Revista Nebrija de Lingüística Aplicada a la Enseñanza de las Lenguas. Vol. 20. Núm. 40 (2026).

ISSN 1699-6569

Assessing emotion in L2 writing: Validating Watson NLU with emotional vocabulary

training

Evaluación de las emociones en la escritura en L2: Validación de Watson NLU con

entrenamiento de vocabulario emocional

María Jesús Sánchez a, Elisa Pérez-García b, Beatriz Bermúdez-Margarettoc

a Universidad de Salamanca, mjs@usal.es

b Universidad de Salamanca, elisapg@usal.es

c Universidad de Salamanca, bermudezmargaretto@usal.es

Abstract

Affective word values have been widely studied across languages, often focusing on isolated words due to the

difficulty of assessing emotionality in texts. This study examines whether written emotional content can be reliably

captured using a specific software tool (Watson Natural Language Understanding). Thirty-three Spanish

undergraduates wrote 150-word autobiographical texts in their L2 (English) before and after a training with

emotional vocabulary. Normative valence ratings of content words obtained in the pre- and post-training phases

were compared with sentiment scores generated by Watson NLU. Strong positive correlations were found between

sentiment and normative valence scores in both phases, with stronger relations at post-training. Regression

analyses confirmed that sentiment scores significantly predicted normative valence. Importantly, while normative

valence did not differ between phases, sentiment scores increased after training. These results suggest that Watson

NLU is a valid and sensitive tool for assessing emotionality in written language and its modulation through training

during text writing.

Keywords. Emotion, bilingualism, sentiment, valence, emotional training

Resumen

Los valores afectivos de las palabras se han estudiado ampliamente en distintas lenguas, a menudo centrándose

en palabras aisladas debido a la dificultad de evaluar la emocionalidad en los textos. Este estudio analiza si el

contenido emocional escrito puede captarse de forma fiable mediante Watson Natural Language Understanding.

Treinta y tres universitarios españoles escribieron textos autobiográficos de 150 palabras en su L2 (inglés) antes

y después de un entrenamiento en vocabulario emocional. La valencia normativa de las palabras de contenido se

comparó con las puntuaciones de sentimiento generadas por Watson NLU. Ambas medidas mostraron

correlaciones positivas y fuertes en las fases pre- y post-entrenamiento, siendo mayores tras el entrenamiento. Los

análisis de regresión confirmaron que las puntuaciones de sentimiento predijeron significativamente la valencia

normativa. Aunque no se observaron cambios en la valencia normativa, las puntuaciones de sentimiento

aumentaron tras el entrenamiento, lo que indica la sensibilidad de la herramienta a la modulación emocional del

lenguaje durante la escritura de textos.

Palabras clave. Emoción, bilingüismo, tono emocional, valencia, entrenamiento emocional

DOI: 10.26378/rnlael2040659

Recibido: 09/01/2026 - Aprobado: 1/04/2026

Publicado bajo licencia de Creative Commons Reconocimiento Sin Obra Derivada 4.0 Internacional

1. Introduction

Word emotionality has been investigated in first (L1) and second (L2) languages (Imbault et al., 2021;

Warriner et al., 2013) by means of different measures: subjective ratings (Dewaele, 2004; Pavlenko,

2005), more objective, behavioral data (response times or accuracy rates during reading or word

categorization), or physiological (skin conductance, Harris, 2004; Harris et al., 2006) and

neurophysiological indices (neural responses, Opitz & Degner, 2012). This research has shown that

emotional words are perceived and evaluated as more emotionally extreme (i.e., more positive or

negative) in L1 than in L2 (Sánchez et al., 2025; Caldwell-Harris, 2015; Ferré et al., 2010) and that their

processing is more automatic and effortful in the L1 too, leading to higher physiological reactivity (skin

conductance, electromyography, pupillometry) and increased or faster brain responses (Conrad et al.,

2011; Fan et al., 2018; Foroni, 2015; Opitz & Degner, 2012; Toivo & Scheepers, 2019; Winskel, 2013).

Although results in the production domain are more scarce, recent evidence also shows higher emotional

verbal fluency in L1 than L2 (Lam & Mardquardt, 2022) as well as more diverse emotional vocabulary

during L1 than L2 text production (Kyriakou et al., 2024; Vidal Noguera & Mavrou, 2025; Pavlenko &

Driagina, 2007). Similarly, it has been reported more gestures usage during the retelling of emotional

experiences in L1 than L2 (Emir Özder et al., 2023). Overall, this research systematically highlights that

L2 speakers tend to experience their L2 as less emotional.

Nonetheless, there is still no unanimous conclusion on the reduced emotional sensitivity in L2 (see, for

instance, lack of L1-L2 differences in Eilola & Havelka, 2010; Kazanas & Altarriba, 2016), with various

factors such as age of L2 acquisition, proficiency and exposure potentially modulating the L2

emotionality (Conrad et al., 2011; Opitz & Degner, 2011). In this line of research, the common reduced

emotionality in L2 has been attributed to weaker associations between words and their emotional

contexts, largely due to fewer meaningful encounters with those words (Pavlenko, 2012). Unlike L1, L2

is often learned in formal instructional contexts such as the school or university, where language use

tends to be less spontaneous and less embedded in emotionally rich interactions. As a result, the limited

exposure to words in socially and affectively meaningful contexts has been proposed as a key factor

underlying the lower emotional resonance in L2.

From this perspective, it is reasonable to hypothesize that increasing the number of such encounters and

embedding learning in richer emotional contexts—as implemented in the present study—may help

counteract this reduced emotionality. In this sense, this study aims to examine whether a pedagogical

intervention enhance the emotional content of learners’ written production by increasing meaningful

encounters with emotional vocabulary and promoting deeper lexical-semantic processing. Among the

instructional strategies that may help enhance emotionality in L2 within formal learning contexts, the

summary strategy appears particularly relevant. Summarizing involves reducing a source text to its

essential ideas and therefore requires a demanding higher-order cognitive process in which learners

synthesize content and identify the most relevant information (Khoshsima & Rabani Nia, 2014). This

complex task engages both cognitive and metacognitive operations, including scanning, skimming,

inferencing, and information construction (Keck, 2006; Mokeddem & Houcine, 2016), making reading

and writing closely interdependent. Previous research has shown that the use of summarization promotes

reading comprehension, writing development, and vocabulary acquisition (Hsiang et al., 2020; Keck,

2014; Shokrpour et al., 2013; Stevens et al., 2019), likely because it encourages deeper lexical-semantic

processing. Such effortful processing may strengthen the mental representation of L2 words and

facilitate vocabulary learning, including emotionally charged lexical items. Thus, it was expected that

producing personal summaries of texts containing both positive and negative emotional content would

increase learners’ encounters with emotional vocabulary in meaningful contexts, and that this repeated,

elaborative engagement would promote a stronger integration of emotional language in L2.

When assessing emotional content in written texts, two complementary approaches can be adopted:

focusing on the emotional value of individual words or analysing emotion at the level of the text as a

whole. Previous studies on emotional processing in L2 have predominantly examined isolated words

(Kousta et al., 2009; Opitz & Degner, 2012; Palazova et al., 2011), whereas evidence at sentence or text

level remains comparatively limited (Tang & Ding, 2024; Sheikh & Titone, 2016; Vidal Noguera &

Mavrou, 2025; Kyriakou et al., 2024), largely because capturing the emotional tone of an entire text is

methodologically more complex. To address this limitation, the present study also explores whether

emotional content in written production in L2 can be captured using recently developed AI-based tools,

specifically IBM Watson Natural Language Understanding (hereafter, Watson NLU1). This natural

language processing system extracts meaning from both structured and unstructured language data and

can provide information about sentiment expressed in text. Unlike traditional approaches based solely

on lexical items, this tool estimates sentiment at the phrase or text level, assigning a score on a continuum

from negative to positive (from –1 to +1), thereby allowing the analysis of the overall emotional tone

conveyed in a text rather than only the valence of isolated words.

At this point, it is worth clarifying the distinction between lexical valence analysis and sentiment

analysis carried out at single-word or text level, respectively. Lexical valence refers to the affective

polarity associated with individual words along a bipolar continuum from negative to positive,

traditionally derived from normative affective databases such as those developed by Warriner et al.

(2013) in L1 English. These databases provide emotional ratings for isolated words based on native-

speaker judgments, while comparable L2 norms for bilinguals and foreign language learners remain

scarce (Imbault et al., 2021). By contrast, sentiment refers to the overall evaluative attitude or emotional

tone expressed by the writer toward a topic within discourse, making it inherently context-sensitive.

Although the terms emotion and sentiment are sometimes used interchangeably, emotion typically refers

to internal affective states, whereas sentiment reflects how those states are linguistically conveyed in

context. From this perspective, sentiment analysis may offer a more naturalistic way of assessing

emotional tone in writing, because it evaluates meaning at text level rather than assigning normative

polarity to lexical items (Taherdoost & Madanchian, 2023). In addition, AI-based tools like Watson NLU

provide important methodological advantages: they enable consistent, replicable analysis of large text

samples and reduce the degree of subjectivity associated with manual coding procedures (Pérez-García

& Sánchez, 2020).

Regarding Watson-based sentiment tools, the earlier version of Watson NLU, IBM Watson Tone

Analyzer, has been shown to be particularly useful for examining emotional features of language in

diverse contexts. For example, Maleki et al. (2023) investigated whether financial incentives influence

the production of health-related content on social media by comparing posts from Steemit, a platform

that rewards user participation, with posts from Reddit, where no such incentives are provided. Their

analysis showed that posts written in the incentive-based environment displayed a more confident and

analytical language style, were less tentative, and expressed more joy and less negativity than those

published on the non-incentivized platform. Similarly, Steffens et al. (2021) used Watson Tone Analyzer

to examine whether the source of funding influences how medical research findings are written. By

examining emotional features in the texts—such as expressions of anger, fear, joy, and sadness—as well

as language style (for example, whether the writing sounded more analytical, confident, or cautious),

they found that studies without commercial funding tended to use language that reflected more fear and

a more impersonal tone than commercially funded studies. Langerhuizen et al. (2021) also analysed

patients’ online comments about healthcare providers and found that comments characterized by joy and

confidence were associated with higher service ratings, whereas sadness and tentativeness were linked

to lower evaluations.

This tool has also been applied in the field of music. Marouf et al. (2019), for instance, analysed a large

corpus of English song lyrics and classified them according to both language style (analytical, confident,

tentative) and emotional tone (anger, fear, joy, sadness). Extending this line of work, Somse et al. (2022)

combined tone detection with voice analysis to identify users’ emotional states and subsequently

recommend music that matched their mood. More practical research has been done on neuromotor

disability to help dependent people (Jain & Verma, 2020). The authors presented a solution to control

the movement of people who speak clearly but cannot walk because of their disability, and proposed a

machine-learning based methodology to detect emotion from speech to help people to interact better

with their surroundings. Likewise, Gain and Hotti (2017) suggested that emotional tones and linguistic

patterns extracted from text may also contribute to assessing personality traits and social tendencies.

Taken together, these findings demonstrate the usefulness of Watson-based language analysis tools and

indicate that they are sufficiently robust to capture emotional and linguistic variation in written discourse

in domains as diverse as healthcare, social media, music, and scientific communication.

However, despite this potential, most previous applications of Watson tools have been developed outside

the fields of philology and language teaching, which highlights the novelty of applying this tool to the

study of emotional content in L2 written production. Nonetheless, a recent study (Sánchez et al., under

review) applied this software to examine the effects of two instructional strategies—summary and

guessing—on emotional writing performance in L2 English. In that study, learners’ written productions

before and after the intervention were analysed using Watson Tone Analyzer to quantify the emotional

tone of each text. Although the intervention did not produce statistically significant changes in overall

emotional writing performance—measured as the average score of four emotional dimensions (anger,

fear, joy, and sadness)—it did lead to a reduction in the analytical tone of the texts produced by both

experimental groups. In this way, the tool made it possible to identify that both teaching strategies were

similarly effective in encouraging learners to write in a less analytical and comparatively more affective

manner.

Based on this rationale, the main aim of the present study was to further investigate possible changes in

the emotional content of L2 written texts following specific instruction based on the summary strategy2.

It was expected that the summary-based training would modulate the emotional content in L2 written

production. Consequently, changes after training were expected to be reflected in both normative

valence scores and sentiment scores. More specifically, the study aimed to test the validity of Watson

NLU for measuring emotional language in written texts. It was hypothesized that sentiment scores

(generated by Watson) would correlate with and predict mean normative valence scores (obtained from

affective ratings traditionally used in L2 research) of the L2 written texts collected both before and after

the teaching instruction.

2. Method

2.1. Participants

A group of Spanish undergraduate students (n=33, 7 males, R=7, Mage= 18.27) enrolled in the English

Studies degree (University of Salamanca, Spain) participated in this study. They were B2-level English

students (Council of Europe, 2018) who volunteered to participate in the research. They were informed

about the purpose of the study and signed an informed consent form. This was administered to account

for the study that was being carried out and to request permission so that their data could be used

globally, never individually.

2.2. Procedure

Participants wrote an autobiographical text (approximately 150 words) in their L2 (English) before

(pretest) and after instruction (posttest) with emotional language (see section Instruction for more

details).

2.1.1. Pretest and Posttest

In the pretest phase, students wrote about a dream (150 words) they had had in approximately 30

minutes. In the posttest phase, they wrote another short text (150 words) about a personal experience

(30 minutes); this phase took place two weeks after the instruction sessions to measure long-term effects.

The topics were chosen to generate texts in which participants felt inclined to use emotional vocabulary

and expressions through the retelling of subjective autobiographical experiences (Pavlenko, 2012).

These activities were administered online through the students’ virtual campus.

2.1.2. Instruction

Instruction was provided in two 50-minute sessions one week apart, and the tasks participants completed

during these two sessions were paper-based. In the first instruction session, a text adapted from a blog

post was used to address negative, high-arousal emotions related to anger (see Appendix 1). Participants

were first asked to read the text to become familiar with the words and emotional expressions, and then

they were asked to summarize it. They were advised to outline the main ideas before summarizing the

text to help them paraphrase and rephrase ideas. While writing the summary they could look at the text,

and guidance and support were always provided by the instructor, in order to motivate the participants

and make them feel more confident (Méndez López, 2016).

In the second instruction session, the input text used was adapted from a blog post on positive (pleasant)

feelings (see Appendix 2). The procedure was the same as in the first session. Participants read and

summarized the text, and while writing the summary they were allowed to look at the text and

encouraged to ask any question.

The fact that the first text dealt with negative emotions (session 1) and the second with positive emotions

(session 2) did not jeopardize the validity of the research because in the pre- and posttest participants

were not directed towards positivity or negativity and could express themselves freely with the

emotional terms learned in the instruction sessions.

2.3. Data analyses

A quasi-experimental pretest / posttest design (Larson-Hall, 2010; Rogers & Révész, 2020) was used to

examine the effect of the teaching summary strategy on students’ emotional L2 writing performance

(cf., Sánchez et al., 2026, where this design was applied to test the effect of teaching strategies on

vocabulary learning in EFL).

Students’ texts were analyzed using two different yet complementary indices: lexical valence scores

derived from a normative database and sentiment scores obtained through automated sentiment analyses

by means of the Watson NLU software tool. For the lexical valence analysis, each text produced in the

pre- and post-instruction phases was first corrected for spelling and tokenized. Once all the words from

each text were extracted, the content words (nouns, adjectives, verbs, and adverbs) were selected and

lemmatized. Thus, words were reduced to their base form (e.g., singular nouns and infinitive verb forms)

in order to facilitate matching with the normative database. Then, valence scores for each lexical item

were obtained from the set of English affective norms provided by Warriner et al. (2013), which provides

ratings on emotional valence of a large set of words, in a scale ranging from 1 (very negative) to 9 (very

positive). Nonetheless, content words that were not present in the database were excluded from the

analysis (23.69% of the words in the pretest texts and 23.87% in the posttest texts). For each text in both

phases, the mean valence score was computed by averaging the valence ratings extracted across all

content words, providing an index of the affective polarity of the lexical items used in the text. This

measure captures the emotional characteristics of the vocabulary, rather than the evaluative tone of the

discourse as a whole. For descriptive purposes, the proportion of valenced words in each text was also

extracted as an index of emotional vocabulary density. Following common practices in previous studies,

words with valence scores ≥ 6 and ≤ 4 were classified as emotional, and its proportion relative to the

total number of words matched with the normative database was calculated for each text.

Regarding sentiment, scores for each text in pre- and posttest phases were automatically generated by

the Watson NLU tool. The system applies machine-learning models trained on large text corpora to

analyze the linguistic features in the input text, providing a computational estimation for the emotional

tone expressed in the text. The resulting sentiment scores range from -1 to 1, where values closer to 1

indicate a more positive tone, values closer to -1 reflect a negative tone, and values around 0, indicate a

more neutral evaluative tone. Therefore, whereas lexical valence operates at the level of individual

words, sentiment scores indicate the overall polarity of texts as a whole, taking into account the linguistic

context in which words appear.

Then, two different analyses were carried out with both normative valence and sentiment scores

obtained for the texts. First, to determine the relationship between sentiment and normative valence

scores, correlational and regression analyses were carried out. Thus, Pearson correlations were

computed between both sentiment and normative valence scores, separately for written texts obtained

in pre- and posttest phases. Then, regression analyses were carried out considering sentiment scores as

predictor or independent variable, and mean valence scores as dependent variable, separately conducted

for texts written in the pre- and posttest phases. Second, to determine the effect of the specific training,

paired-sample t-tests were performed to contrast written L2 texts in pre- and post-training phases,

separately considering normative valence and sentiment scores. Statistical analyses were conducted with

the SPSS package (IBM, version 23) and the R software (Core Team, 2021) was used to plot and

visualize results by means of ggplot2 package (Wickham, 2016) implemented in R studio (version

2022.02.0).

3. Results

Correlational analyses conducted between sentiment and normative valence scores obtained in the

pretest phase demonstrated a strong, positive relation between both indices (r=.55, p=.001). The relation

was found even stronger when indices obtained in the posttest were analyzed (r=.78, p<.001).

Importantly, regression analyses confirmed that sentiment scores significantly predicted normative

valence ratings, showing a strong, linear relation in the pretest [F(1,32)=13.9, R²Adj.=.28, p=.001; see

Graph 1, left panel]. Moreover, such relation was found even stronger in the posttest phase

[F(1,32)=50.21, R²Adj.=.60, p<.001; see Graph 1, right panel]. These results confirm that more positive

sentiment scores actually predict more positive valence scores obtained through normative ratings, thus

indicating the Watson NLU as able to determine the emotional tone of a given text.

Graph 1. Normative valence scores obtained for each written text in pretest (left panel) and posttest (right panel)

as a function of sentiment scores (each point represents the mean obtained for each written text, for normative

valence and sentiment scores)

Regarding the t-test carried out to compare the emotionality of L2 texts in pretest vs. posttest phases, no

differences were observed in normative valence scores between both training phases [t(32)=0.815,

p=0.421, mean pretest= 5.966, mean posttest= 6.006, mean difference= -0.04]. See Graph 2 (left panel).

Indeed, the proportion of emotionally valenced words was similar in both phases (pretest: 61.50%,

posttest: 60.72%) and did not differ significantly across phases (p>.05). Conversely, the analysis

considering text-derived sentiment values revealed that the emotionality of the written texts significantly

increased after the specific training [t(32)=-2.01, p=.05, mean pretest= -0.002, mean posttest= 0.244,

mean difference= -0.246]. See Graph 2 (right panel).

Graph 2. Distribution of normative valence scores (left panel) and sentiment scores (right panel) for L2 written

texts across pretest and posttest phases. Dots within each boxplot represent the mean obtained in normative

valence and sentiment scores for each pretest and posttest condition; the asterisk indicates significant differences

for the contrast between sentiment scores in pretest and posttest phases

4. Discussion

The first aim of the present study was to investigate whether specific training could promote changes in

L2 emotionality in written production. Previous research has consistently reported differences between

L1 and L2 in the processing and use of emotional language, as shown through subjective evaluations

and objective measures such as behavioral and neurophysiological responses (Caldwell-Harris, 2015;

Ferré et al., 2010; Foroni, 2015; Kousta et al., 2009; Opitz & Degner, 2012). Since these differences are

often attributed to L2 learning in affectively detached contexts, such as formal instructional settings, it

was hypothesized that increasing learners’ exposure to L2 words in emotional contexts through targeted

training would encourage the use of emotional language and, consequently, improve emotional L2

communication.

To test this hypothesis, B2-level learners of English as an L2 underwent a training based on the summary

strategy. Emotionality in their written production before and after the intervention was assessed using

two complementary indices: normative valence scores derived from emotional norms in English

(Warriner et al., 2013) and sentiment scores estimated through Watson NLU. Overall, results confirmed

the usefulness of the training to enhance L2 emotionality in writing production. Notably, this

improvement was more clearly detected through discourse-level sentiment scores than through the

traditional word-based approach based on normative valence ratings.

The emotional tone captured by sentiment scores significantly increased after the application of the

summary strategy. This result supports the usefulness of this teaching approach for enhancing emotional

expression in L2 written production and aligns with previous research showing the benefits of

summarization for reading comprehension and vocabulary learning (Hsiang et al., 2020; Keck, 2014;

Shokrpour et al., 2013; Stevens et al., 2019). Summarizing likely promotes deeper processing of

affective language because learners must re-elaborate linguistic content through metacognitive

operations such as identifying key information, paraphrasing, and synthesizing ideas during text

production (Keck, 2006; Mokeddem & Houcine, 2016). This process may facilitate the integration of

emotional L2 vocabulary into memory and improve later access to such vocabulary during writing. It is

also possible that this strategy could strengthen learners’ ability to express emotions in oral

communication or even influence emotional experience in L2, although these possibilities remain open

questions for future research.

However, contrary to our predictions, the emotional valence of the words used in the texts, measured

through standardized normative ratings in English (Warriner et al., 2013), did not change significantly

across testing phases, despite a slight increase after training. The different sensitivity shown by

sentiment and valence scores may be explained by the distinct dimensions captured by each measure,

particularly in a relatively small sample size. Whereas sentiment analysis evaluates the emotional tone

of the text as a whole, emotional valence reflects the degree of pleasantness associated with individual

lexical items, with the overall score calculated as the mean valence of the words produced. In this sense,

sentiment analysis extends beyond the positive or negative connotations of isolated words and captures

the emotional orientation emerging from discourse as a whole. This more context-sensitive and

naturalistic perspective may therefore be especially appropriate for detecting changes in emotional

expression in L2 writing. On a more interpretative level, this differential pattern might suggest that the

pedagogical intervention influences how learners organized and conveyed emotional meaning at the text

level rather than substantially modifying the emotional polarity of individual lexical items.

To our knowledge, this is the first study, together with Authors (Sánchez et al., under review), to examine

changes in written L2 emotionality following a specific teaching intervention. Previous research has

mainly focused on differences in the number of emotional words produced in L2 texts, showing that

learners with higher proficiency tend to use more emotionally marked vocabulary (Dewaele & Pavlenko,

2008; Kyriakou & Mavrou, 2023; Mavrou et al., 2025). Other studies have shown that teaching

strategies involving emotional engagement can help learners build emotional associations with new

vocabulary, which supports vocabulary retention (Pishghadam & Shayesteh, 2016). Positive emotional

stimuli have also been used to facilitate L2 vocabulary learning (Kralova et al., 2022), although changes

in the emotionality of new vocabulary gained through the intervention were not specifically tested. In

addition, teaching approaches based on multimodal elaborative processing—such as imagination, visual

and spoken language, body expression, and gestures—have been found to improve emotional

vocabulary learning in the EFL classroom (Sánchez et al., 2026) and to influence learners’ perception

of emotional word connotations (Sánchez et al., 2025).

A second objective of the present study was to test the usefulness of Watson NLU as a tool for measuring

emotional language in L2 written texts. To address this aim, the study moved beyond the traditional

word-based approach commonly used in previous research (e.g., Kousta et al., 2009; Opitz & Degner,

2012; Palazova et al., 2011) by analyzing emotionality at the discourse level, that is, considering the text

as a whole rather than considering the valence of isolated lexical items. This made it possible to assess

whether sentiment scores generated by Watson NLU converged with mean normative valence scores

derived from affective ratings traditionally used in L2 research. Results showed that sentiment scores

not only correlated with, but also predicted, the mean emotional valence of the words used in the texts,

particularly after the training. This finding supports the hypothesis that sentiment analysis is sensitive

to variations in emotional language and provides internal validation for the sentiment-based approach

adopted in this study. In this sense, the convergence between sentiment and normative valence indices

supports the validity of Watson NLU as a reliable tool for capturing emotional language in L2 written

discourse and for detecting changes associated with instructional intervention. Furthermore, these

findings extend previous research conducted in other fields showing the utility of AI-based language

analysis tools for characterizing emotional aspects of written language (Gain & Hotti, 2017; Jain &

Verma, 2020; Langerhuizen et al., 2021; Maleki et al., 2023; Marouf et al., 2019; Somse, 2022; Steffens

et al., 2021), and suggest that such tools can also capture variation in emotional expression associated

with language learning experiences.

Taken together, these findings contribute both to pedagogical research on how instructional strategies

can enhance emotional expression in L2 writing and to methodological research by supporting the use

of AI-based sentiment analysis tools for studying emotional language in L2 contexts. From an

educational perspective, the results suggest that tools capable of evaluating emotional tone in written

discourse may help teachers carry out more objective assessments of students’ written production

beyond the sentence level. In addition, automated analysis considerably reduces the time and effort

typically required to evaluate student texts. More broadly, this type of naturalistic assessment may help

researchers further investigate L1–L2 differences in emotional processing. Future studies may use this

AI tool or similar ones to further explore their potential and confirm the results obtained here.

Additionally, future research could examine whether sentiment analysis tools interpret emotional tone

differently across genders, which may help identify potential biases in automated assessments and

contribute to a more robust and equitable use of these technologies.

 
  
Notas  
1. The Watson Natural Language Understanding is the equivalent to the Watson Tone Analyzer, which is no longer 
available after 24 February 2023 (https://www.ibm.com/demos/live/natural-language-understanding/self-service).  
2. Part of the texts produced by students, selected from a larger dataset belonging to a study currently under review 
(Sánchez et al., under review), were compiled and analyzed between March and May 2023. 
 
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Anexo I.  
Text used in the 1st instruction session (full version) 
TEXT 

(Adapted from the blog article “Things to do when you’re feeling angry with someone”)

We all have lots of misunderstandings and annoyances (e.g., I felt angry because I have always struggled with

saying n. felt misunderstood and judged). With this in mind, I put together this guide to dealing with anger.

SIT WITH YOUR ANGER

1. Allow yourself to feel angry. You may think you need to cover “negative feelings” with positive ones. You

don’t. You’re entitled to feel whatever you need to feel. We all are.

2. Feel the anger in your body. Is your neck tense? Is your chest burning? Is your throat tightening? Are your

legs twitching? Recognize the sensations in your body and breathe into those areas to clear the blockages that

are keeping you feeling stuck.

EXPLORE YOUR ANGER

3. Check in with your mood before the incident. Were you having a bad day already? Were you already feeling

annoyed or irritated? It could be that someone’s actions were the straw that broke the camel’s back, but not

fully responsible for creating these feelings.

4. Ask yourself: Why is this bothering you so much? Is it really what someone else did, or are you feeling angry

because of what you’re interpreting their actions to mean? (For example, you may think that your boyfriend not

showing up means that he doesn’t respect you, when he may have a valid explanation).

5. Put it in a letter. Now that you know more clearly what part the other person played in your anger and which

part is more about you, write a letter to him or her. You may send this letter, or you might end up just burning

it. This is to help you clarify what exactly you’d like that person to know, understand, or change.

RESPOND WITHOUT ANGER

6. Now that you’re clear about the role you played in your anger, initiate a verbal conversation about what

bothered you. You could also send the letter you wrote, but it will be easier to clarify parts the other person

doesn’t understand if you’re having a direct back-and-forth exchange.

7. Use “I feel” language. So instead of saying, “You didn’t show up so you obviously don’t care about me,” say,

“When you forget about the things that are important to me, I feel hurt.” In this way, you’re not assuming the

other person meant to make you feel bad—you’re just explaining how it makes you feel so they can understand

how their actions impact you.

8. Focus on creating a solution. If your goal is to get the other person to admit that they’re wrong, you’ll probably

end up in a power struggle. Focus instead on what you’d like to change in the future—for example, you’d

appreciate it if she would come straight to you next time instead of complaining about you behind your back.

You can help facilitate this by owning some responsibility—that you will listen if he comes to you instead of

getting emotional.

LEARN FROM YOUR ANGER

9. Learn what you value. This situation taught you something useful about what you value in the people you

choose to be friends with—maybe directness, humility, or loyalty. This will help you decide which people you

might want to spend more or less time with going forward.

10. Learn how to communicate clearly. This experience was an exercise in expressing yourself in the best way

to be heard and understood. There will definitely be more situations like this in the future, so this is good practice

for misunderstandings and struggles to come.

11. Learn how you can improve your response to anger going forward. Maybe you reacted too quickly, so now

you’ve learned to put more space between your feelings and your response. Maybe you got defensive, and the

other person shut down, so you’ve learned to be less accusatory in the future.

Anexo II.

Text used in the 2nd instruction session (full version)

TEXT

(Adapted from the blog article “What are the ten positive actions?”)

One of my favorite books to come out of the "positive psychology" movement is called Positivity, by Dr.

Barbara Fredrickson. Truly a genius and pioneer in the field, Dr. Fredrickson has been studying positive

emotions in her lab long before it was vogue. Her data reveals that negative emotions, like fear, can close down

our ability to function, while positive emotions open us up to possibility, and an increased ability to move

forward.

She prefers the term "Positivity" to "Happiness" and stresses the importance and possibility of not just being

happy; but flourishing. Isn't that a lovely word? Wouldn't we all love to flourish?

Check out Dr. Fredrickson herself describing "Positivity" and why it is so important at this moment in history.

As she says at the end of the clip: "Investing in things that bring us more positive emotions is an investment in

our future. Choosing Hope over Fear." Dr. Fredrickson's came up with a top 10 list of positive emotions, in

order of most frequent to least. Allow yourself an opportunity to scroll through the list and ask yourself, "When

did I last fully experience this emotion?" The answers may surprise you.

1. Joy happens in an instant -- a perfect moment captured when all is just exactly as it should be. Think

of a wonderful holiday morning with the family, an unexpected present that delights you, or seeing the first

smile on your infant's face. What brings you Joy?

2. Gratitude is a moment of realizing someone has gone out of their way for you, or simply feeling

overwhelmed with your heart opening, after being moved in some way. With gratitude comes a desire to give

in return or 'pay it forward' in some way. When did you last experienced deep Gratitude?

3. Serenity is like a mellow, relaxed, or sustained version of Joy. Serenity is a peacefulness that comes on

a cloudless day, when you realize there's nothing you have to do. Serenity is indulging in a favorite luxury, and

being mindful enough to take it in. Serenity is the moment on vacation when you finally let go. Has Serenity

crossed your door lately?

4. Interest is a heightened state that calls your attention to something new that inspires fascination, and

curiosity. Like a shiny new toy to capture your imagination, interest is alive and invigorating. Interest wakes

you up, and leaves you wanting more. What Interests you these days?

5. Hope. Dr. Fredrickson describes it best: "Unlike other emotions that arise out of comfort and safety,

hope springs out of dire circumstances, as a beacon of light. Deep within the core of hope is the belief that

things can change, turn out better. Possibilities exist. Hope sustains you and motivates you to turn things

around." The inauguration of President Obama brought me Hope. What brings you Hope?

6. Pride. Ever done something really well that took a little time and effort? Maybe you reached a goal

you never thought was attainable? Then pat yourself on the back with unadulterated Pride. Stand back, take that

deep breath and let it in -- you earned it. What have you done that made your proud?

7. Amusement. Think of amusement as those delightful surprises that make you laugh. It's those

unexpected moments that interrupt your focus and crack you up. It's a great feeling to have amusement sparkle

out of the doldrums and instantly change your perspective. Have you had any amusement in your life recently?

8. Inspiration is a moment that touches your heart and nearly takes your breath away -- or takes in your

breath, as the word literally translates. Inspiration whispers between the strands of your hair, as you watch a

perfect sunset, witness academic or athletic excellence, or observe unexpected triumphs over adversity. What

brings Inspiration in your life?

9. Awe happens when you come across goodness on a grand scale, and you feel overwhelmed by

greatness. Awe is triggered when we are faced with the vastness of Nature, or the cosmos. Gazing at the Milky

Way and counting the stars or standing at the top of the Grand Canyon triggers awe. Have you had a moment

of awe lately?

10. Love. The #1 most frequent positive emotion is here at the bottom. Love encompasses all of the above:

joy, gratitude, serenity, interest, hope, pride, amusement, inspiration and even awe. When we experience love,

our bodies are flooded with the "feel good" hormones that reduce stress and even lengthen our lives.