In this paper, we analyze corporate e-mail messages as a medium to convey work tasks. Research indicates that categorization of e-mail could alleviate the common problem of information overload. ...Although e-mail clients provide possibilities of e-mail categorization, not many users spend effort on proper e-mail management. Since e-mail clients are often used for task management, we argue that intent- and task-based categorizations might be what is missing from current systems.
We propose a taxonomy of tasks that are expressed through e-mail messages. With this taxonomy, we manually annotated two e-mail datasets (Enron and Avocado), and evaluated the validity of the dimensions in the taxonomy. Furthermore, we investigated the potential for automatic e-mail classification in a machine learning experiment.
We found that approximately half of the corporate e-mail messages contain at least one task, mostly informational or procedural in nature. We show that automatic detection of the number of tasks in an e-mail message is possible with 71% accuracy. One important finding is that it is possible to use the e-mails from one company to train a classifier to classify e-mails from another company. Detecting how many tasks a message contains, whether a reply is expected, or what the spatial and time sensitivity of such a task is, can help in providing a more detailed priority estimation of the message for the recipient. Such a priority-based categorization can support knowledge workers in their battle against e-mail overload.
Deixis Analysis on Oxford University E-mail Messages Nur Azizah, Desi; Sabat, Yuliyanto; Musyarofah, Lailatul ...
Journal of English Language Teaching and Literature,
03/2024, Letnik:
5, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Deixis is essential to creating effective communication. This research aimed (i) to determine the number of each type of deixis contained within E-mails from Oxford University Publications Office and ...(ii) the meanings contained there in. This research used qualitative descriptive method. The basic theory used by researchers is the theory of Yule. Yule described deixis as divided into 3, namely person, spatial, and temporal.The data used in this study came from one of the sources who received an E-mail from Oxford University and interview data. The result of the research was there are 2 types of deixis in E-mail, namely person deixis and spatial deixis. In person deixis was found 29 data with 3 forms of pronouns, namely You, Your, We, Our, Us, Their, and It. In spatial deixis was found 3 data with 2 forms, namely below and this. From the interview data, the researchers found that the language used in international campuses generally easy to comprehend, particularly noting the helpfulness of deixis in grasping message meanings, albeit sometimes requiring repeated reading for full understanding, with messages from Oxford University being notably straightforward.
Objectives
To determine associations between use of three different modes of social contact (in person, telephone, written or e‐mail), contact with different types of people, and risk of depressive ...symptoms in a nationally representative, longitudinal sample of older adults.
Design
Population‐based observational cohort.
Setting
Urban and suburban communities throughout the contiguous United States.
Participants
Individuals aged 50 and older who participated in the Health and Retirement Survey between 2004 and 2010 (N = 11,065).
Measurements
Frequency of participant use of the three modes of social contact with children, other family members, and friends at baseline were used to predict depressive symptoms (measured using the eight‐item Center for Epidemiologic Studies Depression Scale) 2 years later using multivariable logistic regression models.
Results
Probability of having depressive symptoms steadily increased as frequency of in‐person—but not telephone or written or e‐mail contact—decreased. After controlling for demographic, clinical, and social variables, individuals with in‐person social contact every few months or less with children, other family, and friends had a significantly higher probability of clinically significant depressive symptoms 2 years later (11.5%) than those having in‐person contact once or twice per month (8.1%; P < .001) or once or twice per week (7.3%; P < .001). Older age, interpersonal conflict, and depression at baseline moderated some of the effects of social contact on depressive symptoms.
Conclusion
Frequency of in‐person social contact with friends and family independently predicts risk of subsequent depression in older adults. Clinicians should consider encouraging face‐to‐face social interactions as a preventive strategy for depression.
•This article showcase the application of deep learning concept.•The proposed gLove method used for feature extraction called as vector conversion.•The E-mail client prototype provides the data ...inputs to perform the computation.•The system model of learning ability is analysed for better prediction accuracy.•Softmax classifiers are used to provide automatic reply solution.
E-mail is considered the commonly used and efficient way of communication over the globe. In the corporate sectors, the number of E-mails received every day is considerably high and the timely response to every E-mail is essential. Several researchers believe that natural language processing (NLP) techniques by the use of deep learning (DL) architectures have played a considerable part to reduce manual work for repeated E-mail responses and intended to develop E-mail systems with intelligent response function. In this view, this paper designs an intelligent DL enabled optimal bidirectional long short term memory (Bi-LSTM) technique for an automated E-mail reply (OBiLSTM-AER) of E-mail Client Prototype. The goal of the proposed model is to provide an automated E-mail reply solution for persons as well as corporates which receive massive identical E-mails daily. The presented model employs Glove and OBiLSTM model for feature extraction of receiving and response E-mails respectively. Finally, softmax classifier is applied to allocate the class labels. For improving the performance of the BiLSTM model, the hyperparameter tuning process takes place using an oppositional glowworm swarm optimization (OGSO) algorithm. An extensive set of simulations were performed to highlight the betterment of the proposed method and the results are examined interms of distinct measures.
As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art ...performance. The absence of trustworthy human supervision over the data collection process exposes organizations to security vulnerabilities; training data can be manipulated to control and degrade the downstream behaviors of learned models. The goal of this work is to systematically categorize and discuss a wide range of dataset vulnerabilities and exploits, approaches for defending against these threats, and an array of open problems in this space.
The paper examines the influence of opt-in e-mail marketing on consumer behaviour. The study attempts to extend the Stimuli-Organism-Response (S-O-R) theory that has been broadly explored in consumer ...research. Following a critical review of the literature organisation approach, a hypothetical model has been proposed for this study, based on identified factors, such as, informational value, entertainment-based message content, layout, visual appeal, attitude toward e-mail advertising and intention towards the sender in the context of opt-in email marketing. Data were collected in South Africa through an online survey of 436 opt-in e-mail marketing subscribers. Structural equation modelling (SEM) was employed to measure the proposed hypotheses of the study. The research results suggest that even during a pandemic, e-mail marketers could employ certain features in promotional and informational e-mail marketing communication, particularly informational value, entertainment-based message content, layout, visual appeal, as a means to design their e-mail marketing messages and plan e-mail advertising campaigns. The findings of the study are intended to advance the e-mail marketing knowledge base to help marketers during a pandemic, such as COVID-19. The paper provides marketers with relevant insights on how to effectively engage with e-mail subscribers.
This paper empirically investigates using the e‐mail channel to target customers with a delayed incentive promotion—specifically, gift card promotion—and derives data‐driven e‐mail targeting ...policies. Gift card promotions are popular across retailers because they incentivize customers to spend more than a fixed expenditure level on regularly priced products by rewarding customers with a gift card to be redeemed against a future purchase. The e‐mail channel provides retailers with new sources of customer‐level data, which enables better prediction of customers' responsiveness to e‐mails (e.g., clicking) and the sales promotion that comes with it (e.g., participation in the promotion). We formulate the retailer's promotion e‐mail targeting problem by maximizing two objectives—the promotion's profitability (i.e., profit‐based targeting) and e‐mail click‐through rate (i.e., CTR‐based targeting). We also take into account the retailer's promotion budget and exclusivity concerns in targeting e‐mails. We use a comprehensive dataset from a Fortune 500 luxury fashion retailer's online channel and utilize both parametric and non‐parametric methods to predict customers' response to promotion e‐mails. Our data‐driven targeting policies improve the promotion's profitability by 5.57% and e‐mail CTR by 472.57%, on average, compared to our partner retailer's current e‐mail policy. We also find that the CTR‐based targeting policy lowers the promotion profitability by, on average, 9.09% compared to the profit‐based one. However, the CTR‐based policy recuperates the short‐term losses in the long‐term and increases the long‐term profitability by 3.94%, on average, compared to the profit‐based targeting policy.
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