Text analysis: An introduction to methods and techniques
1 like2,819 views
This presentation is based on the material from Aylien.com, the natural-language processing platform. See www.aylien.com
1 of 10
Downloaded 61 times
Recommended
Textmining Introduction
Textmining IntroductionDatamining Tools
油
Text mining refers to extracting knowledge from unstructured text data. It is needed because most biological knowledge exists in unstructured research papers, making it difficult for scientists to manually analyze large amounts of text. Challenges include dealing with noisy, unstructured data and complex relationships between concepts. The text mining process involves preprocessing text through steps like tokenization, feature selection, and parsing to extract meaningful features before analysis can be done through classification, clustering, or other techniques. Potential applications are wide-ranging across domains like customer profiling, trend analysis, and web search.Machine Translation
Machine TranslationSkilrock Technologies
油
Machine Translation using Knowledge Based System
Need of Machine Translation
Issues of Machine Translation
Rule Based Machine TranslationText Analysis with Machine Learning
Text Analysis with Machine LearningTuri, Inc.
油
The document discusses text analysis with machine learning. It begins with introductions and then covers applications of text analysis like product reviews and social media. The bulk of the document discusses fundamentals of text processing like tokenization and feature engineering. It also discusses machine learning toolkits and task-oriented tools like sentiment analysis. Advanced topics like topic models and word embeddings are briefly introduced. The presentation aims to provide an overview of text analysis and point to further resources.Spell checker using Natural language processing 
Spell checker using Natural language processing Sandeep Wakchaure
油
This document presents an overview of spell checking techniques in natural language processing. It discusses how spell checkers work by scanning text, comparing words to a dictionary, and using language-dependent algorithms. Two categories of spelling errors are described: real-word errors involving correctly spelled words and non-word errors containing no dictionary words. Techniques for error detection include dictionary lookup and n-gram comparisons using the Jaccard coefficient. The Levenshtein distance and Jaccard coefficient algorithms are then explained and shown to provide suggestions by calculating the edit distance between source and target words. The presentation concludes that these algorithms filter dictionary words and provide accurate suggestions to correct spelling mistakes in text.Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machin...
Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machin...Simplilearn
油
This presentation about Random Forest in R will help you understand what is Random Forest, how does a Random Forest work, applications of Random Forest, important terms to know and you will also see a use case implementation where we predict the quality of wine using a given dataset. Random Forest is an ensemble Machine Learning algorithm. Ensemble methods use multiple learning models to gain better predictive results. It operates building multiple decision trees. To classify a new object based on its attributes, each tree is classified, and the tree votes for that class. The forest chooses the classification having the most votes (over all the trees in the forest). Now let us get started and understand what is Random Forest and how does it work.
Below topics are explained in this Random Forest in R presentation :
1. What is Random Forest?
2. How does a Random Forest work?
3. Applications of Random Forest
4. Use case: Predicting the quality of the wine
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbour recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
Natural Language Processing
Natural Language ProcessingToine Bogers
油
This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification.
(Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)Text Analytics Presentation
Text Analytics PresentationSkylar Ritchie
油
This document provides an introduction to text analytics using IBM SPSS Modeler. It defines key terms related to text analytics and outlines the main steps in the text analytics process: extraction, categorization, and visualization. It then provides a tutorial on using IBM SPSS Modeler to perform text analytics, including sourcing text, extracting concepts and relationships, categorizing records, and visualizing results. Templates and resources are described that can be used to start an interactive workbench session in Modeler for exploring text analytics.Introduction to Text Analysis
Introduction to Text AnalysisLauren Klein
油
This document introduces text analysis and provides an overview of how to perform text analysis. It defines text analysis as using computational tools to quickly search large texts and conduct complex searches. The document explains that text analysis can help test intuitions during research by encouraging reflection on questions asked of texts. Several examples of text analysis tools and projects are provided, such as Voyant Tools, Google Ngram Viewer, and projects analyzing literary texts. A variety of open-source text analysis tools and programming languages are introduced.Learn how to write a review of literature.
Learn how to write a review of literature.Rakib Hossain
油
This document provides guidance on writing a literature review by outlining its typical sections and content. It explains that a literature review analyzes and summarizes prior research on a topic to provide context. The introduction should define the topic and identify trends, conflicts or gaps in previous work. The body groups and summarizes studies according to common factors and provides analysis. The conclusion summarizes major contributions, evaluates the current state of knowledge, and discusses relationships to broader areas of study.Sentiment analysis
Sentiment analysisChaand Chopra
油
Sentiment Analysis and Opinion Mining
Bing Liu
The University of Illinois at Chicago
Chapter 1 2 and 3How to write a Research Paper
How to write a Research PaperThesis Scientist Private Limited
油
How to write a research paper: How to write a research paper?
The research paper can be considered as the living thing which grows and changes as the student explores, interprets and evaluates sources related to a specific topic. Steps to write a research paper:
Choose your topic.
Begin your search.
Making an outline.
Write your paper.
Academic Writing - The Writing Process
Academic Writing - The Writing ProcessContentwritings Ltd
油
The document outlines the typical steps involved in the writing process: pre-writing, organizing, writing a first draft, revising and editing, and producing a final draft. Pre-writing involves choosing a topic, brainstorming ideas, and focusing the topic. Organizing means creating an outline to plan the essay. The first draft is written without worrying about minor errors. It is then revised by reviewing content and organization, and edited for spelling, grammar, and punctuation. The final copy is the polished draft submitted for grading.The Writing Process
The Writing ProcessAhsan Qazi
油
The document provides an overview of the writing process. It discusses why we write, the key steps in the writing process which include choosing a topic, defining the audience, researching the topic, organizing thoughts, and revising. It also outlines different forms of writing like persuasive, expository, creative, and scientific writing. Tips for structuring writing are also provided such as using an introduction, body paragraphs, and conclusion. The document emphasizes planning, clear purpose, simple language, avoiding redundancy, and proofreading. It concludes with a writing exercise asking the reader to persuade a customer to buy an SUV.Natural Language Toolkit (NLTK), Basics 
Natural Language Toolkit (NLTK), Basics Prakash Pimpale
油
The presentation describes how to install the NLTK and work out the basics of text processing with it. The slides were meant for supporting the talk and may not be containing much details.Many of the examples given in the slides are from the NLTK book (http://www.amazon.com/Natural-Language-Processing-Python-Steven/dp/0596516495/ref=sr_1_1?ie=UTF8&s=books&qid=1282107366&sr=8-1-spell ). Lecture: Word Sense Disambiguation
Lecture: Word Sense DisambiguationMarina Santini
油
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.What is academic writing
What is academic writingarthurdemelosa
油
A presentation about the chapter "What is Academic Writing" by L. Lennie Irvin, from the book "Writing Spaces".Automatic multiple choice question generation system for
Automatic multiple choice question generation system forAlexander Decker
油
The International Institute for Science, Technology and Education (IISTE). Science, Technology and Medicine Journals Call for Academic ManuscriptsUnderstanding the Web Page Layout
Understanding the Web Page LayoutJhaun Paul Enriquez
油
The document discusses HTML elements and web page layout. It defines various HTML tags like <header>, <nav>, <section>, <article>, and <footer> that are used to semantically mark up the different parts of a web page. These semantic elements clearly describe their meaning to both browsers and developers, and help structure the layout of web pages.Proofreading
ProofreadingJools Jerome
油
This document provides guidance on proofreading written work. It explains that proofreading involves carefully examining text to find and correct errors in spelling, grammar, style, and punctuation. It recommends proofreading strategies like reading text out loud and covering lines to avoid skipping ahead. The document also defines key proofreading terms and directs the reader to additional online proofreading resources and techniques.Machine Tanslation
Machine TanslationMahsa Mohaghegh
油
Machine translation is the use of computers to translate text from one language to another. There are two main approaches: rule-based systems which directly convert words and grammar between languages, and statistical systems which analyze phrases and "learn" translations from large datasets. While rule-based systems can model complex translations, statistical systems are better suited for most applications today due to lower costs and greater robustness. Current popular machine translation services include Google Translate, Microsoft Translator, and IBM's statistical speech-to-speech translator Mastor.Introduction to Natural Language Processing (NLP)
Introduction to Natural Language Processing (NLP)VenkateshMurugadas
油
These slides are an introduction to the understanding of the domain NLP and the basic NLP pipeline that are commonly used in the field of Computational Linguistics. Machine Learning With Logistic Regression
Machine Learning With Logistic RegressionKnoldus Inc.
油
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.ANTLR4 and its testing
ANTLR4 and its testingKnoldus Inc.
油
The presentation covers ANTLR and its testing. In the presentation we will discuss what is grammar and how its been parsed into its corresponding parse tree. Then we will focus on the stages of the process of parsing. We will then understand what is ANTLR and will see some of the companies exploring features of ANTLR. Towards the end of the discussion we discuss how to test weather an input string is correct with respect to a grammar or not using TestRig along with the demonstration.
You may refer following blog:
https://blog.knoldus.com/2016/04/29/testing-grammar-using-antlr4-testrig-grun/Amazon sentimental analysis
Amazon sentimental analysisAkhila
油
1. The document describes an analysis of sentiment in reviews from Amazon Fine Foods using natural language processing techniques.
2. Over 568,454 reviews from 256,059 users on 74,258 products were analyzed to determine if each review expressed a positive, negative, or neutral sentiment.
3. After data cleaning and text preprocessing using techniques like removing stop words and applying stemming/lemmatization, different text vectorization techniques (bag-of-words, tf-idf, word2vec) were compared to represent the text of each review, with word2vec found to perform best.
4. Several classification algorithms were tested on the text vectors to predict sentiment, with logistic regression achieving the highest accuracyNatural Language Generation: New Automation and Personalization Opportunities
Natural Language Generation: New Automation and Personalization OpportunitiesAutomated Insights
油
Discover how natural language generation technology is providing new opportunities for writing automation in business, significantly increasing productivity, efficiency and personalization across a range of industries.
Outlining new practical applications for using natural language generation technology to automate personalized content for clients, prospects, shoppers and internal audiences.Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...Edureka!
油
( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** )
This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT:
Introduction to Big Data
What is Text Mining?
What is NLP?
Introduction to Stemming
Introduction to Lemmatization
Applications of Stemming & Lemmatization
Difference between stemming & Lemmatization
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edurekaAttribute oriented analysis
Attribute oriented analysisHirra Sultan
油
Attribute oriented analysis is a technique in data mining and knowledge discovery. Using this, useful knowledge is derived from large amounts of data.(Fast) Introduction to HTML & CSS 
(Fast) Introduction to HTML & CSS Dave Kelly
油
HTML structures web documents and defines the semantics, or meaning, of content. CSS handles presentation and styling. HTML uses tags to define headings, paragraphs, lists, links and other content. CSS allows styling of elements using selectors, properties and values. External CSS files allow separation of concerns and reuse of styles across pages.Automatic Persona Generation: Introduction & Current Challenges
Automatic Persona Generation: Introduction & Current ChallengesJoni Salminen
油
Dr. Joni Salminen
Invited talk at IT University Copenhagen
September 30, 2021
https://persona.qcri.org
#personas #data #analyticsFive NLP Challenges in Data-Driven Personas
Five NLP Challenges in Data-Driven PersonasJoni Salminen
油
Dr. Joni Salminen
Invited talk at Nanyang Technological University, Singapore
October 20, 2021
https://persona.qcri.org
#personas #NLP #problemsMore Related Content
What's hot (20)
Learn how to write a review of literature.
Learn how to write a review of literature.Rakib Hossain
油
This document provides guidance on writing a literature review by outlining its typical sections and content. It explains that a literature review analyzes and summarizes prior research on a topic to provide context. The introduction should define the topic and identify trends, conflicts or gaps in previous work. The body groups and summarizes studies according to common factors and provides analysis. The conclusion summarizes major contributions, evaluates the current state of knowledge, and discusses relationships to broader areas of study.Sentiment analysis
Sentiment analysisChaand Chopra
油
Sentiment Analysis and Opinion Mining
Bing Liu
The University of Illinois at Chicago
Chapter 1 2 and 3How to write a Research Paper
How to write a Research PaperThesis Scientist Private Limited
油
How to write a research paper: How to write a research paper?
The research paper can be considered as the living thing which grows and changes as the student explores, interprets and evaluates sources related to a specific topic. Steps to write a research paper:
Choose your topic.
Begin your search.
Making an outline.
Write your paper.
Academic Writing - The Writing Process
Academic Writing - The Writing ProcessContentwritings Ltd
油
The document outlines the typical steps involved in the writing process: pre-writing, organizing, writing a first draft, revising and editing, and producing a final draft. Pre-writing involves choosing a topic, brainstorming ideas, and focusing the topic. Organizing means creating an outline to plan the essay. The first draft is written without worrying about minor errors. It is then revised by reviewing content and organization, and edited for spelling, grammar, and punctuation. The final copy is the polished draft submitted for grading.The Writing Process
The Writing ProcessAhsan Qazi
油
The document provides an overview of the writing process. It discusses why we write, the key steps in the writing process which include choosing a topic, defining the audience, researching the topic, organizing thoughts, and revising. It also outlines different forms of writing like persuasive, expository, creative, and scientific writing. Tips for structuring writing are also provided such as using an introduction, body paragraphs, and conclusion. The document emphasizes planning, clear purpose, simple language, avoiding redundancy, and proofreading. It concludes with a writing exercise asking the reader to persuade a customer to buy an SUV.Natural Language Toolkit (NLTK), Basics
Natural Language Toolkit (NLTK), Basics Prakash Pimpale
油
The presentation describes how to install the NLTK and work out the basics of text processing with it. The slides were meant for supporting the talk and may not be containing much details.Many of the examples given in the slides are from the NLTK book (http://www.amazon.com/Natural-Language-Processing-Python-Steven/dp/0596516495/ref=sr_1_1?ie=UTF8&s=books&qid=1282107366&sr=8-1-spell ). Lecture: Word Sense Disambiguation
Lecture: Word Sense DisambiguationMarina Santini
油
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.What is academic writing
What is academic writingarthurdemelosa
油
A presentation about the chapter "What is Academic Writing" by L. Lennie Irvin, from the book "Writing Spaces".Automatic multiple choice question generation system for
Automatic multiple choice question generation system forAlexander Decker
油
The International Institute for Science, Technology and Education (IISTE). Science, Technology and Medicine Journals Call for Academic ManuscriptsUnderstanding the Web Page Layout
Understanding the Web Page LayoutJhaun Paul Enriquez
油
The document discusses HTML elements and web page layout. It defines various HTML tags like <header>, <nav>, <section>, <article>, and <footer> that are used to semantically mark up the different parts of a web page. These semantic elements clearly describe their meaning to both browsers and developers, and help structure the layout of web pages.Proofreading
ProofreadingJools Jerome
油
This document provides guidance on proofreading written work. It explains that proofreading involves carefully examining text to find and correct errors in spelling, grammar, style, and punctuation. It recommends proofreading strategies like reading text out loud and covering lines to avoid skipping ahead. The document also defines key proofreading terms and directs the reader to additional online proofreading resources and techniques.Machine Tanslation
Machine TanslationMahsa Mohaghegh
油
Machine translation is the use of computers to translate text from one language to another. There are two main approaches: rule-based systems which directly convert words and grammar between languages, and statistical systems which analyze phrases and "learn" translations from large datasets. While rule-based systems can model complex translations, statistical systems are better suited for most applications today due to lower costs and greater robustness. Current popular machine translation services include Google Translate, Microsoft Translator, and IBM's statistical speech-to-speech translator Mastor.Introduction to Natural Language Processing (NLP)
Introduction to Natural Language Processing (NLP)VenkateshMurugadas
油
These slides are an introduction to the understanding of the domain NLP and the basic NLP pipeline that are commonly used in the field of Computational Linguistics. Machine Learning With Logistic Regression
Machine Learning With Logistic RegressionKnoldus Inc.
油
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.ANTLR4 and its testing
ANTLR4 and its testingKnoldus Inc.
油
The presentation covers ANTLR and its testing. In the presentation we will discuss what is grammar and how its been parsed into its corresponding parse tree. Then we will focus on the stages of the process of parsing. We will then understand what is ANTLR and will see some of the companies exploring features of ANTLR. Towards the end of the discussion we discuss how to test weather an input string is correct with respect to a grammar or not using TestRig along with the demonstration.
You may refer following blog:
https://blog.knoldus.com/2016/04/29/testing-grammar-using-antlr4-testrig-grun/Amazon sentimental analysis
Amazon sentimental analysisAkhila
油
1. The document describes an analysis of sentiment in reviews from Amazon Fine Foods using natural language processing techniques.
2. Over 568,454 reviews from 256,059 users on 74,258 products were analyzed to determine if each review expressed a positive, negative, or neutral sentiment.
3. After data cleaning and text preprocessing using techniques like removing stop words and applying stemming/lemmatization, different text vectorization techniques (bag-of-words, tf-idf, word2vec) were compared to represent the text of each review, with word2vec found to perform best.
4. Several classification algorithms were tested on the text vectors to predict sentiment, with logistic regression achieving the highest accuracyNatural Language Generation: New Automation and Personalization Opportunities
Natural Language Generation: New Automation and Personalization OpportunitiesAutomated Insights
油
Discover how natural language generation technology is providing new opportunities for writing automation in business, significantly increasing productivity, efficiency and personalization across a range of industries.
Outlining new practical applications for using natural language generation technology to automate personalized content for clients, prospects, shoppers and internal audiences.Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...Edureka!
油
( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** )
This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT:
Introduction to Big Data
What is Text Mining?
What is NLP?
Introduction to Stemming
Introduction to Lemmatization
Applications of Stemming & Lemmatization
Difference between stemming & Lemmatization
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edurekaAttribute oriented analysis
Attribute oriented analysisHirra Sultan
油
Attribute oriented analysis is a technique in data mining and knowledge discovery. Using this, useful knowledge is derived from large amounts of data.(Fast) Introduction to HTML & CSS
(Fast) Introduction to HTML & CSS Dave Kelly
油
HTML structures web documents and defines the semantics, or meaning, of content. CSS handles presentation and styling. HTML uses tags to define headings, paragraphs, lists, links and other content. CSS allows styling of elements using selectors, properties and values. External CSS files allow separation of concerns and reuse of styles across pages.More from Joni Salminen (20)
Automatic Persona Generation: Introduction & Current Challenges
Automatic Persona Generation: Introduction & Current ChallengesJoni Salminen
油
Dr. Joni Salminen
Invited talk at IT University Copenhagen
September 30, 2021
https://persona.qcri.org
#personas #data #analyticsFive NLP Challenges in Data-Driven Personas
Five NLP Challenges in Data-Driven PersonasJoni Salminen
油
Dr. Joni Salminen
Invited talk at Nanyang Technological University, Singapore
October 20, 2021
https://persona.qcri.org
#personas #NLP #problemsProblem of majority voting
Problem of majority votingJoni Salminen
油
The document discusses agreement levels among raters when evaluating comments. It shows that with 3 raters, 100% agreement is achieved, but with 5 raters only 40.1% of comments reach at least 66.7% agreement, and increasing the number of raters to 7 and 9 only modestly increases the percentage of comments with over 66.7% agreement to 43.5% and 44.9%, respectively.Persona Analytics: Progress Report and Road Ahead
Persona Analytics: Progress Report and Road AheadJoni Salminen
油
Science Monday presentation at Qatar Computing Research Institute
Dr. Joni Salminen, May 24 2021
https://persona.qcri.orgEnriching social media personas with personality traits
Enriching social media personas with personality traitsJoni Salminen
油
Presentation at the International Conference of Human-Computer Interaction (HCII'20). Remote presentation, July 21, 2020.User Studies for APG: How to support system development with user feedback?
User Studies for APG: How to support system development with user feedback?Joni Salminen
油
Presentation at QCRI's Science Monday of the Social Computing group. January 14, 2019. Doha, Qatar. Access the Automatic Persona Generation system: https://persona.qcri.orgCombining Behaviors and Demographics to Segment Online Audiences:Experiments ...
Combining Behaviors and Demographics to Segment Online Audiences:Experiments ...Joni Salminen
油
Link to article: https://www.springerprofessional.de/en/combining-behaviors-and-demographics-to-segment-online-audiences/16204306
CITE: Jansen, Bernard J., Jung, S., Salminen, J., An, J. and Kwak, H. (2018), Combining Behaviors and Demographics to Segment Online Audiences: Experiments with a YouTube Channel, Proceedings of the 5th International Conference of Internet Science (INSCI 2018), Springer, St. Petersburg, Russia.
Link to Automatic Persona Generation: https://persona.qcri.orgResearch Roadmap for Automatic Persona Generation (2018)
Research Roadmap for Automatic Persona Generation (2018)Joni Salminen
油
This document outlines a research roadmap for automatically generating personas by addressing key challenges including choosing the correct information elements and layouts for different users and use cases, finding representative quotes, evaluating completeness and credibility of personas, inferring user attributes from data, analyzing changes over time, selecting profile images, and applying personas in different contexts such as e-commerce, politics, and gaming. The goal is to process vast online data to better represent users.To Use Branded Keywords or Not? Rationale of Professional Search-engine Marke...
To Use Branded Keywords or Not? Rationale of Professional Search-engine Marke...Joni Salminen
油
CITE: "Lyytikk辰, J., Salminen, J., & Jansen, B. J. (2018). To Use Branded Keywords or Not? Rationale of Professional Search-engine Marketers for Brand Bidding Strategy. Presented at the 13th Global Brand Conference, Northumbria University, UK, 24 May."
Download paper: http://jonisalminen.com/wp-content/uploads/2018/08/To-use-branded-keywords-or-not-Rationale-of-professional-search-engine-marketers-for-brand-bidding-strategy.pdfDetermining Online Brand Reputation with Machine Learning from Social Media M...
Determining Online Brand Reputation with Machine Learning from Social Media M...Joni Salminen
油
CITE: "Rantanen, A., Salminen, J., & Jansen, B. J. (2018). Determining Online Brand Reputation with Machine Learning from Social Media Mentions: A Study in the Banking Context. Presented at the 13th Global Brand Conference, Northumbria University, UK, 24 May."Is More Better?: Impact of Multiple Photos on Perception of Persona Profiles
Is More Better?: Impact of Multiple Photos on Perception of Persona ProfilesJoni Salminen
油
The document reports on a study that examined how the inclusion of different types of photos in automatically generated online persona profiles impacts people's perceptions of confusion and informativeness. The study found that including contextual photos increased perceived informativeness while including multiple similar attribute photos increased confusion. The results suggest that including a headshot photo and contextual photos of the same person provides the optimal persona profile design.Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for...
Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for...Joni Salminen
油
CITE: "Salminen, J., Almerekhi, H., Milenkovi, M., Jung, S., An, J., Kwak, H., & Jansen, B. J. (2018). Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2018), San Francisco, California, USA, 2528 June."
Download paper: http://jonisalminen.com/wp-content/uploads/2018/08/Anatomy-of-hate_aaai18_ICWSM18_submit_final_camera.pdfOSS-EBM: Open Source Software Entrepreneurial Business Modelling
OSS-EBM: Open Source Software Entrepreneurial Business ModellingJoni Salminen
油
CITE: Teixeira, J., & Salminen, J. (2014). Open-Source Software Entrepreneurial Business Modelling. In L. Corral, A. Sillitti, G. Succi, J. Vlasenko, & A. I. Wasserman (Eds.), Open Source Software: Mobile Open Source Technologies (pp. 8082). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-55128-4_10
Gender effect on e-commerce sales of experience gifts: Preliminary empirical ...
Gender effect on e-commerce sales of experience gifts: Preliminary empirical ...Joni Salminen
油
CITE: "Salminen, J., Seitz, S., Jansen, B. J., & Salenius, T. (2017). Gender Effect on E-Commerce Sales of Experience Gifts: Preliminary Empirical Findings. In Proceedings of International Conference on Electronic Business (ICEB 2017). Dubai, 48 December."
We analyze purchase data from 493 customers of an e-commerce store selling experience gifts to find how gender correlates with average purchase value, category of purchased products, and the use of discount codes. We find no significant differences for average purchase value or category of purchased products, but according to the data, women are more likely to use discount codes than are males. Ideas for further research concerning the gender effect on online shopping behavior are discussed.
Link to full paper: https://www.researchgate.net/publication/321586760_Gender_effect_on_e-commerce_sales_of_experience_gifts_Preliminary_empirical_findings
Keywords: e-commerce; online consumer behavior; online purchase behavior; online shopping behavior; genderTips for Scale Development: Evaluating Automatic Personas
Tips for Scale Development: Evaluating Automatic PersonasJoni Salminen
油
This document discusses research on automatically generating persona profiles from online data. It describes an Automatic Persona Generation (APG) system that aims to computationally analyze vast amounts of online data to discover useful representations of personas. Various techniques are discussed for different aspects of persona generation, including information architecture, commenting analysis, profile picture generation, topic classification, and temporal analysis of how personas change over time. It also discusses challenges in evaluating generated personas, both in terms of objective accuracy and subjective user perceptions. The document provides tips and guidelines for developing a persona perception scale to systematically measure how users view automatically generated personas.Big Data, Small Personas: Research Agenda for Automatic Persona Generation
Big Data, Small Personas: Research Agenda for Automatic Persona GenerationJoni Salminen
油
A presentation at ICSEC17. Doha, Qatar. Read more: https://persona.qcri.org
***
Automatic Persona Generation (APG) is a system and methodology developed at Qatar Computing Research Institute, Hamad Bin Khalifa University.
The goal is to give faces to social and online analytics data. Personas can be generated from YouTube, Facebook, and Google Analytics data.
The system can be found at https://persona.qcri.org
Why do startups avoid difficult problems?
Why do startups avoid difficult problems?Joni Salminen
油
CITE: "Salminen, J. (2013) Why avoid difficult problems? Exploring the avoidance behavior within startup motive. Proceedings of LCBR European Marketing Conference, August 1516, 2013, Frankfurt."
Download paper: http://jonisalminen.com/wp-content/uploads/2018/08/why-founders-avoid-difficult-problems.pdfSocial Espionage: Drawing Benefit from Competitors Social Media Presence
Social Espionage: Drawing Benefit from Competitors Social Media PresenceJoni Salminen
油
This document discusses social espionage, which involves drawing competitive intelligence from rivals' social media presence. Social espionage combines traditional competitive intelligence gathering with analysis of information on social media to gain strategic advantages. It outlines how firms can now access customer relationships through social media and describes tactical, operational, and strategic activities that constitute social espionage, such as intercepting messages, encouraging customer switching, and identifying weaknesses. Benefits include gaining insights at tactical, operational, and strategic levels, but it also carries risks. The document advocates a strategic grid approach and defines social espionage as a combination of competitive intelligence, social media monitoring, and a "Machiavellian" mindset.Strategic Digital Marketing (Digital Marketing '15 @ Oulu University)
Strategic Digital Marketing (Digital Marketing '15 @ Oulu University)Joni Salminen
油
The document discusses strategic considerations for digital marketing and channel choice. It begins by defining strategy and outlining some strategic issues in digital marketing, such as choice of channels/platforms and how digital fits in a company's business model. It then discusses factors to consider when choosing marketing channels like customer behavior, profitability, and reach. Approaches to channel choice are compared to Roman military strategies of focusing resources or dividing them. The document also covers adoption of new platforms over time, managing a portfolio of digital marketing channels at the strategic, tactical, and operational levels, and how "operative" marketing can become strategic by being linked to goals and customer interface.Social Media Marketing (Digital Marketing '15 @ Oulu University)
Social Media Marketing (Digital Marketing '15 @ Oulu University)Joni Salminen
油
This document discusses marketing in social media and provides guidance on using social media for job searching. It begins by defining social media and explaining its characteristics as two-way communication platforms for user-generated content. It then discusses why companies should use social media, noting that conversion is the ultimate goal but social media can also be used for branding. The document provides tips for running a targeted social media job campaign using Twitter, Pinterest, a landing page, and optimizing one's LinkedIn profile. It emphasizes that people are more interested in other people than companies on social media and that social media optimization will grow more important for career development.Recently uploaded (20)
PLAN_OF_WORK_PPT_BY_ROHIT_BHAIRAM_--2212020201003[1] new.pptx![PLAN_OF_WORK_PPT_BY_ROHIT_BHAIRAM_--2212020201003[1] new.pptx](https://cdn.slidesharecdn.com/ss_thumbnails/planofworkpptbyrohitbhairam-22120202010031new-250329072214-99e4fccd-thumbnail.jpg?width=560&fit=bounds)
PLAN_OF_WORK_PPT_BY_ROHIT_BHAIRAM_--2212020201003[1] new.pptxbhairamrohit948
油
Ppt about the degradation of dyes buiding web based land registration buiding web based land registration and m...
buiding web based land registration buiding web based land registration and m...habtamudele9
油
buiding web based land registration and management systemBeyond Metadata for BBC iPlayer: an autoencoder-driven approach for embedding...
Beyond Metadata for BBC iPlayer: an autoencoder-driven approach for embedding...Simone Spaccarotella
油
I built a machine learning pipeline that generates metadata item embeddings for the calculation of item similarity in the context of Content-To-Content similarity recommendations for BBC iPlayer programmes.Sources of Data and Data collection methods.pptx
Sources of Data and Data collection methods.pptxdenniskhisa
油
Sources of Data and Data collection methodsOPPOTUS - Malaysias on Malaysia 4Q2024.pdf
OPPOTUS - Malaysias on Malaysia 4Q2024.pdfOppotus
油
Key things you need to know on Malaysian consumer confidence, key behavioral, tech, e-wallet & crypto trends in Malaysia. Model Context Protocol - path to LLM standartization
Model Context Protocol - path to LLM standartizationAntons Kranga
油
Tech talk materials: Model Context Protocol (MCP), a framework designed to design and private data source access for Generative AI applications. Well discuss how MCP integrates into existing AI system architecture. We will see is it really an app-store moment for Generative AI or not?
See examples on: http://github.com/akranga/mcp-workshop/Capital market of Nigeria and its economic values
Capital market of Nigeria and its economic valuesezehnelson104
油
Shows detailed Explanation of the Nigerian capital market and how it affects the country's vast economyHow can Competitive Intelligence Platforms benefit a Business?
How can Competitive Intelligence Platforms benefit a Business?Contify
油
Competitive intelligence platforms help businesses stay ahead by analyzing market trends, tracking competitors, and identifying growth opportunities. They provide real-time insights, improving decision-making and strategic planning. With data-driven analysis, businesses can optimize marketing, enhance product development, and gain a competitive edge, ensuring long-term success in a dynamic market.
For more information please visit here https://www.contify.com/platform/
PostGIS Workshop: a comprehensive tutorial.ppt
PostGIS Workshop: a comprehensive tutorial.pptLonJames2
油
PostGIS workshop covering installation, performance tuning, troubleshooting and extensibility. Comprehensive.GenAI-powered assistants compared in a real case - 2025-03-18
GenAI-powered assistants compared in a real case - 2025-03-18Alessandra Bilardi
油
AWS User Group Venezia #7
How to choose GenAI-powered assistants.
https://www.meetup.com/aws-user-group-venezia/events/305666720/
Agile Infinity: When the Customer Is an Abstract Concept
Agile Infinity: When the Customer Is an Abstract ConceptLoic Merckel
油
巨介 巨 腫咋介 介稲腫咋介 瑞稲 腫諮稲介署: 駒瑞駒稲 腫腫 基駒告 咋署告介咋介諮駒腫諮
In some SAFe and Scrum setups, the user is so astronomically far removed, they become a myth.
The product? Unclear.
The focus? Process.
Working software? Closing Jira tickets.
Customer feedback? A demo to a proxy of a proxy.
Customer value? A velocity chart.
Agility becomes a prescribed ritual.
Agile becomes a performance, not a mindset.
Welcome to the Agile business:
鏝 where certifications are dispensed like snacks from vending machines behind a 7/11 in a back alley of Kiyamachi,
鏝 where framework templates are sold like magic potions,
鏝 where Waterfall masquerades in Scrum clothing,
鏝 where Prime One-Day delivery out-of-the-box rigid processes are deployed in the name of adaptability.
And yet...
鏝 Some do scale value.
鏝 Some focus on real outcomes.
鏝 Some remember the customer is not a persona in a deck; but someone who actually uses the product and relies on it to succeed.
鏝 Some do involve the customer along the way.
And this is the very first principle of the Agile Manifesto.
Not your typical SAFe deck.
鏝 Viewer discretion advised: this deck may challenge conventional thinking.
Only the jester can speak truth to power.
![[3] Storing and backup - AIESEC Session 2](https://cdn.slidesharecdn.com/ss_thumbnails/3storingandbackup-250328143426-2d917e75-thumbnail.jpg?width=560&fit=bounds)
Selzy: Simplifying Email Marketing for Maximum Growth
Selzy: Simplifying Email Marketing for Maximum GrowthSelzy
油
This presentation is about Selzy, an easy-to-use and affordable email marketing tool that helps businesses create and launch effective email campaigns with minimal effort. It highlights the challenges of traditional email marketing, showcases Selzys AI-powered email builder, fast setup, and 24/7 support, and demonstrates the tools impact through user growth and market potential. With a strong ROI and a rapidly expanding customer base, Selzy positions itself as a powerful yet simple solution for businesses looking to boost engagement and sales through email marketing.
AI system mimicking human expert decision-making..pptx
AI system mimicking human expert decision-making..pptxritikacompscience
油
AI: Machines learning, reasoning, problem-solving.
Beyond Metadata for BBC iPlayer: an autoencoder-driven approach for embedding...
Beyond Metadata for BBC iPlayer: an autoencoder-driven approach for embedding...Simone Spaccarotella
油
Text analysis: An introduction to methods and techniques
- 1. Text analysis An introduction to methods and techniques
- 2. This presentation is based on the material from Aylien.com, the natural- language processing platform.
- 3. Text analysis methods 1. language detection 2. sentiment analysis 3. text summarization 4. text classification 5. entity extraction 6. concept extraction
- 10. Thanks!