In a Twitter dataset we are provided with tweets, retrieved per a pre-defined “Trend”.
Can we verify those trends back from the raw statuses? If so – we could use this technique to topic any un-trended tweet-list!
On a tweet dataset, curated from a top of 10 twitter trends, I researched different Natural Language Processing (NLP) and Clustering techniques to apply on the raw tweets’ text.
I found that with the right NLP and Clustering – I could verify ~80% of the tweets back to their labeled trends!
The GPT-3 model architecture is a transformer-based neural network that has been fed 45TB of text data. It is non-deterministic, in the sense that given the same input, multiple runs of the engine will return different responses. Also, it is trained on massive datasets that covered the entire web and contained 500B tokens, humongous 175 Billion parameters, a more than 100x increase over GPT-2, which was considered state-of-the-art technology with 1.5 billion parameters.
This document summarizes a student project on aspect/topic modeling for opinion mining from tweets. The goals of the project were to preprocess tweets, apply a modified LDA technique to extract topics from tweets, and classify tweets into categories like jokes, sports, movies, and politics. The students used a probabilistic model and SVM for classification, and were able to detect new trending topics not present in training data and categorize them as potential new topics.
Now a days Twitter has provided a way to collect and understand user’s opinions about many
private or public organizations. All these organizations are reported for the sake to create and monitor
the targeted Twitter streams to understand user’s views about the organization. Usually a user-defined
selection criteria is used to filter and construct the Targeted Twitter stream. There must be an
application to detect early crisis and response with such target stream, that require a require a good
Named Entity Recognition (NER) system for Twitter, which is able to automatically discover emerging
named entities that is potentially linked to the crisis. However, many applications suffer severely from
short nature of tweets and noise. We present a framework called HybridSeg, which easily extracts and
well preserves the linguistic meaning or context information by first splitting the tweets into
meaningful segments. The optimal segmentation of a tweet is found after the sum of stickiness score of
its candidate segment is maximized.This computed stickiness score considers the probability of
segment whether belongs to global context(i.e., being a English phrase) or belongs to local context(i.e.,
being within a batch of tweets).The framework learns from both contexts.It also has the ability to learn
from pseudo feedback. Also from the result of semantic analysis the proposed system provides with
sentiment analysis.
Sensing Trending Topics in Twitter for Greater Jakarta Area IJECEIAES
Information and communication technology grows so fast nowadays, especially related to the internet. Twitter is one of internet applications that produce a large amount of textual data called tweets. The tweets may represent real-world situation discussed in a community. Therefore, Twitter can be an important media for urban monitoring. The ability to monitor the situations may guide local government to respond quickly or make public policy. Topic detection is an important automatic tool to understand the tweets, for example, using non-negative matrix factorization. In this paper, we conducted a study to implement Twitter as a media for the urban monitoring in Jakarta and its surrounding areas called Greater Jakarta. Firstly, we analyze the accuracy of the detected topics in term of their interpretability level. Next, we visualize the trend of the topics to identify popular topics easily. Our simulations show that the topic detection methods can extract topics in a certain level of accuracy and draw the trends such that the topic monitoring can be conducted easily.
- The document presents a study analyzing factors that influence continued participation in Twitter group chats. It develops a 5F model examining individual initiative, group characteristics, perceived receptivity, linguistic affinity, and geographic proximity.
- The study analyzes data from 30 educational Twitter chats over two years involving over 71,000 users. It also conducted a user survey.
- The 5F model effectively predicts whether a new user who attends one session will return based on analysis of their contributions, how well they fit with the group linguistically, and other metrics.
Question 1 describe the series of connections that would be mDIPESH30
The document contains 4 questions asking about connections and protocols when sending an email from a laptop to a wireless hotspot, how signals are clocked and how that affects data transmission, the encapsulation process of data packets on the internet, and calculations related to frequency, period, and bandwidth. It provides examples of how to reference different sources in APA style and specifies using sans serif fonts. Responses to the questions should be placed in the provided text boxes and include in-text citations and a reference list.
Meta-evaluation of machine translation evaluation methodsLifeng (Aaron) Han
Cite: Lifeng Han. 2021. Meta-evaluation of machine translation evaluation methods. In Metrics2021 Tutorial Track/type: Workshop on Informetric and Scientometric Research (SIG-MET), ASIS&T. October 23–24.
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
The GPT-3 model architecture is a transformer-based neural network that has been fed 45TB of text data. It is non-deterministic, in the sense that given the same input, multiple runs of the engine will return different responses. Also, it is trained on massive datasets that covered the entire web and contained 500B tokens, humongous 175 Billion parameters, a more than 100x increase over GPT-2, which was considered state-of-the-art technology with 1.5 billion parameters.
This document summarizes a student project on aspect/topic modeling for opinion mining from tweets. The goals of the project were to preprocess tweets, apply a modified LDA technique to extract topics from tweets, and classify tweets into categories like jokes, sports, movies, and politics. The students used a probabilistic model and SVM for classification, and were able to detect new trending topics not present in training data and categorize them as potential new topics.
Now a days Twitter has provided a way to collect and understand user’s opinions about many
private or public organizations. All these organizations are reported for the sake to create and monitor
the targeted Twitter streams to understand user’s views about the organization. Usually a user-defined
selection criteria is used to filter and construct the Targeted Twitter stream. There must be an
application to detect early crisis and response with such target stream, that require a require a good
Named Entity Recognition (NER) system for Twitter, which is able to automatically discover emerging
named entities that is potentially linked to the crisis. However, many applications suffer severely from
short nature of tweets and noise. We present a framework called HybridSeg, which easily extracts and
well preserves the linguistic meaning or context information by first splitting the tweets into
meaningful segments. The optimal segmentation of a tweet is found after the sum of stickiness score of
its candidate segment is maximized.This computed stickiness score considers the probability of
segment whether belongs to global context(i.e., being a English phrase) or belongs to local context(i.e.,
being within a batch of tweets).The framework learns from both contexts.It also has the ability to learn
from pseudo feedback. Also from the result of semantic analysis the proposed system provides with
sentiment analysis.
Sensing Trending Topics in Twitter for Greater Jakarta Area IJECEIAES
Information and communication technology grows so fast nowadays, especially related to the internet. Twitter is one of internet applications that produce a large amount of textual data called tweets. The tweets may represent real-world situation discussed in a community. Therefore, Twitter can be an important media for urban monitoring. The ability to monitor the situations may guide local government to respond quickly or make public policy. Topic detection is an important automatic tool to understand the tweets, for example, using non-negative matrix factorization. In this paper, we conducted a study to implement Twitter as a media for the urban monitoring in Jakarta and its surrounding areas called Greater Jakarta. Firstly, we analyze the accuracy of the detected topics in term of their interpretability level. Next, we visualize the trend of the topics to identify popular topics easily. Our simulations show that the topic detection methods can extract topics in a certain level of accuracy and draw the trends such that the topic monitoring can be conducted easily.
- The document presents a study analyzing factors that influence continued participation in Twitter group chats. It develops a 5F model examining individual initiative, group characteristics, perceived receptivity, linguistic affinity, and geographic proximity.
- The study analyzes data from 30 educational Twitter chats over two years involving over 71,000 users. It also conducted a user survey.
- The 5F model effectively predicts whether a new user who attends one session will return based on analysis of their contributions, how well they fit with the group linguistically, and other metrics.
Question 1 describe the series of connections that would be mDIPESH30
The document contains 4 questions asking about connections and protocols when sending an email from a laptop to a wireless hotspot, how signals are clocked and how that affects data transmission, the encapsulation process of data packets on the internet, and calculations related to frequency, period, and bandwidth. It provides examples of how to reference different sources in APA style and specifies using sans serif fonts. Responses to the questions should be placed in the provided text boxes and include in-text citations and a reference list.
Meta-evaluation of machine translation evaluation methodsLifeng (Aaron) Han
Cite: Lifeng Han. 2021. Meta-evaluation of machine translation evaluation methods. In Metrics2021 Tutorial Track/type: Workshop on Informetric and Scientometric Research (SIG-MET), ASIS&T. October 23–24.
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
Social Media Brand Positioning Workflow- David GersonPyData
This document describes using Twitter data and natural language processing techniques to create perceptual maps of social media brands. It involves extracting Twitter data for various fast food companies over a week. The text is then tokenized, stemmed, and stopwords are removed. Term frequency-inverse document frequency (TF-IDF) is used to determine the most important words for food and restaurants. A word count matrix is created to quantify words for each brand. Multidimensional scaling (MDS) is performed on the matrix to plot brand positions based on language used. The analysis of the MDS plot can reveal brand positioning opportunities and competitive threats. Some pain points encountered include ASCII encoding issues and the need for checkpoints when calculations are long.
Thomas Wolf "An Introduction to Transfer Learning and Hugging Face"Fwdays
In this talk I'll start by introducing the recent breakthroughs in NLP that resulted from the combination of Transfer Learning schemes and Transformer architectures. The second part of the talk will be dedicated to an introduction of the open-source tools released by Hugging Face, in particular our transformers, tokenizers, and NLP libraries as well as our distilled and pruned models.
Objective of the Project
Tweet sentiment analysis gives businesses insights into customers and competitors. In this project, we combined several text preprocessing techniques with machine learning algorithms. Neural network, Random Forest and Logistic Regression models were trained on the Sentiment140 twitter data set. We then predicted the sentiment of a hold-out test set of tweets. We used both Python and PySpark (local Spark Context) to program different parts of the pre-processing and modelling.
Make a query regarding a topic of interest and come to know the sentiment for the day in pie-chart or for the week in form of line-chart for the tweets gathered from twitter.com
ML Framework for auto-responding to customer support queriesVarun Nathan
This document provides an overview of Frankbot, an AI assistant created by Anthropic to be helpful, harmless, and honest. It summarizes the key aspects of Frankbot, including its use of historical customer support data to train models to intercept and resolve common customer queries, its methodology for offline training and online processing, and how it is periodically refreshed and taught by customer support agents. Metrics are presented showing how Frankbot has helped increase customer satisfaction scores while reducing average first response times for customers.
This paper describes a system for detecting toxic content in multiple Indian languages on social media. The authors used pretrained transformer models like XLM-RoBERTa and MuRIL, fine-tuned on labeled comment data from ShareChat/Moj. They performed data augmentation by transliterating text and used techniques like ensembling multiple models and adjusting inference thresholds by language. Their best system, which ensembled XLM-RoBERTa and MuRIL and used metadata features, achieved a mean F1 score of 0.9 on the test data, ranking first in the IIIT-D Multilingual Abusive Comment Identification challenge.
ML Framework for auto-responding to customer support queriesVarun Nathan
The synopsis of this presentation is about how ML can be employed to develop a bot that has the capability to understand natural language and provide suitable response.
Market Research Meets Big Data Analytics for Business Transformation Sally Sadosky
This document summarizes a presentation given by Al Nevarez and Sally Sadosky of LinkedIn on how the company uses market research and big data analytics. It discusses LinkedIn's business goals and vision, how it conducts market research through surveys, and how it analyzes massive amounts of member data using tools like Hadoop and Pig to gain insights at low cost. Integrating survey data with behavioral data through SQL joins allows answering questions about member segments and experiences.
STAT200: Assignment #2 - Descriptive Statistics Analysis and Writeup - Instructions
Page 1 of 3
STAT200 Introduction to Statistics
Assignment #2: Descriptive Statistics Analysis and Writeup
Assignment #2: Descriptive Statistics Analysis and Writeup
In the first assignment (Assignment #1: Descriptive Statistics Analysis Data Plan), you developed a
scenario about annual household expenditures and a plan for analyzing the data using descriptive
statistic methods. The purpose of this assignment is to carry out the descriptive statistics analysis plan
and write up the results. The expected outcome of this assignment is a two to three page write-up of
the findings from your analysis as well as a recommendation.
Assignment Steps:
Step #1: Review Feedback from Your Instructor
Before performing any analysis, please make sure to review your instructor’s feedback on Assignment
#1: Descriptive Statistics Data Analysis Plan. Based on the feedback, modify variables, tables, and
selected statistics, graphs, and tables, if needed.
Step #2: Perform Descriptive Statistic Analysis
Task 1: Look at the dataset.
• (Re)Familiarize yourself with the variables. Review Table 1: Variables Selected for the
Analysis you generated for the first assignment as well as your instructor’s feedback. In
addition, look at the data dictionary contained in the data set for information about the
variables.
• Select the variables you need for the analysis.
Task 2: Complete your data analysis, as outlined in your first assignment, with any needed
modifications, based on your instructor’s feedback.
• Calculate Measures of Central Tendency and Variability. Use the information from
Assignment #1 - Table 2. Numerical Summaries of the Selected Variables. Here again,
be sure to see your instructor’s feedback and incorporate into the analysis.
• Prepare Graphs and/or Tables. Use the information from Assignment #1 - Table 3.
Type of Graphs and/or Tables for Selected Variables. Here again, be sure to see your
instructor’s feedback and incorporate into the analysis.
STAT200: Assignment #2 - Descriptive Statistics Analysis and Writeup - Instructions
Page 2 of 3
Step #3: Write-up findings using the Provided Template
For this part of the assignment, write a short 2-3 page write-up of the process you followed and the
findings from your analysis. You will describe, in words, the statistical analysis used and present the
results in both statistical/text and graphic formats.
Here are the main sections for this assignment:
✓ Identifying Information. Fill in information on name, class, instructor, and date.
✓ Introduction. For this section, use the same scenario you submitted for the first assignment and
modified using your instructor’s feedback, if needed. Include Table 1 (Table 1: Variables
Selected for the Analysis) you used in Assignment #1 to show the variables you selected for the
analysis.
✓ Data .
[DSC MENA 24] Nada_GabAllah_-_Advancement_in_NLP_and_Text_Analytics.pptxDataScienceConferenc1
In recent years, NLP and text analytics have witnessed remarkable progress, transforming the way we interact with language data. From sentiment analysis to named entity recognition, these techniques play a pivotal role in understanding and extracting valuable insights from vast amounts of unstructured text. In this session, we’ll delve into the latest advancements, explore state-of-the-art models, and discuss practical applications across domains such as healthcare, finance, and customer service. Join us to unravel the intricacies of NLP and discover how it empowers organizations to unlock the hidden potential of textual information.
Details regarding the working of chatgpt and basic use cases can be found in this presentation. The presentation also contains details regarding other Open AI products and their useability. You can also find ways in which chatgpt can be implemented in existing App and websites.
Research Literature OUTLINE, APA Format!American writer Wa.docxWilheminaRossi174
The document provides an outline template in APA format for a research paper on major themes in Walt Whitman's poetry, including slavery, religion, and sexuality. It includes sections for an introduction with background and thesis, main ideas with supporting topics and references, and a conclusion. Examples of objectives and bullet points for a performance review as a technology consultant are also provided, covering efficiency, effectiveness, controls, value, self-development, and other metrics.
This document provides 6 tips for optimizing topic models like LDA for better interpretability: 1) Identify phrases through n-grams and filter for noun structures to help cluster topics. 2) Filter remaining words for nouns to extract more interpretable topics. 3) Optimize the number of topics through a coherence measure, which typically peaks around 15 topics. 4) Adjust LDA hyperparameters like iterations and passes to improve topic coherence. 5) Manually inspect topics and remove redundant words. 6) Present top words and sample documents for each topic to aid human interpretation.
This document describes a student project to design a handwritten digit recognition system. The main objective is to build a model that can recognize handwritten digits from 0 to 9. The students used the MNIST dataset and explored models like LeNet, ResNet, VGGNet, GoogleNet and CNN. They achieved 98.2% accuracy using LeNet, which had the shortest training time of 7 minutes on average. In conclusion, the students successfully created a digit recognition program using neural networks and datasets.
This document describes a project to perform sentiment analysis on Twitter product reviews using neural networks. The authors plan to use two existing datasets (IMDB movie reviews and Twitter sentiment reviews) to train models including Naive Bayes, bidirectional RNN, and bidirectional LSTM. For extra credit, they will use pseudo-labeling with an unlabeled Twitter product review dataset to improve performance. They conducted experiments including hyperparameter tuning of the BiLSTM model on the two datasets. The best BiLSTM model achieved 69.2% accuracy on the Twitter sentiment dataset and 88.5% on the larger IMDB movie review dataset.
1. The document summarizes a capstone project on automatic text summarization using both extractive and abstractive techniques.
2. It discusses motivations for summarization, approaches to extractive and abstractive summarization, data collection and analysis, classification methods, and evaluation metrics.
3. The project uses a BBC news dataset and develops sequence-to-sequence and GloVe embedding models to generate abstractive summaries that are evaluated using ROUGE scores against human-written references.
IRJET- Twitter Sentimental Analysis for Predicting Election Result using ...IRJET Journal
This document proposes using Twitter sentiment analysis and an LSTM neural network to predict election results. It involves collecting tweets related to political parties and candidates in India, cleaning the data, training an LSTM classifier on labeled tweets, and using the trained model to classify tweets as positive, negative or neutral sentiment and compare sentiment levels for each candidate. The goal is to analyze public sentiment expressed on Twitter and how it correlates with actual election outcomes.
Enhancing Enterprise Search with Machine Learning - Simon Hughes, Dice.comSimon Hughes
In the talk I describe two approaches for improve the recall and precision of an enterprise search engine using machine learning techniques. The main focus is improving relevancy with ML while using your existing search stack, be that Luce, Solr, Elastic Search, Endeca or something else.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Social Media Brand Positioning Workflow- David GersonPyData
This document describes using Twitter data and natural language processing techniques to create perceptual maps of social media brands. It involves extracting Twitter data for various fast food companies over a week. The text is then tokenized, stemmed, and stopwords are removed. Term frequency-inverse document frequency (TF-IDF) is used to determine the most important words for food and restaurants. A word count matrix is created to quantify words for each brand. Multidimensional scaling (MDS) is performed on the matrix to plot brand positions based on language used. The analysis of the MDS plot can reveal brand positioning opportunities and competitive threats. Some pain points encountered include ASCII encoding issues and the need for checkpoints when calculations are long.
Thomas Wolf "An Introduction to Transfer Learning and Hugging Face"Fwdays
In this talk I'll start by introducing the recent breakthroughs in NLP that resulted from the combination of Transfer Learning schemes and Transformer architectures. The second part of the talk will be dedicated to an introduction of the open-source tools released by Hugging Face, in particular our transformers, tokenizers, and NLP libraries as well as our distilled and pruned models.
Objective of the Project
Tweet sentiment analysis gives businesses insights into customers and competitors. In this project, we combined several text preprocessing techniques with machine learning algorithms. Neural network, Random Forest and Logistic Regression models were trained on the Sentiment140 twitter data set. We then predicted the sentiment of a hold-out test set of tweets. We used both Python and PySpark (local Spark Context) to program different parts of the pre-processing and modelling.
Make a query regarding a topic of interest and come to know the sentiment for the day in pie-chart or for the week in form of line-chart for the tweets gathered from twitter.com
ML Framework for auto-responding to customer support queriesVarun Nathan
This document provides an overview of Frankbot, an AI assistant created by Anthropic to be helpful, harmless, and honest. It summarizes the key aspects of Frankbot, including its use of historical customer support data to train models to intercept and resolve common customer queries, its methodology for offline training and online processing, and how it is periodically refreshed and taught by customer support agents. Metrics are presented showing how Frankbot has helped increase customer satisfaction scores while reducing average first response times for customers.
This paper describes a system for detecting toxic content in multiple Indian languages on social media. The authors used pretrained transformer models like XLM-RoBERTa and MuRIL, fine-tuned on labeled comment data from ShareChat/Moj. They performed data augmentation by transliterating text and used techniques like ensembling multiple models and adjusting inference thresholds by language. Their best system, which ensembled XLM-RoBERTa and MuRIL and used metadata features, achieved a mean F1 score of 0.9 on the test data, ranking first in the IIIT-D Multilingual Abusive Comment Identification challenge.
ML Framework for auto-responding to customer support queriesVarun Nathan
The synopsis of this presentation is about how ML can be employed to develop a bot that has the capability to understand natural language and provide suitable response.
Market Research Meets Big Data Analytics for Business Transformation Sally Sadosky
This document summarizes a presentation given by Al Nevarez and Sally Sadosky of LinkedIn on how the company uses market research and big data analytics. It discusses LinkedIn's business goals and vision, how it conducts market research through surveys, and how it analyzes massive amounts of member data using tools like Hadoop and Pig to gain insights at low cost. Integrating survey data with behavioral data through SQL joins allows answering questions about member segments and experiences.
STAT200: Assignment #2 - Descriptive Statistics Analysis and Writeup - Instructions
Page 1 of 3
STAT200 Introduction to Statistics
Assignment #2: Descriptive Statistics Analysis and Writeup
Assignment #2: Descriptive Statistics Analysis and Writeup
In the first assignment (Assignment #1: Descriptive Statistics Analysis Data Plan), you developed a
scenario about annual household expenditures and a plan for analyzing the data using descriptive
statistic methods. The purpose of this assignment is to carry out the descriptive statistics analysis plan
and write up the results. The expected outcome of this assignment is a two to three page write-up of
the findings from your analysis as well as a recommendation.
Assignment Steps:
Step #1: Review Feedback from Your Instructor
Before performing any analysis, please make sure to review your instructor’s feedback on Assignment
#1: Descriptive Statistics Data Analysis Plan. Based on the feedback, modify variables, tables, and
selected statistics, graphs, and tables, if needed.
Step #2: Perform Descriptive Statistic Analysis
Task 1: Look at the dataset.
• (Re)Familiarize yourself with the variables. Review Table 1: Variables Selected for the
Analysis you generated for the first assignment as well as your instructor’s feedback. In
addition, look at the data dictionary contained in the data set for information about the
variables.
• Select the variables you need for the analysis.
Task 2: Complete your data analysis, as outlined in your first assignment, with any needed
modifications, based on your instructor’s feedback.
• Calculate Measures of Central Tendency and Variability. Use the information from
Assignment #1 - Table 2. Numerical Summaries of the Selected Variables. Here again,
be sure to see your instructor’s feedback and incorporate into the analysis.
• Prepare Graphs and/or Tables. Use the information from Assignment #1 - Table 3.
Type of Graphs and/or Tables for Selected Variables. Here again, be sure to see your
instructor’s feedback and incorporate into the analysis.
STAT200: Assignment #2 - Descriptive Statistics Analysis and Writeup - Instructions
Page 2 of 3
Step #3: Write-up findings using the Provided Template
For this part of the assignment, write a short 2-3 page write-up of the process you followed and the
findings from your analysis. You will describe, in words, the statistical analysis used and present the
results in both statistical/text and graphic formats.
Here are the main sections for this assignment:
✓ Identifying Information. Fill in information on name, class, instructor, and date.
✓ Introduction. For this section, use the same scenario you submitted for the first assignment and
modified using your instructor’s feedback, if needed. Include Table 1 (Table 1: Variables
Selected for the Analysis) you used in Assignment #1 to show the variables you selected for the
analysis.
✓ Data .
[DSC MENA 24] Nada_GabAllah_-_Advancement_in_NLP_and_Text_Analytics.pptxDataScienceConferenc1
In recent years, NLP and text analytics have witnessed remarkable progress, transforming the way we interact with language data. From sentiment analysis to named entity recognition, these techniques play a pivotal role in understanding and extracting valuable insights from vast amounts of unstructured text. In this session, we’ll delve into the latest advancements, explore state-of-the-art models, and discuss practical applications across domains such as healthcare, finance, and customer service. Join us to unravel the intricacies of NLP and discover how it empowers organizations to unlock the hidden potential of textual information.
Details regarding the working of chatgpt and basic use cases can be found in this presentation. The presentation also contains details regarding other Open AI products and their useability. You can also find ways in which chatgpt can be implemented in existing App and websites.
Research Literature OUTLINE, APA Format!American writer Wa.docxWilheminaRossi174
The document provides an outline template in APA format for a research paper on major themes in Walt Whitman's poetry, including slavery, religion, and sexuality. It includes sections for an introduction with background and thesis, main ideas with supporting topics and references, and a conclusion. Examples of objectives and bullet points for a performance review as a technology consultant are also provided, covering efficiency, effectiveness, controls, value, self-development, and other metrics.
This document provides 6 tips for optimizing topic models like LDA for better interpretability: 1) Identify phrases through n-grams and filter for noun structures to help cluster topics. 2) Filter remaining words for nouns to extract more interpretable topics. 3) Optimize the number of topics through a coherence measure, which typically peaks around 15 topics. 4) Adjust LDA hyperparameters like iterations and passes to improve topic coherence. 5) Manually inspect topics and remove redundant words. 6) Present top words and sample documents for each topic to aid human interpretation.
This document describes a student project to design a handwritten digit recognition system. The main objective is to build a model that can recognize handwritten digits from 0 to 9. The students used the MNIST dataset and explored models like LeNet, ResNet, VGGNet, GoogleNet and CNN. They achieved 98.2% accuracy using LeNet, which had the shortest training time of 7 minutes on average. In conclusion, the students successfully created a digit recognition program using neural networks and datasets.
This document describes a project to perform sentiment analysis on Twitter product reviews using neural networks. The authors plan to use two existing datasets (IMDB movie reviews and Twitter sentiment reviews) to train models including Naive Bayes, bidirectional RNN, and bidirectional LSTM. For extra credit, they will use pseudo-labeling with an unlabeled Twitter product review dataset to improve performance. They conducted experiments including hyperparameter tuning of the BiLSTM model on the two datasets. The best BiLSTM model achieved 69.2% accuracy on the Twitter sentiment dataset and 88.5% on the larger IMDB movie review dataset.
1. The document summarizes a capstone project on automatic text summarization using both extractive and abstractive techniques.
2. It discusses motivations for summarization, approaches to extractive and abstractive summarization, data collection and analysis, classification methods, and evaluation metrics.
3. The project uses a BBC news dataset and develops sequence-to-sequence and GloVe embedding models to generate abstractive summaries that are evaluated using ROUGE scores against human-written references.
IRJET- Twitter Sentimental Analysis for Predicting Election Result using ...IRJET Journal
This document proposes using Twitter sentiment analysis and an LSTM neural network to predict election results. It involves collecting tweets related to political parties and candidates in India, cleaning the data, training an LSTM classifier on labeled tweets, and using the trained model to classify tweets as positive, negative or neutral sentiment and compare sentiment levels for each candidate. The goal is to analyze public sentiment expressed on Twitter and how it correlates with actual election outcomes.
Enhancing Enterprise Search with Machine Learning - Simon Hughes, Dice.comSimon Hughes
In the talk I describe two approaches for improve the recall and precision of an enterprise search engine using machine learning techniques. The main focus is improving relevancy with ML while using your existing search stack, be that Luce, Solr, Elastic Search, Endeca or something else.
Similar to Self Trending a Tweet - Cluster and Topic Analysis on Tweets (20)
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.