Emotion and Modifier in Henry Rider Haggard’s Novels
Year: 2023

Abstract:
In recent years, there has been a growing scholarly interest in employing quantitative methods to analyze literary texts, as they offer unique insights, theories, and interpretations. In light of this, the current study employs quantitative analysis to examine the fiction written by the renowned British adventure novelist Sir Henry Rider Haggard. Specifically, the study aims to investigate the affective content and prevalence of distinctive linguistic features in six of Haggard’s most distinguished works. We evaluate dominant emotional states at the sentence level as well as investigate the deployment of specific linguistic features such as modifiers and deontic modals, and collocated terms. Through sentence-level emotion analysis, the findings reveal a notable prevalence of joy-related emotions across the novels. Furthermore, the study observes that intensifiers are employed more commonly than the mitigators as modifiers and the collocated terms of modifiers exhibit high similarity across the novels. By integrating quantitative analyses with qualitative assessments, this study presents a novel perspective on the patterns of emotion and specialized grammatical features in some of Haggard’s most celebrated literary works.

heading: 1 Introduction
text: Henry Rider Haggard (1856-1925) was a prominent British novelist and acclaimed adventure fiction writer known for his captivating tales set in exotic locations, particularly Africa. He is considered a pioneer in the lost world genre, characterized by thrilling narratives of exploration and discovery in remote and enigmatic places. One of his most famous series of novels features the adventures of Allan Quatermain, a white hunter. Haggard’s works are notable for their vivid descriptions of African landscapes, depictions of African culture, and imaginative portrayals of ancient civilizations. In addition to the Allan Quatermain series, Haggard’s novels She and its sequel, The Return of
She, have gained widespread recognition as seminal examples of imperialistic fiction, showcasing a fusion of adventure, romanticism, and supernatural elements.
Digital humanities is an interdisciplinary field that combines humanities disciplines such as history, literature, and philosophy with computer science and technology to study and create new forms of digital culture (Burdick et al., 2016). In recent years, interest in quantitative analysis of digital humanities has been on the rise with the help of accessible tools and methodologies that encompass a range of approaches, including natural language processing (NLP) and text mining, network analysis, data visualization, statistical analysis, and machine learning (Sazzed, 2022; Levine, 2022). More researchers and professionals in this field are becoming interested in using numerical and statistical methods to study various cultural and humanistic phenomena. Researchers have been using NLP and text mining techniques to analyze text depicting literary works, historical documents, or online archives for diverse purposes (Samothrakis and Fasli, 2015; Simonton, 1990; Dinu and Uban, 2017; San Segundo, 2017; Stockwell and Mahlberg, 2015).
In this study, we focus on analyzing the emotional and specific linguistic features of six of Henry Rider Haggard’s most celebrated novels, employing a variety of natural language processing (NLP) techniques. In particular, we aim to explore the following research aspects-
RQ1: How emotional tones are illustrated in Henry Rider Haggard’s most popular classics?
RQ2: Whether the usage of two linguist features: modifiers and deontic modals vary across Haggard’s popular novels?
We first analyze the presence of various forms of emotions across the six novels by scrutinizing the distribution of emotions at the sentence level. We find similar patterns of emotions at the sentence
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level in all six novels, with joy being the most frequently occurring emotion. In addition, we conduct a linguistic analysis to identify the occurrence of specific linguistic phenomena, such as the usage of mitigators, intensifiers, and deontic modals. Our results indicate that although the comparative presence of intensifiers and modifiers varies across the novels a bit, in general, the percentages are similar, within the range of 0.2%- 0.3% for intensifiers and 0.19%-0.25% for mitigators. Overall, our findings indicate substantial degrees of consistency in all the attributes studied across all six novels.

heading: 2 Dataset
text: The six literary works, namely King Solomon’s Mines (KSM), Allan Quatermain (AQ), The Holy Flower (HF), The Ivory Child (IC), She (SHE), and Ayesha, the Return of She (ARS), are obtained from the Project Gutenberg1 library as UTF-8 formatted text files. To ensure only literary content is analyzed, we manually remove the metadata present in the text file of each novel. The NLTK tokenizer (Bird et al., 2009) is employed to segment the text of each novel into sentences. Very short sentences containing fewer than three words are excluded from the analysis. The resulting dataset is summarized in Table 1.

heading: 3 Emotion Analysis
text: Emotion analysis in literature is the study of emotions and sentiments expressed in written works, such as novels, poems, and short stories employing computational and linguistic methods. Emotion analysis can recognize emotional words and phrases, identify patterns of emotion over time, and categorize emotions into broad categories, such as joy, anger, or sadness. Here, we explore the distributions of prevalent emotions at the sentence-level.
1https://www.gutenberg.org
We utilize the EmoNet emotion recognition framework (Abdul-Mageed and Ungar, 2017) to ascertain the prevailing emotions at the sentence level. The EmoNet framework can identify eight primary categories of emotions (Plutchik, 1980), namely joy (JOY), anticipation (ANT), surprise (SUR), trust (TRU), anger (ANG), disgust (DIS), fear (FEA), and sadness (SAD) in text. We compute the relative frequencies of each primary emotion category in each of the novels and report their respective distributions. It should be noted that according to the authors of EmoNet, each primary emotion category in EmoNet encompasses three related types (i.e., subcategories) of emotions, as defined by Plutchik (1980). For example, joy encompasses the following three types of emotionsecstasy, joy, and serenity. Therefore, overall, 24 types of emotions are considered in this study.

heading: 4 Specialized Modifiers
text: We analyze the presence of three specific types of linguistic feature, intensifier, mitigator and deontic modal, which can be grouped under a broader category of modifiers. Intensifiers and mitigators allow the precise representation of attitudes and opinions by adapting the strength or weakness of the language to correspond to the circumstance. On the other hand, the deontic modal expresses obligations, permissions, or requirements in relation to actions or events.

heading: 4.1 Intensifier
text: An intensifier is a word or phrase employed to strengthen or increase the impact of an adjective, adverb, or verb in a sentence. Intensifiers are used to express degree or emphasis and can help to convey the speaker’s attitude or level of certainty about the information being communicated. Some common intensifiers include- very, quite, absolutely, totally, completely, and utterly. In addition, we examine which words are collocated with the top intensifiers.

heading: 4.2 Mitigator
text: A mitigator is a word or phrase used to soften or lessen the impact of an adjective, adverb, or verb in a sentence. Similar to intensifiers, mitigators are used to articulate degree or emphasis; however, they have the opposite effect of the intensifier. Instead of strengthening the impact of a word, mitigators weaken it. Some common mitigators include
a little, somewhat, kind of, rather, and slightly. In addition to identifying top mitigators, we investigate the words associated (i.e., co-occurring) with them.

heading: 4.3 Deontic Modals
text: Deontic modals refer to a category of modal verbs that express obligation, permission, or necessity. They are typically used to denote what is required, permitted, or prohibited by some rule or principle. In this study, we consider the following deontic modals: must, have to, should, ought to, may, might, could, and can. We investigate the occurrence of deontic modals in all six novels.

heading: 5 Results and Discussion
text: Table 1 highlights that out of the six analyzed novels, HF is the longest, encompassing nearly 5000 sentences. Examining diverse statistics, we observe that the sentence length, measured in words, exhibits minimal variation across all six novels, with average sentence length ranging from 24.0 (HF) to 27.95 (SHE). Additionally, we do not notice any relation between the word count of the novels and their average sentence length.
Table 2 presents the distributions of emotions at the sentence level across all six novels. The results indicate that, among the eight emotion categories considered, the most prominent emotion in all the novels is joy, with 46%-49% of sentences exhibiting the highest values for this emotion type. This finding aligns with the expectations, given the tendency of Haggard’s adventure novels to feature exhilarating and exciting storylines while also exploring the calm and reflective thoughts of human consciousness. The subsequent most dominant emotions are related to fear, anger, and sadness, having a range of 10%-13%. However, their
frequency is considerably lower compared to the joy category, which stands at close to 50% as the dominant emotion. It is important to note that each sentence may contain multiple emotions, and the "dominant emotion category" refers to the emotion with the highest value, which may have a varying distance from the second most prominent emotion, ranging from nearly equal values to quite a large difference.
Table 3 shows the presence of two types of modifiers: intensifiers and modifiers, in all six novels. except for KSM and ARS, the other four novels show very similar occurrences of intensifiers, around 0.3%; in KSM and ARS, the presence is much lower, around 0.2%. Nevertheless, when we consider the most occurring intensifiers, we find they are similar in all books, very, really, utterly, absolutely are four common intensifiers among the top five. We further analyze collocated words of the most frequently occurring intensifier. For the most occurring intensifier very, we find top collocated words are similar. For example, in all novels, well, good, and much are observed 3-5 times with it (Figure 1)
Analogously, we notice frequently occurring mitigators are very similar in all novels, with quite, rather, and almost being the top three mitigators in all six books. However, in contrast to the intensifiers, where very is the most frequently used intensifier, with a significantly higher occurrence rate than other top intensifiers, no such preeminence is observed in the realm of mitigators, as the comparable prevalence of the aforementioned mitigators, namely, quite, rather, and almost, is noted. We also examine the co-occurring words that accompany these mitigators. However, we notice the co-occurring words are very distinct and appear in conjunction with the top mitigators at most twice.
Novel Deontic modal (%) KSM 0.61% (could:161, should:108, must:95) AQ 0.56% (could:241, should:123, may:68) HF 0.77% (could:259, should:223, must:160) IC 0.78% (could:276, should:184, might:132)
SHE 0.65% (could:244, should:144, must:129) ARS 0.80% (could:293, must:188, should:170)
Table 4: Percentages and occurrences of deontic modals in all six novels
As Table 4 depicts, the prevalent deontic modals exhibit a similar distribution across all novels. Could is the most frequently occurring deontic modal in all six novels, followed by should in all cases, except the SHE. We observe a consistent presence of the deontic modals in all novels, ranging from 0.61% to 0.80%.

heading: 6 Summary and Future Work
text: As a preliminary study, here, we scrutinize the emotional and specific linguistic aspects of six celebrated works of Henry Rider Haggard leveraging
various NLP techniques. The emotion recognition framework reveals similar patterns of emotions in all six novels, with joy being the most dominant. The linguistic analysis uncovers the frequency and presence of modifiers, intensifiers, and deontic modals and the collocated words and phrases. Overall, this research observes uniformity in the examined features across all six novels. The findings of this preliminary study reveal emotional and specific linguistic aspects of some of Haggard’s most celebrated works.
Some possible future works will focus on a finegrained analysis of emotion, such as identifying sub-categories of primary emotions and understanding the changes of emotions throughout the story, and finding how it is related to plot twists and other narrative elements. Besides, we will encompass an augmented set of linguistic features to conduct a more exhaustive analysis. Furthermore, additional novels authored by Henry Rider Haggard from multiple genres will be explored to find the consistency and divergence of linguistic and psychological patterns.

heading: Future Work
text: Some possible future works will focus on a finegrained analysis of emotion, such as identifying sub-categories of primary emotions and understanding the changes of emotions throughout the story, and finding how it is related to plot twists and other narrative elements. Besides, we will encompass an augmented set of linguistic features to conduct a more exhaustive analysis. Furthermore, additional novels authored by Henry Rider Haggard from multiple genres will be explored to find the consistency and divergence of linguistic and psychological patterns