture (from Kiviat, 2014 identifying categories (themes) and coding the presence or absence of each theme in a text (Bernard, 2006; see also Mohammad, 2011). The coded data are then analyzed for patterns. ...
Wetlands display general ecological characteristics that are reflected in human culture. Nature and culture, in turn, are reflected in fiction. Wetlands provide many resources to human society as well as presenting significant hazards, and have been both protected and destroyed; fiction might provide insight into the ambivalent human-wetland relationship. I consulted 200 English-language novels containing wetland imagery, and analyzed in detail 15 American novels. Using grounded theory, I coded 17 broadly-defined themes associated with wetland images in each novel. In a total of 1117 themes in 4192 printed pages, the commonest themes (≥55 instances each) were Transition, Place, Refuge, Beauty, Peace, Product, Mosquito, Discord, Danger, Decay, and Sex. Richness and diversity of themes were high in most novels analyzed. There were few relationships between general characteristics of the novels (e.g., author age or gender, number of pages, positive or negative ending) and the occurrence of particular themes. On the whole-book level, Discord, Danger, Decay, and Sex were correlated with the most other themes. Theme patterns seem related to human ambivalence towards wetlands (i.e., resources vs. hazards), and the prominence of production and decomposition in wetlands.
... The lexicon was created by crowdsourcing to Mechanical Turk and contains over 14,000 words (Mohammad and Turney 2013). It was validated for several different domains, from fairy tales to discussions on the news, as well as U.S. sports fans' tweets during World Cup 2014 (Mohammad 2011(Mohammad , 2012Kennedy et al. 2012;Yu and Wang 2015). For each word, it assigns a binary value for each of 8 emotions, based on whether the word is associated with that emotion. ...
... These techniques, therefore, look for the presence of appropriate affect words in the text. On top of being a very large database, it was proven to work very well on different domains (Kennedy et al. 2012;Mohammad 2011). Each review was then annotated using the NRC lexicon for Plutchik eight emotions, as well as the positive and negative sentiment. ...
Online reviews are broadly believed to reflect consumers’ opinions towards the reviewed items. In this work, we postulate that online reviews for experience goods also reflect something very different, the reviewer’s emotions while experiencing the item. We study the case of films, which are made with the intent of evoking an emotional response. We postulate that the emotions that the viewer experienced while watching the movie are reflected in their online review, and can be reliably extracted from it. We aggregate these emotional experiences to create an emotional signature for the film—the emotions that it tends to evoke in viewers. We conduct a series of validations, each designed to offer evidence that the emotional signature indeed reflects the emotions that the film evokes in viewers. Our results demonstrate that experience goods’ online reviews can be utilized for extracting and understanding users’ emotional experience. Items’ induced emotions can be used in affective recommender systems and affective multimedia retrieval. In addition, users’ ability to easily see in advance the emotions that the item has induced in others, is relevant for research on the effect of expected emotions on decision-making.
... The work presented by Mohammad [6] uses a set of techniques that automatically access and analyze books (mostly novels and fairy tales) using sentiment analysis in tandem with effective visualizations. This allows one to quantify and track emotions in both individual books and across very large collections. ...
Literature work reading is an essential activity for human communication and learning. However, several relevant tasks as selection, filter or analyze in a high number of such works become complex. For dealing with this requirement, several strategies are proposed to rapidly inspect substantial amounts of text, or retrieve information previously read, exploiting graphical, textual or auditory resources. In this paper, we propose a methodology to generate audiovisual summaries by the combination of emotion-based music composition and graph-based animation. We applied natural language processing algorithms for extracting emotions and characters involved in literary work. Then, we use the extracted information to compose a musical piece to accompany the visual narration of the story aiming to convey the extracted emotion. For that, we set important musical features as chord progressions, tempo, scale, and octaves, and we assign a set of suitable instruments. Moreover, we animate a graph to sum up the dialogues between the characters in the literary work. Finally, to assess the quality of our methodology, we conducted two user studies that reveal that our proposal provides a high level of understanding over the content of the literary work besides bringing a pleasant experience to the user.
... In most cases, sentiment analysis has been applied on a short-term time scale, such as social media interactions (Lansdall-Welfare et al. 2012). However, some researchers have explored a longer time scale, analysing the expression of emotions in several decades of song lyrics (Dodds and Danforth 2010), newspaper articles (Iliev et al. 2016), in Grimm's folktales (Mohammad 2013) or in centuries of literary works (Acerbi et al. 2013). ...
Popular music offers a rich source of data that provides insights into long-term cultural evolutionary dynamics. One major trend in popular music, as well as other cultural products such as literary fiction, is an increase over time in negatively valenced emotional content, and a decrease in positively valenced emotional content. Here we use two large datasets containing lyrics from n = 4913 and n = 159,015 pop songs respectively and spanning 1965–2015, to test whether cultural transmission biases derived from the cultural evolution literature can explain this trend towards emotional negativity. We find some evidence of content bias (negative lyrics do better in the charts), prestige bias (best-selling artists are copied) and success bias (best-selling songs are copied) in the proliferation of negative lyrics. However, the effects of prestige and success bias largely disappear when unbiased transmission is included in the models, which assumes that the occurrence of negative lyrics is predicted by their past frequency. We conclude that the proliferation of negative song lyrics may be explained partly by content bias, and partly by undirected, unbiased cultural transmission.
... Bu çalışmada, NRC Emotion Lexicon (Duygu Puan Listesi) oluşturulmuştur. Bu liste, bazı İngilizce kelimelerin sekiz temel duygu (öfke, korku, beklenti, güven, şaşkınlık, hüzün, sevinç ve iğrenme) ve iki kutup duygu (negatif ve pozitif) ile olan ilişkilerinin bir puan listesidir [2,3]. Bu listede, sınırlı sayıda kelimenin puanı mevcuttur, birçok kelimeye ait puan verilmemiştir. ...
