Essays about: "email clustering"

Found 5 essays containing the words email clustering.

  1. 1. Evaluation of the performance of machine learning techniques for email classification

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Isabella Tapper; [2022]
    Keywords : Natural Language Processing; Text Representations; Email Classification; Text Classification; Behandling Av Naturliga Språk; Text Representation; epost-klassificering; Textklassificering;

    Abstract : Manual categorization of a mail inbox can often become time-consuming. Therefore many attempts have been made to use machine learning for this task. One essential Natural Language Processing (NLP) task is text classification, which is a big challenge since an NLP engine is not a native speaker of any human language. READ MORE

  3. 2. Predictive maintenance using NLP and clustering support messages

    University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknik

    Author : Ugur Yilmaz; [2022]
    Keywords : Predictive maintenance; support messages; NLP; unsupervised clustering; intent recognition; LDA; UMAP; HDBSCAN; BERT; Swedish BERT KB-BERT ; Billogram;

    Abstract : Communication with customers is a major part of customer experience as well as a great source of data mining. More businesses are engaging with consumers via text messages. Before 2020, 39% of businesses already use some form of text messaging to communicate with their consumers. Many more were expected to adopt the technology after 2020[1]. READ MORE

  4. 3. Extracting Customer Sentiments from Email Support Tickets : A case for email support ticket prioritisation

    University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Author : Albert Fiati-Kumasenu; [2019]
    Keywords : Machine Learning; Natural Language Processing; Sentiment Analysis; Cluster Ensemble; VADER; Customer support;

    Abstract : Background Daily, companies generate enormous amounts of customer support tickets which are grouped and placed in specialised queues, based on some characteristics, from where they are resolved by the customer support personnel (CSP) on a first-in-first-out basis. Given that these tickets require different levels of urgency, a logical next step to improving the effectiveness of the CSPs is to prioritise the tickets based on business policies. READ MORE

  5. 4. Analysis of Organizational Structure of a Company by Evaluation of Email Communications of Employees : A Case Study

    University essay from Blekinge Tekniska Högskola/Institutionen för datalogi och datorsystemteknik

    Author : Sai Mohan Harsha Kota; [2018]
    Keywords : Cluster Validation Measures; Clustering; Data Analysis; Organizational Structure; Human Capital Management; Email;

    Abstract : There are many aspects that govern the performance of an organization. One of the most important thing is their organizational structure. Having a well-planned organizational structure facilitates good internal communication among the employees, which in turn contributes to the success of the organization. READ MORE

  6. 5. Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing

    University essay from KTH/Skolan för datavetenskap och kommunikation (CSC)

    Author : Christoffer Pettersson; [2016]
    Keywords : Machine learning; Unsupervised; Natural language processing; nlp; clustering; centroid based; k-means; text clustering; limited data; email clustering; lsa; svd; tf-idf; dimensionality reduction; the gap statistic; Lloyd s algorithm; vectorization; feature extraction;

    Abstract : The goal of this project is to investigate any correlation between marketing emails and their receivers using machine learning and only a limited amount of initial data. The data consists of roughly 1200 emails and 98.000 receivers of these. Initially, the emails are grouped together based on their content using text clustering. READ MORE