Unique Words Research Articles

Feelings in words: Emotion word use and cardiovascular reactivity in marital interactions.

Putting feelings into words is often thought to be beneficial. Few studies, however, have examined associations between natural emotion word use and cardiovascular reactivity. This laboratory-based study examined emotion word use (i.e., from computerized text analysis) and cardiovascular reactivity (i.e., interbeat interval changes from baseline) across two interaction contexts (i.e., conflict and positive conversations) in 49 mixed-sex married couples (age: M = 43.11, SD = 9.20) from diverse socioeconomic backgrounds. We focused on both frequency (i.e., relative proportion of emotion words) and diversity (i.e., relative proportion of unique emotion words) of emotion words. Data were collected between 2015 and 2017 and analyzed treating both partners and conversations as repeated measures, resulting in 196 observations overall (four per dyad). Findings showed that (a) when spouses used more negative emotion words (especially anger), they showed higher cardiovascular reactivity. This finding was robust when controlling for covariates; generalized across gender, interaction contexts, and socioeconomic status. Moreover, (b) when spouses used a more diverse negative emotion vocabulary, they showed higher cardiovascular reactivity, but this was not robust when controlling for negative emotion word frequency. Associations between (c) positive emotion word use and cardiovascular reactivity were not statistically significant. Verbalizing negative emotions thus seems to go along with higher cardiovascular reactivity, at least in the short term. Replication is needed across other relationship types, genders, and sexual orientations. These findings highlight emotion word use as an indicator of cardiovascular reactivity, which has implications for the identification of potential health risks that emerge during marital interactions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Current Effect of Mother-Child Memory Talk on Emotion Regulation, Self-Esteem, and Memory

Children talking to their parents more frequently about past experiences tend to have higher emotion regulation skills and self-esteem in their future lives, which may lead to higher volume and richer emotional content in future memories. Previous research also indicated that self-esteem has a strong bond with emotion regulation skills. This study’s aim is to measure the mediator roles of emotion regulation difficulty and self-esteem on the relationship between childhood maternal reminiscing frequency and the volume and emotional content of the current memory experienced with the mother. Additionally, mediator roles of self-esteem in the connection between past maternal reminiscing and emotion regulation difficulty, and emotion regulation difficulty in the relationship between self-esteem and the current memory variables (i.e., total words, total emotion and unique emotion words) are examined. Participants (N=124, the age range was 22-39) filled out Rosenberg Self-Esteem, Emotion Regulation Difficulty, and Family Reminiscence Scales and wrote down one negatively-charged recent memory about their mothers. Path analysis revealed significant positive associations between past maternal reminiscing and self-esteem, emotion regulation difficulty, and total and unique emotion words in recent memory, and negative association between self-esteem and emotion regulation difficulty, supporting half of the mediation hypotheses. Results supported the notion that the frequency of parent-child reminiscing conversations in childhood is a parameter of child development since it can show its prospective effect via improving self-esteem and emotional functioning. Gender differences were not evident for current memory variables but more research on this issue is needed to reach more precise conclusions.

Influence of pre-processing methods on the automatic priority prediction of native-language end-users’ maintenance requests through machine learning methods

Feedback and requests by occupants are relevant sources of data to improve building management, and building maintenance. Indeed, most predictable faults can be directly identified by occupants and communicated to facility managers through communications written in the end-users’ native language. In this sense, natural language processing methods can support the request identification and attribution process if they are robust enough to extract useful information from these unstructured textual sources. Machine learning (ML) can support assessing and managing these data, especially in the case of many simultaneous communications. In this field, the application of pre-processing and ML methods to English-written databases has been widely provided, while efforts in other native languages are still limited, impacting the real applicability. Moreover, the performance of combinations of methods for pre-processing, ML and classification classes attribution, has been limitedly investigated while comparing different languages. To fill this gap, this work hence explores the performance of automatic priority assignment of maintenance end-users’ requests depending on the combined influence of: (a) different natural language pre-processing methods, (b) several supervised ML algorithms, (c) two priority classification rules (2-class versus 4-class), (d) the database language (i.e. the original database written in Italian, the native end-users’ language; a translated database version in English, as standard reference). Analyses are performed on a database of about 12000 maintenance requests written in Italian concerning a stock of 23 buildings open to the public. A random sample of the sentences is supervised and labelled by 20 expert annotators following the best-worst method to attribute a priority score. Labelled sentences are then pre-processed using four different approaches to progressively reduce the number of unique words (potential predictors). Five different consolidated ML methods are applied, and comparisons involve accuracy, precision, recall and F1-score for each combination of pre-processing action, ML method and the number of priority classes. Results show that, within each ML algorithm, different pre-processing methods limitedly impact the final accuracy and average F1-score. In both Italian and English conditions, the best performance is obtained by NN, LR, SVM methods, while NB generally fails, and by considering the 2-class priority classification scale. In this sense, results confirm that facility managers can be effectively supported by ML methods for preliminary priority assessments in building maintenance processes, even when the requests database is written in end-users’ native language.

NSP-SCD: A corpus construction protocol for child-directed print in understudied languages

Child-directed print corpora enable systematic psycholinguistic investigations, but this research infrastructure is not available in many understudied languages. Moreover, researchers of understudied languages are dependent on manual tagging because precise automatized parsers are not yet available. One plausible way forward is to limit the intensive work to a small-sized corpus. However, with little systematic enquiry about approaches to corpus construction, it is unclear how robust a small corpus can be made. The current study examines the potential of a non-sequential sampling protocol for small corpus development (NSP-SCD) through a cross-corpora and within-corpus analysis. A corpus comprising 17,584 words was developed by applying the protocol to a larger corpus of 150,595 words from children’s books for 3-to-10-year-olds. While the larger corpus will by definition have more instances of unique words and unique orthographic units, still, the selectively sampled small corpus approximated the larger corpus for lexical and orthographic diversity and was equivalent for orthographic representation and word length. Psycholinguistic complexity increased by book level and varied by parts of speech. Finally, in a robustness check of lexical diversity, the non-sequentially sampled small corpus was more efficient compared to a same-sized corpus constructed by simply using all sentences from a few books (402 books vs. seven books). If a small corpus must be used then non-sequential sampling from books stratified by book level makes the corpus statistics better approximate what is found in larger corpora. Overall, the protocol shows promise as a tool to advance the science of child language acquisition in understudied languages.

Speech patterns during memory recall relates to early tau burden across adulthood.

Early cognitive decline may manifest in subtle differences in speech. We examined 238 cognitively unimpaired adults from the Framingham Heart Study (32-75 years) who completed amyloid and tau PET imaging. Speech patterns during delayed recall of a story memory task were quantified via five speech markers, and their associations with global amyloid status and regional tau signal were examined. Total utterance time, number of between-utterance pauses, speech rate, and percentage of unique words significantly correlated with delayed recall score although the shared variance was low (2%-15%). Delayed recall score was not significantly different between β-amyoid-positive (Aβ+) and -negative (Aβ-) groups and was not associated with regional tau signal. However, longer and more between-utterance pauses, and slower speech rate were associated with increased tau signal across medial temporal and early neocortical regions. Subtle speech changes during memory recall may reflect cognitive impairment associated with early Alzheimer's disease pathology. Speech during delayed memory recall relates to tau PET signal across adulthood. Delayed memory recall score was not associated with tau PET signal. Speech shows greater sensitivity to detecting subtle cognitive changes associated with early tau accumulation. Our cohort spans adulthood, while most PET imaging studies focus on older adults.

Ibn-Ginni: An Improved Morphological Analyzer for Arabic

Arabic is a morphologically rich language, which means that the Arabic language has a complicated system of word formation and structure. The affixes in the Arabic language (i.e., prefixes and suffixes) can be added to root words to generate different meanings and grammatical functions. These affixes can indicate aspects such as tense, gender, number, case, person, and more. In addition, the meaning and function of words can be modified in Arabic using an internal structure known as morphological patterns. Computational morphological analyzers of Arabic are vital to developing Arabic language processing toolkits. In this article, we introduce a new morphological analyzer (Ibn-Ginni) that inherits the speed and quality of the Buckwalter Arabic Morphological Analyzer (BAMA). The BAMA has poor coverage of the classical Arabic language. Hence, the coverage of classical Arabic is improved by using the Alkhalil analyzer. Although it is slow, it was used to generate a huge number of solutions for 3 million unique Arabic words collected from different resources. These word form-based solutions were converted to stem-based solutions, refined manually, and added to the database of BAMA, resulting in substantial improvements in the quality of the analysis. Hence, Ibn-Ginni is a hybrid system between BAMA and Alkhalil analyzers and may be considered an efficient large-scale analyzer. The Ibn-Ginni analyzer analyzed 0.6 million more words than the BAMA analyzer. Therefore, our analyzer significantly improves the coverage of the Arabic language. Besides, the Ibn-Ginni analyzer is high speed at providing solutions, the average time to analyze a word is 0.3 ms. Using a corpus designed for benchmarking Arabic morphological analyzers, our analyzer was able to find all solutions for 72.72% of the words. Moreover, the analyzer did not provide all possible morphological solutions for 24.24% of the words. The analyzer and its morphological database are publicly available on GitHub. 1

Generating Contextual Variables From Web-Based Data for Health Research: Tutorial on Web Scraping, Text Mining, and Spatial Overlay Analysis.

Contextual variables that capture the characteristics of delimited geographic or jurisdictional areas are vital for health and social research. However, obtaining data sets with contextual-level data can be challenging in the absence of monitoring systems or public census data. We describe and implement an 8-step method that combines web scraping, text mining, and spatial overlay analysis (WeTMS) to transform extensive text data from government websites into analyzable data sets containing contextual data for jurisdictional areas. This tutorial describes the method and provides resources for its application by health and social researchers. We used this method to create data sets of health assets aimed at enhancing older adults' social connections (eg, activities and resources such as walking groups and senior clubs) across the 374 health jurisdictions in Catalonia from 2015 to 2022. These assets are registered on a web-based government platform by local stakeholders from various health and nonhealth organizations as part of a national public health program. Steps 1 to 3 involved defining the variables of interest, identifying data sources, and using Python to extract information from 50,000 websites linked to the platform. Steps 4 to 6 comprised preprocessing the scraped text, defining new variables to classify health assets based on social connection constructs, analyzing word frequencies in titles and descriptions of the assets, creating topic-specific dictionaries, implementing a rule-based classifier in R, and verifying the results. Steps 7 and 8 integrate the spatial overlay analysis to determine the geographic location of each asset. We conducted a descriptive analysis of the data sets to report the characteristics of the assets identified and the patterns of asset registrations across areas. We identified and extracted data from 17,305 websites describing health assets. The titles and descriptions of the activities and resources contained 12,560 and 7301 unique words, respectively. After applying our classifier and spatial analysis algorithm, we generated 2 data sets containing 9546 health assets (5022 activities and 4524 resources) with the potential to enhance social connections among older adults. Stakeholders from 318 health jurisdictions registered identified assets on the platform between July 2015 and December 2022. The agreement rate between the classification algorithm and verified data sets ranged from 62.02% to 99.47% across variables. Leisure and skill development activities were the most prevalent (1844/5022, 36.72%). Leisure and cultural associations, such as social clubs for older adults, were the most common resources (878/4524, 19.41%). Health asset registration varied across areas, ranging between 0 and 263 activities and 0 and 265 resources. The sequential use of WeTMS offers a robust method for generating data sets containing contextual-level variables from internet text data. This study can guide health and social researchers in efficiently generating ready-to-analyze data sets containing contextual variables.

FASSILA: A Corpus for Algerian Dialect Fake News Detection and Sentiment Analysis

In the context of low-resource languages, the Algerian dialect (AD) faces challenges due to the absence of annotated corpora, hindering its effective processing, notably in Machine Learning (ML) applications reliant on corpora for training and assessment. This study outlines the development process of a specialized corpus for Fake News (FN) detection and sentiment analysis (SA) in AD called FASSILA. This corpus comprises 10,087 sentences, encompassing over 19,497 unique words in AD, addresses the language's significant lack of linguistic resources, and covers seven distinct domains. We propose an FN detection and SA annotation scheme detailing the data collection, cleaning, and labeling. The remarkable Inter-Annotator Agreement indicates that the annotation scheme produces high-quality and consistent annotations. Subsequent classification experiments using BERT-based and ML models are presented, demonstrating promising results and highlighting avenues for further research. The dataset is currently freely available to facilitate future advancements in the field.

Finding biotic anomalies described in specimen label text is a challenge that artificial intelligence can address

Biodiversity specimen collectors are on the front lines of observing biotic anomalies, some of which herald early stages of significant changes (e.g., the arrival of a new disease; Pearson and Mast 2019). Online data sharing has opened new possibilities for the discovery of anomaly descriptions on collectors’ labels, but it remains a challenge to find these needles in the haystack of many millions of specimen records available at aggregators like iDigBio and Global Biodiversity Information Facility. In a recent community survey, over 200 collectors identified 170 unique words and phrases (e.g., atypical) that they would use to describe six types of anomaly (Pearson and Mast 2019). Left unanswered was the relative efficiency with which anomaly descriptions can be found using the simple presence of these words. Here, we address that question with a focus on one type of anomaly (phenological; related to the timing of life historyevents) and ask a second question: can we further improve the efficiency of anomaly description discovery by engaging artificial intelligence (AI)?

The Role of Task Types and Reading Proficiency on Young English as a Foreign Language Learners' Writing Performances

AbstractThis study examined the role of writing task types and L2 reading proficiency on young English as a foreign language (EFL) learners' writing performances. The participants were 185 students (Grades 7 and 8) from Denmark, Finland, and the Netherlands. The students responded to a descriptive and an email writing tasks that assessed their writing ability. The written responses were scored holistically and analyzed for aspects of lexical sophistication, syntactic complexity, writing fluency, and idea development. The students were grouped into strong or emergent readers based on their performances on an English reading comprehension test. Results showed that the students' writing performances differed across task types and were associated with their reading proficiency. The students produced more unique and concrete words and more complex syntactic structures and wrote longer essays in the descriptive writing task. Strong readers outperformed the emergent readers in both tasks and in writing fluency and idea development. Findings corroborate previous research on the connection between reading and writing skills and highlight the importance of strengthening young EFL learners' reading skills to support their writing development.

A PRELIMINARY STUDY OF SENTIMENT ANALYSIS ON COVID-19 NEWS: LESSON LEARNED FROM DATA ACQUISITION, PRE-PROCESSING, AND DESCRIPTIVE ANALYTICS

Sentiment analysis is a method used to analyze opinions and feelings. The goal of sentiment analysis is to determine whether a document contains a positive or negative emotion. Along with the spread of Covid-19 cases, news related to Covid-19 has often become a trending topic in the mass media. Conducting sentiment analysis using all news becomes more challenging because it might take time and cost. Therefore, the sampling method is needed to obtain representative news for the analysis. Web scraping was employed to obtain the news article about Covid-19 in Indonesia. In order to select the representative news, two-step sampling was employed by using stratified and systematic random sampling. According to the topic modelling results using lambda 0.6, news articles are grouped into three topics: updating Covid-19 cases, vaccination, and government policy. In addition, based on the number of positive and negative words, news articles are grouped into news dominated by positive words, news dominated by negative words, and news with the same number of positive and negative words. Methods for representing text in numerical form have been developed. Some of them use tf-idf weighting and word embedding. It does not pay attention to word order or meaning, only based on the frequency of words both locally and globally. Furthermore, this method will form a vector size as large as the number of unique words in the document, so it is less effective when many documents are used. Meanwhile, the vector size generated from the word2vec method is not as much as the number of unique words in the corpus. In addition, word2vec considers the context of the words in the corpus.

An Exploration of the Nature of Vocabulary Change Following Language and Literacy Together Intervention.

This exploratory study evaluates how bilingual first graders' vocabulary use in narrative changed after a Spanish-language intervention that focuses on connection between language and literacy. Ten Spanish-English bilingual first graders produced three English and three Spanish narratives based on the Test of Narrative Language protocol pre- and postintervention. All samples were transcribed. Based on comprehensive word inventories, all words produced by the children were identified as being core words; Tiers 1, 2, and 3 words; and/or cognates in both languages. We also coded all utterances for use of internal state terms (ISTs). We evaluated total and unique production of these kinds of words. Postintervention children increased their total number of cognates and ISTs across languages. They also increased the unique core words, Tier 1 words, and cognates postintervention. They also used more of each of these kinds of words in Spanish-the language of intervention-than in English. Children make qualitative changes to their vocabulary postintervention. There is greater change in the use of core words and Tier 1 words in Spanish. These increases in productivity and variability were indicated by the number of unique words added to the children's repertoires in ways that supported narrative production.

Automatic classification of underlying AD vs. FTLD pathology in people with clinical Frontotemporal Dementia using digital speech markers

AbstractBackgroundRecent studies show promising results using speech features to automatically distinguish patients with amnestic Alzheimer’s disease (AD) from healthy controls (HC). However these studies exclude atypical presentations of AD, which present with linguistic and/or behavioral impairments that clinically overlap with frontotemporal dementia (FTD). A clinically meaningful challenge is distinguishing underlying AD pathology from frontotemporal lobar degeneration (FTLD) pathology in people presenting with an FTD phenotype. We used digital speech features to automatically classify patients with clinical FTD with as to underlying AD or FTLD pathology.MethodWe extracted 90 lexical and 9 acoustic features from one‐minute picture descriptions produced by 179 participants using our previously validated automated pipelines (Table 1). All patients presented clinically with FTD and had AD or FTLD pathology based on either autopsy or CSF (AD: CSF pTau/Aβ42³0.1 and tTau/Aβ42³0.34). Extracted features included part‐of‐speech, unique words per 100 words, several lexical characteristics, and acoustic/durational measures. Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP) classifiers were trained for two tasks: 1) AD vs. FTLD pathology and 2) all patients vs. HC. Five feature sets were tested: Set 1 (baseline): demographics+MMSE, Set 2: speech only, Set 3: speech+demographics, Set 4: speech+MMSE, Set 5: speech+demographics+MMSE. Features were selected using the elastic net regression with 10‐fold validations; the best performance models were reported after hyperparameter tuning.ResultDistinguishing AD from FTLD, the MLP model trained with speech had the highest AUC (0.88; accuracy = 83%) (Table 2). The RF model trained with speech and MMSE showed similar performance (accuracy = 84%, AUC = 0.85). Verb counts had the highest feature importance (FI) values in the MLP model trained with speech, followed by noun length. Distinguishing all patients from HC, the MLP model trained with speech only had the highest accuracy (93%; AUC = 0.93) (Table 3). The MLP model trained with speech and MMSE showed a comparable result (accuracy = 92%, AUC = 0.94). Unique adjective counts had the highest FI in the MLP model trained with speech, followed by word length.ConclusionSpeech models outperformed baseline models and showed comparable results with speech+MMSE. Features extracted from brief speech samples are valuable in automatically distinguishing AD from FTLD pathology, and FTD from HC.

Regional amyloid‐β is linked to disruptions in connected speech in amnestic mild cognitive impairment

AbstractBackgroundSpeech changes in amnesic mild cognitive impairment (aMCI) are potential markers for the early detection of Alzheimer disease (AD). However, few studies investigate the relationship between speech and amyloid‐β burden. The focus of this study was to examine the relationship between the linguistic features and the distribution of amyloid‐β load in aMCI.MethodThis study recruited 25 aMCI patients (52% female, aged 70‐85 years) and 25 healthy elders (44% female, aged 68‐80 years) from the memory clinic of Cardinal Tian Hospital in Taiwan. All participants received [18F] florbetaben PET and standardized cognitive tests. Amyloid‐β load using a standardized uptake value ratio (SUVr) was estimated from 5 cortical regions (parietal, frontal, cingulate, temporal, and occipital). Spontaneous speech was recorded using the picture description task (three Taiwanese culture‐related pictures) and transcribed verbatim. Linguistic features were extracted, including lexical richness (total words, unique words, type‐token‐ratio, lexical density, and frequency), syntactic complexity (mean length of utterance/sentence, ratio of verbs/pronouns/passive sentence), and speech fluency (ratio of long pauses/ fillers).ResultEighteen subjects with high amyloid burden (SUVr > 1.21) progressed (defined as CDR‐SB change≧1) within 1 year. We performed linear regression analyses between linguistic features (dependent variables, separate models) and amyloid‐β load in brain regions. Analyses were adjusted for age and education. The data revealed that a high amyloid‐β burden in the frontal region was associated with fewer unique words, less lexical density, and an increased proportion of long pauses and fillers. Reduced passive sentence, short mean length of utterance and lower scores on episodic memory tests related to increased amyloid‐β burden in the parietal region.ConclusionLexical richness and speech fluency was associated with amyloid‐β burden in the frontal region, indicating that voluntary lexical retrieval may require the additional recruitment of frontal lobe. Less syntactic complexity related to increased amyloid‐β burden in the parietal region. In conclusion, regional amyloid‐β is linked to subtle spontaneous speech deficits in aMCI.

Examining Automatic Generated Language Features of Chinese Spontaneous Speech for Early Detection of Cognitive Decline

AbstractBackgroundConnected speech is sensitive on detecting cognitive decline in early Alzheimer disease. In this study, we apply artificial intelligence to develop an automatic assessment for analyzing speech data.Method79 samples of picture description speech data were collected. All participants were native Chinese speakers including 31 normal controls (59‐88 yrs) and 49 patients (amnestic mild cognitive impairment and early Alzheimer’s disease) (58‐88 yrs). We extracted 16 linguistic features from the manually transcribed text. These linguistic features were generated by well‐trained linguists or automatically processed by the machine. We also trained decision tree models with those two sets of linguistic features. Seventy percent of the data was used for training and the remainder was used for testing. We also applied a cross‐validation method to avoid overfitting.ResultTotal words, unique words, and content words generated from linguists or machines were highly correlated (about r = 0.99, p < .001). However, the correlations between verb ratio, mean length of sentences, and filler ratio were relatively low (about r = 0.75, p < .001). With manually corrected features, the model could achieve 91.67% accuracy. Among them, the pronoun ratio and MLU (mean length of utterance) appeared to be equally crucial for model prediction. By contrast, with automatically generated features, the model achieved 83.33% accuracy only. MLU and MLS (mean length of sentences) were essential features for prediction.ConclusionAutomatic assessment could also achieve a good accuracy rate compared to humans (83.33% v.s. 91.67%). Some linguistic features such as fillers or sentences were still highly dependent on the expert’s judgment. Interestingly, MLU was critical in the prediction model of automatic or experts.

Automated lexical analysis of story recall in healthy controls

AbstractBackgroundImpaired episodic memory is one of the earliest and most prominent symptoms of Alzheimer’s disease (AD). Examining how patients produce words and phrases in story recall tasks is useful for tracking and understanding progression of their disease. Traditional approaches rely on manual assessments of recalled stories which can be limited, time‐consuming, and not applicable in large‐scale studies. Here, we implement an automated, computational approach to measure verbal episodic memory in a digitally recorded story recall task performed by young healthy speakers using natural language processing models.MethodWe analyzed digitized speech samples of pre‐recorded immediate and delayed Craft Story recall tasks performed by healthy speakers (n = 67, mean age = 20.31 years, SD = 2.17, 45 (67%) females). All transcripts were transcribed by trained annotators and automatically scored for number of verbatim and paraphrase recall, total recall score ( = verbatim+paraphrase), and semantic distance from the original story (degree of semantic similarity between the original craft story and the recalled story based on the Word2vec module in python). We also calculated total word count and number of unique words, and rated all recalled words for word familiarity, concreteness, and semantic ambiguity based on published norms. Number of pauses and total durations of speech and silent pauses were also measured.ResultWe found larger semantic distance between successive words in the delayed recall task compared with immediate recall within speaker (p<.001). Delayed recall also elicited lower total recall scores than immediate recall (p = .03). Comparing the effect sizes of semantic distance vs. total recall scores, our model comparison showed a smaller prediction error for semantic distance (AIC = 95.45) than total recall score (AIC = 736.18). In addition, speakers in the delayed recall task performed more paraphrase recall, had higher total word count, number of unique words, more ambiguous words, and produced more speech (all p‐values<.001).ConclusionOur findings suggest that automated speech analysis can provide informative and cost‐effective measures from a recorded story recall task. The study provides an automated and quantitative way to better measure memory abilities by including semantic distance. Future work will test procedures in patients with AD or other neurodegenerative diseases.

Mothers used wider vocabulary and talked to their six-month-old infants more during shared book reading than when they played with toys.

We compared mothers reading books to six-month-old infants or playing with toys and measured whether the maternal language input influenced the children's spoken vocabulary at 18 months of age. This Taiwanese study recruited 46 dyads and video recorded them while the mothers read books to their infants and played with them with toys at 6 months of age. The mothers' lexical diversity, which is the ratio of different unique words to the total number of words, was measured. We then assessed the children's spoken vocabulary at 18 months. The mother used more diverse vocabulary and a higher number of words when they were reading books than playing with toys with their children (p = 0.001). Maternal lexical diversity at 6 months of age accounted for 14.4% of the unique variance in the number of different words used by the child at 18 months. We believe that this is a novel finding. Mothers used wider vocabulary and talked to their infants more during book reading than when they played with toys. Diverse maternal vocabulary at 6 months of age positively influenced the number of different words their children used at 18 months of age.

Natural emotion vocabularies and borderline personality disorder

BackgroundEmotion dysregulation is a characteristic central to borderline personality disorder (BPD). Valuably, verbal behaviour can provide a unique perspective for studying emotion dysregulation in BPD, with recent research suggesting that the varieties of emotion words one actively uses (i.e., active emotion vocabularies [EVs]) reflect habitual experience and potential dysregulation therein. Accordingly, the present research examined associations between BPD and active EVs across two studies. MethodsStudy 1 (N = 530) comprised a large non-clinical sample recruited from online forums, whereby BPD traits were measured via self-report. Study 2 (N = 64 couples) consisted of mixed-gender romantic couples in which the woman had a BPD diagnosis, as well as a control group of couples. In both studies, participants’ verbal behaviours were analysed to calculate their active EVs. ResultsResults from both studies revealed BPD to be associated with larger negative EV (i.e., using a broad variation of unique negative emotion words), which remained robust when controlling for general vocabulary size and negative affect word frequency in Study 2. The association between BPD and negative EV was insensitive to context. LimitationsLimitations of this research include: 1) the absence of a clinical control group; 2) typical constraints surrounding word-counting approaches; and 3) the cross-sectional design (causality cannot be inferred). ConclusionsOur findings contribute to BPD theory as well as the broader language and emotion literature. Importantly, these findings provide new insight into how individuals manifesting BPD attend to and represent their emotional experiences, which could be used to inform clinical practice.

Vocabulary Change in Process Writing: Effect of Text Structure Instruction

Abstract The purpose of this study, conducted as a follow-up study of Oshima (2020), was to examine whether Japanese EFL students’ use of vocabulary changed after being given lessons on explicit instruction on text structure and process writing. Two groups of college students—the beginner-level group writing a descriptive essay and the advanced-level group writing an argumentative essay—wrote an outline, the first draft (D1), the second draft (D2), and the final draft (FD), and I examined the differences in lexical richness between students’ D1 and FD with New Word Level Checker (Mizumoto, 2021). The results showed that both groups’ drafts had changed in the number of words used (tokens), the number of unique words used (types), and the number of lower frequency words used. This study’s finding also supports the importance of choosing an appropriate measurement to analyze students’ vocabulary levels. For Japanese students, 1K-word bands, which have been widely used in previous literature, seem too broad to capture their small vocabulary improvement.

TWITTER DATA SENTIMENT ANALYSIS FORSTOCK MARKET PREDICTION USING MACHINE LEARNING

: Recent outrageous posts on social media have taken the globe by storm and have led to diverse views and views of the general public. Social media plays a significant act for or against a government or a corporation that simply can’t decide the movement of market but to grasp the sentiment of twitter data that are posted on social media with good method could be a supreme necessity. It will analyse some twitter postings to grasp human semantic. In any tweet intended posting there are some downgraded keyword. At last, a data-set is ready that consists of unique words collected from twitter posts or comments and so the data-set is trained using Naive Bayes algorithm supported with applied mathematics to spot the sentiment given during a new call and comment . They are going to extract each word of the posting and so it'll be matched by virtue with the data-set words for dilution. Finally, it will be tested to their algorithm using numerous posts from twitter that can deliver the result with good accuracy.