“How Does That Make You Feel?”: The Psychological Dimensions of Editorial Comments

Researcher’s note (June 2026). I wrote this chapter to argue that editing is not only a technical act but an emotional and interpersonal one—that the language editors choose carries sentiment, signals expertise, and either protects or bruises an author’s ego. The core idea was to use a sentiment analysis of real student comments to show how editors balance clout and empathy when they ask an author to change their work. That question matters even more now in my current work on AI in writing and assessment, because AI increasingly mediates the feedback writers receive, and we still need to understand the psychological dimensions of a comment before we automate it.

Chapter Takeaways

• Describes the four psychological dimensions that underlie editorial comments and the language markers related to sentiment, social cognition, and social order.
• Investigates how editors’ and authors’ gender, college major, and native speaker status informs the sentiment of editorial comments.
• Provides recommendations for improving the sentiment of editorial comments, thereby strengthening future author–editor collaborations.

Introduction

Technical editing is the most underdeveloped subfield of technical communication because unverified common knowledge often dictates its best practices. A substantial portion of this literature was written by practicing editors whose recommendations were often informed by personal experience rather than data analysis. Eaton (2010) described this body of knowledge as a collection of “cup of coffee articles” because the recommendations were useful, but only as useful as having a cup of coffee with someone and chatting about an experience (p. 9). As a result, we bring incomplete information into our technical editing classrooms and, worse, the information we can provide is not generalizable and does not necessarily inspire students to pursue the profession.

For example, Eaton and her colleagues (2008a, 2008b) explored the author–editor relationship, which previous literature often posited as adversarial, tense, and full of dissatisfaction. In fact, Eaton et al.’s survey revealed that most authors characterized their experiences with editors as useful and positive. The results also indicated how authors typically defined editing, their preferred editing modes, and commenting structures—information that strengthens author–editor collaboration and provides concrete best practices to discuss with our students.

This chapter addresses one aspect of the author–editor relationship, specifically the psychological dimensions that underlie the comments that editors provide for their authors. Previous literature has explored obliging comment structures, but no study has yet addressed how these comments, in their totality, reflect sentiment, cognition, and social order. Understanding how these dynamics relate offers insight into how editors can motivate a more successful revision from their authors. For this study, I analyzed a 41,146-word corpus of graduate student editorial comments on four sentiment variables: analytical thinking, clout, authenticity, and emotional tone. A regression analysis also indicated whether editors varied how they delivered these comments based on their own or their authors’ gender, college major, and native speaker status.

Literature Review

The author–editor relationship is arguably the most covered topic in technical editing literature—both as lore and empirically—because it presents some of the most intriguing hierarchical dynamics in communication. In this section, I briefly describe how these dynamics interact (and sometimes compete) based on editorial objectives and the social variables of editors and authors. One strategy for improving author–editor collaboration is constructing effective comments. Therefore, I next describe the major empirical studies that have explored comment structures and suggest that also examining the psychological dimensions behind these comments can produce more informed best practices for teaching technical editing.

On a foundational level, technical editors are in demand because they enjoy “paying rigorous attention to nuance and detail, finessing the intricacies of language, and negotiating with authors” (Murphy, 2010, pp. 1–2). Authors typically report useful editing experiences, but many of them seek this assistance because it is mandated by their company (Eaton et al., 2008b). Editors are tasked with projecting professional competence, but not in ways that unnecessarily challenge the authors’ writing style (and patience). In the end, authors possess final editorial control, and editors’ effectiveness hinges on having their suggestions accepted. Negative editing experiences are perhaps not as common as the literature suggests, but authors complain about unneeded or excessive editing, changing the meaning of the document, and an excessive amount of time necessary to review the edited documents (Lanier, 2004). One editor’s attention to detail and finessing might be one author’s definition of an excessive, time-consuming process.

The dynamics that inform the author–editor relationship become further complicated when considering social variables, including gender, academic major, and native speaker status. Linguistic research has indicated that social variables correlate with syntactic choices (Adamson, 1992; Fries, 1940). Specifically, females often choose more formal registers than male speakers (Finegan & Biber, 2001) as well as use more emphatics (e.g., really, real, such, so) when communicating with other females (Biber, Conrad, & Reppen, 1998). Similar trends have been reported in technical communication research. For example, female technical writing students typically attend the demonstrative this with a noun phrase, thus choosing the more formal register (Boettger & Wulff, 2014). Female students also use more adverbs in their technical writing than males as well as associate with adverbs common to conversation, including really, too, and maybe (Boettger & Wulff, under review). Additionally, both studies reported that academic major and native speaker status influenced some of the writers’ language choices, meriting similar exploration within technical editing. Survey results from Eaton et al. (2008a) again provide the best information on how social variables impact the editing process. Female and male authors as well as native and nonnative English speakers had different opinions on the most obliging commenting structure. Native English speakers were also more likely to accept proofreading and copyediting suggestions than nonnative speakers. However, as Eaton et al. point out, one of the most significant findings from their survey was that social variables did not impact editorial communication as much as perhaps presumed.

Learning—let alone conceptualizing—all these dynamics can be challenging to illustrate in the technical editing classroom. When reviewing literature for this chapter, I discovered an abundance of professional characteristics that effective technical editors “must” possess: editors must always be confident, assured, diplomatic, patient, polite, neutral, and objective. They must also account for authors’ reactions to their suggestions, so they must be modest, flexible, willing to compromise, empathetic, sensitive, pleasant, and even cheery (e.g., Bostian, 1986; Dahm, 1998; Hart, 2004, 2010; Mackiewicz & Riley, 2003). It is a daunting list of requirements to share with students and one that is more derived from subjective interpretation than data-driven inquiry.

One strategy for developing these professional characteristics, and, thus, for better understanding the dynamics of the author–editor relationship, is learning to write constructive comments. I define constructive as comments that encapsulate both editorial expertise and empathy, leading the author to accept those comments, and, as a result, improving the overall effectiveness of the document. Again, the editing literature on commenting strategies is founded in the anecdotal, but recent research has filled this gap with more generalizable findings. Mackiewicz and Riley (2003) began the conversation with eight linguistic strategies for balancing clarity and politeness in editorial comments. These strategies included both direct and indirect language features and accounted for many of the aspirational characteristics mentioned earlier. The most comprehensive assessment of commenting structures was conducted by Eaton and her colleagues (2008a, 2008b). The researchers assessed Mackiewicz and Riley’s recommendations on over 400 authors and concluded that only two of the eight strategies performed as predicted. In fact, authors ranked a structure that Eaton et al. added as the second most obliging structure; this comment type begins with a payoff statement followed by the suggestion. More recently, Albers and Marsella (2011) analyzed the quality of 132 comprehensive comments from undergraduate technical editing students. They noted that student editors grasped paragraph-level edits and often delivered their comments with a direct style; however, they struggled with producing global-level edits related to the documents’ overall structure and organization.

As technical editing research continues to empirically explore the structure of effective comments, it is important to also investigate the sentiment, cognitive processing, and social positioning that underlie these comments. Understanding linguistic structures in tandem with psychological dimensions can produce more generalizable strategies for constructing editorial comments.

This chapter begins this exploration by subjecting a corpus of student editorial comments to a sentiment analysis. Sentiment analysis is the automatic extraction of semantic information related to human feelings and opinions (Pang & Lee, 2008). Researchers use sentiment analyses to better understand how emotions, feelings, and opinions influence cognition, economic choices, learner engagement, and political affiliations (Crossley, Kyle, & McNamara, 2017).

Technical editing teachers and students can benefit from the results of sentiment analyses in many ways. First, the computerized techniques used to assess sentiment are derived by analyzing texts on multiple language markers. For example, function words (e.g., pronouns, articles, prepositions) convey information about psychological characteristics such as thinking style, honesty, and social status (Drouin, Boyd, Hancock, & James, 2017; Kacewicz, Pennebaker, Davis, Jeon, & Graesser, 2014; Newman, Pennebaker, Berry, & Richards, 2003). Understanding how language markers with little lexical meaning could signal sentiment better informs the teaching of grammar as well as the strategies developing editors use in their commenting structures. Next, sentiment analyses account for the abundance of characteristics associated with effective technical editors, namely an editor’s ability to display professional competence while also protecting their authors’ ego.

The sentiment variables discussed in this chapter were derived from several language categories by the developers of the Linguistic Inquiry and Word Count (LIWC). These four variables include analytical thinking, clout, authenticity, and emotional tone.

• Analytical Thinking encompasses the level of formality and logic a writer displays in a text (Pennebaker, Chung, Frazee, Lavergne, & Beaver, 2014).
• Clout accounts for the expertise and confidence that writers project (Kacewicz et al., 2014).
• Authenticity measures honesty and the level of disclosure (Newman et al., 2003).
• Emotional Tone measures the positive and negative emotions in a text (Cohn, Mehl, & Pennebaker, 2004).

Analytical thinking and clout may be particularly relevant variables to editorial comments because they reflect the hierarchical dynamics synonymous with author–editor collaborations. Editors need to exhibit clout with their authors, but they must also acknowledge authors’ ownership of the text. Similarly, editors need to exhibit a high degree of analytical thinking to persuade authors to accept those comments. Authenticity and emotional tone might also provide insight into other relational dynamics, such as the openness and empathy that editors use when delivering their comments. Finally, the interactions among these four variables might also motivate inquiry into other technical texts. For example, technical communicators have hypothesized that texts with more emotional tone than analytical thinking would display a greater proportion of emotional rather than analytical word choices (Campbell, Naidoo, & Campbell, 2017). This amount of emotion could mark an inexperienced technical communicator, who cannot effectively deliver information to the intended audience.

I designed this study around the following research questions:

RQ1 What are the psychological dimensions that underlie editorial comments? Specifically, how do the comments reflect analytical thinking, clout, authenticity and emotional tone?

RQ2 How do social variables (gender, major, native speaker status) influence how editors deliver their comments?

Methods

In this section, I describe the sample as well as the measures used to explore the editorial comments.

Sample

Data were collected from 37 student editors who were enrolled across five sections of a graduate-level technical editing course. All student editors were majors in professional and technical communication. Twenty-three of the editors were female and 14 were male. Thirty-three of the editors were native English speakers, and four were nonnative speakers.

The corpus included the comprehensive comments and author cover letters for an assignment on editing job materials. The corpus included 41,146 words and 3,477 unique words. When examined by text type, the editorial comments corpus was 21,598 words and the letter corpus was 19,548 words. For this assignment, editors identified an author who needed a set of job materials for an upcoming employment opportunity. A set of job materials was either a job letter and resume or a personal statement and resume. All materials were edited in Microsoft Word with the track changes and commenting features. Editors submitted a copy of their edited materials as well as a cover letter addressed to the author that outlined the major editorial suggestions. Editors also submitted the targeted job posting as well as the author’s original materials. Since editors selected their own authors for this assignment, the existence of these authors and their original job materials were verified by the instructor.

Student editors followed the suggested graduate syllabus included in the instructor manual for Rude and Eaton (2011). Editors were at midsemester when they submitted their job materials assignment and had previously completed an intensive unit on copyediting. In preparation for this assignment, editors were assigned readings related to genre (Charney, Rayman, & Ferreira-Buckley, 1992; Eaton, 2009) and editorial commenting strategies (Eaton et al., 2008a; Locker, 2003; Mackiewicz & Riley, 2003).

Further, the editors worked with 37 different authors (23 females, 14 males). Despite the equal number of female and male editors and authors, 13 female editors worked with female authors, 10 female editors with male authors, 10 male editors with female authors, and four male editors with male authors. Twenty-eight of the authors had a STEM educational background and nine had a humanities background. Thirty-two of the authors were native speakers of English, and five were nonnative speakers. Editorial comments were collected with institutional IRB approval.

Measures

Data were subjected to a sentiment analysis via the Linguistic Inquiry and Word Count (LIWC) in addition to a regression analysis.

The LIWC is a sentiment analysis tool that analyzes texts on 80 different categories (Pennebaker, Booth, Boyd, & Francis, 2015). The proprietary LIWC dictionary contains almost 4,500 words, which were compiled from previous dictionaries and then assessed and refined by independent researchers (Pennebaker, Boyd, Jordan, & Blackburn, 2015). The latest edition of LIWC includes four summary variables based on algorithms the creators developed through their own research: analytical thinking (Pennebaker et al., 2014), clout (Kacewicz et al., 2014), authenticity (Newman et al., 2003), and emotional tone (Cohn et al., 2004). The algorithms for these summary variables are also proprietary and rescaled to reflect a 100-point scale (ranging from 0 to 100). The results section focuses on these four summary variables but additional output from LIWC is provided to better illustrate the findings.

Results from the four summary variables also served as dependent variables in the regression. The generalized regression model was Y = α + β1X + β2GRADE where Y is any dependent variable and X is a categorical variable, such as gender. The gender, native speaker status, and major of the editors and authors were all independent variables. [All editors were majoring in professional and technical communication and classified as non-STEM. Therefore, Editor’s Major was not a variable explored in this study.] In addition, Text Type (the cover letter to the author, comprehensive comments) and assignment Grade were analyzed. The latter variable was included to provide a quality measure to the study. On average, editors earned an 86.03% on the assignment (SD = 5.44). The level of significance for this study was p ≤ .05.

Results

Overall, the editorial comments scored the highest percentage in Clout (87.05%), Emotional Tone (77.53%), Analytical Thinking (67.52%), and Authenticity (27.00%). Results from the regression indicated that the independent variables explained 31.6% of Emotional Tone (Table 3.1). Two- and three-way interactions were also identified. The following describes the results in descending order of the LIWC summary scores followed by the related regression results.

Clout

The editorial comments averaged 87.05% in Clout (SD = 11.84, median = 89.84). This summary variable considers the social status, confidence, or leadership that writers display (Pennebaker, Booth, Boyd, & Francis, 2015). Technical editors use clout to project their professional competence as well as to establish trust with their authors.

Language markers associated with clout include personal pronouns, specifically how often writers reference themselves in comparison with others. Related research has demonstrated that communicators in high-status positions prefer to use second- and third-person pronouns, suggesting these leaders are more focused on group success than individual needs (Kacewicz et al., 2014).

Table 3.1 LIWC summary variables sorted by adjusted R-squared, F test, and p-value

	Adj R²	F	p-value
Tone	0.316	2.296	.006
Clout	0.105	1.331	.194
Analytic	−0.097	0.751	.782
Authentic	−0.159	0.615	.908

On average, 15.25% of the words in the editorial comments corpus were pronouns, 10.45% of which were personal pronouns (SD = 2.33). As shown in Table 3.2, technical editors preferred to deliver their suggestions to authors in second-person (M = 6.64, SD = 2.20).

The following examples illustrate some editorial strategies used to establish clout. The cover letter excerpt in [a] showed how one editor balanced her use of first- and second-person pronouns. She introduced her edits by highlighting details the author briefly mentioned in the job materials as well as by recalling a previous conversation with the author. The editor attributed text ownership to the author (your resume), but she used first-person and strong verbs (think, include, reformat) to deliver suggestions and convey edits.

[a] You also noted briefly that you wrote a grant for Kid Zone that led to $7,000 in funding, which I think you should re-emphasize in an “Accomplishments” section in your resume. I remember you told me once that you had written numerous grants for Tim that were all funded. You should include these in that section as well. I also reformatted your resume so that your resume’s “guts” are more offset from the section headings, which helps make better use of white space.

Similarly, the editor in [b] justified her word choice change by referencing language she found on the targeted company’s website. The editor used third-person (their website, their ideals) to reinforce the intended audience and rhetorical situation.

[b] The website highlights the company’s policy to deliver excellent customer service. By using the wording from their website, you display to Movie Tavern that you not only share their ideals, but also have done your research on the company.

Finally, the regression results identified a three-way interaction among Clout and Editor’s Gender, Author’s Gender, and Author’s Major. Clout decreased when male editors communicated with male authors who had STEM educational backgrounds (p = .03). However, this finding must be heavily hedged as only one male editor worked with a male STEM author. This editor’s letter scored a 37.86% in Clout, the lowest score in this summary variable.

Table 3.2 Personal pronouns analyzed in LIWC and rate of use in the editorial comments

Personal Pronoun	Rate of Use % (SD)
I	2.56 (1.68)
we	0.13 (0.28)
you	6.64 (2.20)
she/he	0.09 (0.38)
they	0.73 (0.57)

Emotional Tone

Technical editors use emotional tone to show empathy for the author’s concerns or to acknowledge their editorial expectations. The editorial comments corpus averaged 77.53% in emotional tone (SD = 15.68, median = 82.27). This summary variable considers the positive and negative emotions in the text. A high percentage of emotional tone suggests a more positive, upbeat style, while an average around 50% suggests a lack of emotionality or ambivalence (Pennebaker, Booth, et al., 2015).

Language markers used to convey emotional tone include words related to social orientation and cognitive processes (Cohn et al., 2004). On average, 3.43% of the words in the editorial comments corpus were associated with positive emotions compared with 0.31% that were associated with negative emotions. In the example below [c], the editor began her letter by establishing goodwill with the author. The inclusion of positive language (e.g., thank, you, we, opportunity, positive, happy, excited) established a rapport with the author, but, more important, buffered the discussion of the actual editorial comments.

[c] Thank you for the opportunity to edit your job materials. We both had positive experiences at Houston Catholic schools during our primary and secondary education, and I’m excited to see that you want to give back to the communities that shaped us from an early age. I’m also happy to see you’re searching for jobs in Houston; I’m also considering moving back to Houston after finishing my Master’s, so I’m excited about the possibility of living in the same city.

Editors also established emotional tone by using language to express cognitive processes. This type of language demonstrates concern for the organization and intellectual understanding of the issues addressed in the writing (Cohn et al., 2004). On average, 13.98% of the words in the editorial comments related to cognitive processes (SD = 2.88), and examples [d–h] illustrate how editors showed insight (link, feel, find, think); cause (how, because, makes); differentiation (if, or, but); and tentativeness (hopefully, some, else).

[d] You might want to mention what teams you led and how you communicated effectively. Link this to your college projects.

[e] Deleted because I’ve condensed the information in the second bullet. It shortens the list and makes the information easier to read.

[f] I think this is an appropriate level of directness. If you feel it’s too strong, we can find a way to ease off a bit.

[g] I couldn’t find the name of someone in HR or a hiring manager, but if you know the name, you should use it.

[h] Hopefully some of this helps! Let me know if there is anything else I can help with, and good luck with your job search.

The regression results indicated that the independent variables explained 31.6% of Emotional Tone. An interaction between Emotional Tone and Text Type was also identified. In other words, the relationship between these two variables influences the statistical significance. Editors presented a significantly higher emotional tone in their author letters than in their in-text comments (p = .001). Examples [a] and [b] demonstrate strategies editors used in the author letters to achieve this tone.

Analytical Thinking

The editorial comments averaged a 67.52% in Analytical Thinking (SD = 15.32, median = 68.88). This summary variable considers the formal, logical, and hierarchical thinking that writers display (Pennebaker, Booth, et al., 2015). In addition to delivering comments with authority and empathy, technical editors must understand how to synthesize their comments in ways that are logical and clear to authors.

Language markers associated with analytical thinking include function words, such as articles, prepositions, pronouns, auxiliary verbs, adverbs, conjunctions, and negations. Function words typically account for 55%–59% of written and speech communication and provide grammatical structure rather than lexical meaning to a sentence (Rochon, Saffran, Berndt, & Schwartz, 2000). However, relevant research has found that function words are often reliable markers of psychological states (Newman, Jones, & Ritter, 2016; Pennebaker et al., 2014). On average, 51.18% of the words in the editorial corpus were function words (SD = 3.71), slightly outside the typical range. Table 3.3 lists the eight function word types analyzed by LIWC, their rate of use in the corpus of editorial comments, and their rate of use in a corpus of over 50,000 college admissions essays. The latter corpus was analyzed by Pennebaker et al. (2014) and serves as a comparable for the present study. For example, these researchers found that categorical language markers, including articles and prepositions, were associated with formal and precise descriptions. Articles have been associated with concrete and formal writing (Biber, 1991) and prepositions with cognitive complexity (Pennebaker & King, 1999). In contrast, impersonal pronouns, conjunctions, and adverbs have been associated with texts that are more narrative and personal in style; their use also appears to mark lower performing writers in academic settings (Pennebaker et al., 2014).

Table 3.3 Function word types, examples, rate of use in editorial comments corpus, and rate of use in college admissions essays corpus

Word Types	Examples	Editorial Comments Rate of Use % (SD)	Admissions Essays Rate of Use % (SD)
Articles	a, an, the	7.31 (1.91)	6.83 (1.30)
Prepositions	all, below, much	13.16 (1.52)	14.71 (1.41)
Personal Pronouns	I, us, you, hers, they	10.14 (2.33)	10.88 (2.05)
Impersonal Pronouns	it, this, anything	5.82 (2.18)	5.03 (1.38)
Auxiliary verbs	are, did, have	7.40 (1.83)	8.25 (1.72)
Adverbs	even, just, usually	3.96 (1.38)	3.90 (1.04)
Conjunctions	and, so, until	5.92 (1.32)	6.41 (1.02)
Negations	no, never, not	0.73 (0.64)	1.04 (0.49)

The editorial comments included more articles than the admissions essays (7.31% compared with 6.83%) but fewer prepositions (13.16%, 14.71%). The remaining function words were relatively balanced between the corpora; however, the editorial comments contained slightly more impersonal pronouns (5.82% compared with 5.03%).

The following examples of articles and prepositions illustrate ways editors used formal and precise descriptions of objects, events, goals, and plans. In the cover letter excerpt [i], the editor used articles (the, an) to describe differences between the versions of the edited document; the version with all the Microsoft Word changes tracked, and the version with all those changes accepted. Another editor [j] used articles to instruct the author to include a phrase in her cover letter that echoes language used in the job posting. Further, in another cover letter excerpt [k], the editor used prepositions (of, about, to, than) to encourage the author to modify how she presented her performance testimonials in her resume. In the final example [l], the editor used prepositions to justify his edit (since) and to denote content placement (above).

[i] The first part is an edited version of the original document. The second part is the edited document with all changes and comments listed using Microsoft Word’s Review function. This allows you to review each change and comment individually.

[j] Add a phrase to convey that you are an active, modern artist. This allows you to echo a phrase from the job description that asks for “substantial knowledge of modern and contemporary art.”

[k] Also, I think your approach of including positive comments about your work is of value, but you might consider placing them on a separate attachment devoted solely to these quotes, rather than inserting them directly in the resume. Their inclusion in the resume makes it a bit busy and, at times, difficult to follow.

[l] The inclusion of the email seems redundant since it is listed above.

The regression identified an interaction between Analytical Thinking and Grade (p = .010). For every 1-point increase in grade, analytical thinking scores were expected to decrease by 1.146 +/− 0.425 (Figure 3.1). This is an interesting finding as Pennebaker et al. (2014) found that categorical language was consistently linked with better academic performance, whereas dynamic language was not. Further, the researchers found these effects were consistent across academic disciplines.

Authenticity

The editorial comments averaged 27.00% in Authenticity (SD = 17.82, median = 21.99). This summary variable considers how writers reveal themselves in an authentic or honest way. A lower number suggests a more guarded, distant form of discourse (Pennebaker, Booth, et al., 2015). Technical editors need to be authentic in their comments but also to maintain some distance, in part to demonstrate detachment from the document.

Relevant research on the summary variable has primarily focused on language markers that distinguish true from false stories as well as in emotional text types like health narratives and poetry. Therefore, the language markers associated with low authenticity include fewer self-references, more negative emotion words, and fewer markers of cognitive complexity (Newman et al., 2003). The editorial comments examined in this study were not compared with another corpus, so these types of findings may not directly inform author–editor collaborations. For example, technical editors included fewer self-references, but their strong use of second-person/you-inclusive language likely contributed to their clout score. Similarly, the editorial comments included a small amount of negative emotion words (0.31%); however, the words the LIWC dictionary classified as negative depended on context.

[Figure 3.1: Interaction between Analytical Thinking and Assignment Grade. — image to be added.]

The word tense appears in the editorial comments corpus 14 times, but it is never associated with a negative situation. In fact, tense collocated with words like past, present, and verb [m]. Similarly, the editor in [n] uses the word cut to express concern that the author’s contact information will get cut off during the printing process if it is in the Microsoft Word document header instead of within the text.

[m] Importantly, make sure you use past tense for jobs you no longer work at. Many of the older jobs flip between present and past tense.

[n] Also, your contact information was taken out of the header and placed in the body of the document. This ensures that the information does not accidently get cut off when printed.

The regression identified a significant two-way interaction among Authenticity and Author’s Gender and Author’s Major (p = .043). Authenticity appeared to increase if the author was male and had a STEM educational background. The editors worked with six different male STEM authors.

Discussion

The student editors in this study delivered comments that showed expertise and empathy. Overall, their comments included high percentages of clout, emotional tone and analytical thinking. The following section discusses the results of this study.

Editors’ balance of personal pronouns reflected their attempts to establish clout. Specifically, the comments focused substantially more on the author (you, your) and intended readers (they, their) than the editor (I, my). An increased use of pronouns like we and you reflect that high-status individuals are more collectively oriented than other-oriented (Kacewicz et al., 2014). While authors make the final decision to accept or reject editorial suggestions, the editors must first establish professional competence. They establish this competence by focusing their attention outward, toward their authors. The language features that inform LIWC’s clout variable encompass many of the recommendations for creating a you-attitude, a writing style that considers communication from the reader’s point of view (Locker, 2003). Students will likely be familiar with techniques for establishing goodwill; however, having them explore how they apply personal pronouns in their own writing assignments (and even in their personal emails and text messages) can make abstract concepts like balancing power dynamics more concrete. Examples [a]–[b] model ways that editors established clout with their authors.

The editorial comments also contained a high percentage of emotional tone, suggesting a more positive writing style. Establishing a rapport with authors becomes increasingly important as editors are tasked with delivering constructive criticism (but criticism, nonetheless). Student editors in this study used positive language related to insight, cause, and differentiation that delivered the comments with authority but also acknowledged authors’ ownership of their job materials. The regression also showed the author cover letters gave editors a significant opportunity to use an emotional tone. In their cover letters, editors thanked authors for the chance to review their work as well as outlining how to approach the revision process. The letters functioned as a rhetorical buffer that acknowledged the anxiety synonymous with opening electronic files marked with red tracked changes. However, as with any emotional appeal, editors must be mindful of using a tone that could conceal any legitimate structural and organizational issues with the document. Authors are typically more concerned with understanding the comment rather than the structure used to convey that comment (Eaton et al., 2008a, 2008b; Mackiewicz & Riley, 2003). Editors must be aware that too much emotional tone can diminish their perceived clout and analytical thinking. Examples [c]–[h] model ways that editors established emotional tone with their authors.

Editors’ use of categorical language (articles, prepositions) also accounted for a high percentage of analytical thinking. Articles are associated with concrete and formal writing and prepositions are used to link ideas. On the other hand, dynamic language is associated with impersonal pronouns, conjunctions, and adverbs as well as with lower academic performance (Pennebaker et al., 2014). Interestingly, the regression indicated that editors who earned the lowest assignment grades often displayed higher analytical thinking. While this finding contrasted with the Pennebaker et al. (2014) study of college admissions essays, a closer examination found that these student editors also scored below the average on clout or emotional tone. Examples [i]–[1] model ways that editors established analytical thinking with their authors.

In contrast, the editorial comments included a low percentage of authenticity, which considers how writers reveal themselves in an authentic or honest way. As mentioned earlier, the relevant research that informed this LIWC summary variable was conducted to distinguish true from false stories (Newman et al., 2003) as well as measure the level of disclosure in emotional narratives (Boals & Klein, 2005). Therefore, the language markers associated with low authenticity include fewer self-references, more negative emotion words, and fewer markers of cognitive complexity. However, the low frequency of editorial self-references was supplemented with a high frequency of references to authors, resulting in the high percentage of clout. Similarly, words classified as negative in LIWC did not typically connect to any negative sentiments within editorial comments. For example, tense was associated with verb tense rather than a heightened situation. Anecdotally, I explored the Authenticity variable in a variety of other technical text types. The LIWC reported a score of 21.69% on a corpus of critical reviews and 14.93% on a corpus of white papers. These observations further suggest that this LIWC variable might not be suitable for studying technical writing. In fact, consistently low scores in Authenticity might instead suggest that effective technical writing contains an appropriate level of professional distance with its readers. Examples [m]–[n] model ways that editors established authenticity with their authors.

The regression identified few significant interactions related to the psychological dimensions and the gender, major, and native speaker status of the editors and authors. The insignificance here, however, might actually be of significance to technical editing students and teachers. Editors did not adjust how they constructed their comments based on these social variables. Eaton et al. (2008a) made similar observations as well. However, other variables might inform these comments and should be explored in future studies. For example, the fact that authors identified (and therefore knew) their author for this assignment could have resulted in the high percentage of emotional tone.

Pedagogical Applications

The results of the study have implications for teaching students the psychological dimensions that underlie editorial comments. Understanding how editors apply these dimensions could further improve author–editor collaborations.

First, it is important to highlight that students who participated in this study were responding to an authentic situation rather than a scenario. Allowing students to interact with actual authors allows them to apply the concepts they read about and discuss in class. Students identified their own author for this assignment; however, this familiarity did not appear to decrease engagement level. In fact, the high percentage of clout suggests the editors used the assignment as an opportunity to demonstrate their developing expertise.

When introducing students to the concepts of clout, analytical thinking, authenticity, and emotional tone, begin classroom discussion on a conceptual level. The examples provided throughout this chapter can be used to illustrate these applications and motivate class discussion. Describe the four dimensions and their related language markers and then facilitate students’ initial applications of these dimensions.

Once students understand how language markers convey sentiment, encourage them to explore these concepts in their own technical writing as well as in their personal emails and text messages (as a comparison). The LIWC is not a freely available software program; however, you can emphasize how function words—pronouns, articles, and adverbs—reflect cognitive processes and social order. This type of discussion can also contextualize grammar and copyediting, which, while foundational, are typically the drier instructional units for students.

You can also use technology to assist students’ understanding of these psychological dimensions. As a classroom activity, ask students to calculate the Empathy Index of a piece of writing by using the find and replace feature in Microsoft Word. The Empathy Index measures reader focus (Cleland, 2014). To calculate the index, count the number of references to the target readers and subtract it from the number of references to the writer or the organization. The find and replace feature allows students to highlight and count these instances with ease. Ideally, students should end up with a positive number; the higher the number, the more reader-focused the writing. This activity can connect to a discussion of all four psychological dimensions.

Introduce your students to AntConc (Anthony, 2006) if you want to explore language markers through a more fine-grained approach. AntConc is a freely available text processing program that allows users to generate word lists, concordances, and collocates of texts. Boettger and Wulff (2014) provided a guide for integrating text processing tools into the technical editing classroom and steps on how students could create their own corpus. A class corpus of editorial comments, for example, could help students refine their editorial style over the semester. Teachers can build this corpus over several semesters to increase the generalizability of the findings.

Conclusions

This study is not without its limitations. Sentiment analysis is one way to analyze language and any results produced by it should be scrutinized. Sentiment analysis tools are also created differently. The indices in LIWC, for example, are based on simple word counts that do not consider issues like negations or part-of-speech tags. Similarly, the algorithms for the four LIWC summary variables are proprietary, which can make analysis more difficult. New sentiment analysis tools are becoming increasingly available, and their construction and findings can benefit the field of technical communication.

The sample explored in this chapter also invites its own limitations. This study examined the editorial comments written by developing student editors (rather than professional editors) on a single assignment. Additional data could suggest how the editorial tone of students evolves over the course of the semester. Additionally, students selected their authors for this project, which could have influenced the results. Finally, the social variables explored in this study are only a few factors that could influence how editors communicate with their authors. Future studies in this area as well as studies on how authors perceive the clout, emotional tone, analytical thinking, and authenticity of the comments can improve the author–editor relationship and further advance the scholarship in technical editing.

Pedagogical Practicalities

• Understanding the language markers that inform clout, analytical thinking, emotional tone, and authenticity can improve how technical editors deliver comments to their authors.
• The examples in this chapter are included to motivate in-class discussion about how to construct editorial comments as well as to encourage students’ independent learning.

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