video link => http://youtu.be/D9PBX8FmtpQ
Tweets Classifier which categorises tweets into these 6 categories:
Business
Politics
Music
Health
Sports
Technology
IRE Project IIIT Hyderabad Tweet classification Group 37manish jindal
Classification of tweets using different machine learning approach into wiki categories.IIIT hyderabad Project no 9 , Group no 37.
Submitted by-
Manish Jindal
Trilok Sharma
Yash Shah
Sentiment Prediction and Analysis of Major Airline Tweets using Rule-Based and Machine Learning Classifications. General Assembly Data Science Capstone Project.
Sentiment analysis is the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback
IRE Project IIIT Hyderabad Tweet classification Group 37manish jindal
Classification of tweets using different machine learning approach into wiki categories.IIIT hyderabad Project no 9 , Group no 37.
Submitted by-
Manish Jindal
Trilok Sharma
Yash Shah
Sentiment Prediction and Analysis of Major Airline Tweets using Rule-Based and Machine Learning Classifications. General Assembly Data Science Capstone Project.
Sentiment analysis is the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback
Sentiment Analysis/Opinion Mining of Twitter Data on Unigram/Bigram/Unigram+Bigram Model using:
1. Machine Learning
2. Lexical Scores
3. Emoticon Scores
YouTube Video: https://youtu.be/VuR16P87yPE
Link to the WebPage: http://akirato.github.io/Twitter-Sentiment-Analysis-Tool
Github Page: https://github.com/Akirato/Twitter-Sentiment-Analysis-Tool
Set it and Forget it: Auto Scaling Target Tracking Policies - AWS Online Tech...Amazon Web Services
Learning Objectives:
- How and when to use step scaling policies
- How to use target tracking scaling policies
- How to use scaling policies for dynamic scaling and EC2 fleet management
Presentation at Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021 organized by the scientific research group in Egypt with Collaboration with Faculty of Computers and AI, Cairo University and the Chinese University in Egypt
Kaggle Dataset -Twitter Sentiment analysis for Airlines
Exploratory Analysis
i.When do people tweet?
ii.Which airlines gets the most tweets?
iii.Which sentiments are dominant?
iv.How these sentiments are distributed?
Text Analytics
i.Most frequently used words
ii.Most frequently used words when the sentiment is negative.
iii.Most frequently used words when the sentiment is positive.
iv.Tweet length vs Sentiment
Before starting the analysis ,data cleansing was carried where in Null and missing values were replaced with appropriate values and some records were even deleted.
To ensured that there was no class imbalance problem ,tweets with positive and negative sentiment were seeded in equal proportions.
Analysed the time of tweet to know the issues faced by customer during rush hours. Classified the tweets into positive and negative and later on analysed the reasons for negative tweets.Analysed distribution of tweets between different airlines.
Created the Word cloud for tweets for each airlines.Carried out association analysis on the words used in the tweet to find the words that are correlated and are commonly found in tweets,further carried out hierarchical clustering to confirm on the relation between the words.
Analysed and found a strong association between tweet length and sentiment. Longer the tweet,higher the negative sentiment in it.
Sentiment analysis or opinion mining is a process of categorizing and identifying the sentiment expressed in a particular text. The need of automatic sentiment retrieval of
the text is quite high as a number of reviews obtained from the Internet sources like Twitter are huge in number. These reviews or opinions on popular products or events help in determining the public opinion towards the issue. An averaged histogram model is proposed in the process that deals with text classification in continuous variable approach. After data cleaning and feature extraction from the reviews, average histograms are constructed for every class, containing a generalized feature representation in that particular class, namely positive and negative. Histograms of every test elements are then classified using k-NN, Bayesian Classifier and LSTM network. This work is then implemented in Android integrated with Twitter. The user will have to provide the topic for analysis. The Application will show the result as the percentage of positive review tweets in favor of the topic using Bayesian Classifier.
Sentiment Analysis/Opinion Mining of Twitter Data on Unigram/Bigram/Unigram+Bigram Model using:
1. Machine Learning
2. Lexical Scores
3. Emoticon Scores
YouTube Video: https://youtu.be/VuR16P87yPE
Link to the WebPage: http://akirato.github.io/Twitter-Sentiment-Analysis-Tool
Github Page: https://github.com/Akirato/Twitter-Sentiment-Analysis-Tool
Set it and Forget it: Auto Scaling Target Tracking Policies - AWS Online Tech...Amazon Web Services
Learning Objectives:
- How and when to use step scaling policies
- How to use target tracking scaling policies
- How to use scaling policies for dynamic scaling and EC2 fleet management
Presentation at Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021 organized by the scientific research group in Egypt with Collaboration with Faculty of Computers and AI, Cairo University and the Chinese University in Egypt
Kaggle Dataset -Twitter Sentiment analysis for Airlines
Exploratory Analysis
i.When do people tweet?
ii.Which airlines gets the most tweets?
iii.Which sentiments are dominant?
iv.How these sentiments are distributed?
Text Analytics
i.Most frequently used words
ii.Most frequently used words when the sentiment is negative.
iii.Most frequently used words when the sentiment is positive.
iv.Tweet length vs Sentiment
Before starting the analysis ,data cleansing was carried where in Null and missing values were replaced with appropriate values and some records were even deleted.
To ensured that there was no class imbalance problem ,tweets with positive and negative sentiment were seeded in equal proportions.
Analysed the time of tweet to know the issues faced by customer during rush hours. Classified the tweets into positive and negative and later on analysed the reasons for negative tweets.Analysed distribution of tweets between different airlines.
Created the Word cloud for tweets for each airlines.Carried out association analysis on the words used in the tweet to find the words that are correlated and are commonly found in tweets,further carried out hierarchical clustering to confirm on the relation between the words.
Analysed and found a strong association between tweet length and sentiment. Longer the tweet,higher the negative sentiment in it.
Sentiment analysis or opinion mining is a process of categorizing and identifying the sentiment expressed in a particular text. The need of automatic sentiment retrieval of
the text is quite high as a number of reviews obtained from the Internet sources like Twitter are huge in number. These reviews or opinions on popular products or events help in determining the public opinion towards the issue. An averaged histogram model is proposed in the process that deals with text classification in continuous variable approach. After data cleaning and feature extraction from the reviews, average histograms are constructed for every class, containing a generalized feature representation in that particular class, namely positive and negative. Histograms of every test elements are then classified using k-NN, Bayesian Classifier and LSTM network. This work is then implemented in Android integrated with Twitter. The user will have to provide the topic for analysis. The Application will show the result as the percentage of positive review tweets in favor of the topic using Bayesian Classifier.
The lecture was delivered on the Online Course on Macroeconomic Modelling, estimating and modelling on http://elearning.aneconomist.com. Students on this course will get all the lessons also in form of recorded videos and will be offered to select their specific topics in the offered course which will then be presented Live and Interactively.
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding D...Jinho Choi
Recent advances in deep learning have facilitated the demand of neural models for real applications. In practice, these applications often need to be deployed with limited resources while keeping high accuracy. This paper touches the core of neural models in NLP, word embeddings, and presents a new embedding distillation framework that remarkably reduces the dimension of word embeddings without compromising accuracy. A novel distillation ensemble approach is also proposed that trains a high-efficient student model using multiple teacher models. In our approach, the teacher models play roles only during training such that the student model operates on its own without getting supports from the teacher models during decoding, which makes it eighty times faster and lighter than other typical ensemble methods. All models are evaluated on seven document classification datasets and show significant advantage over the teacher models for most cases. Our analysis depicts insightful transformation of word embeddings from distillation and suggests a future direction to ensemble approaches using neural models.
This lecture has been delivered during the Online Course on Macroeconomic Modelling using Eviews. This lecture summarizes the use of VAR and Cointegration Testing before forecasting the model afterwards. This lecture further introduced the use Eviews.
weakly supervised deep embedding for product review sentiment analysisVenkat Projects
Product reviews are valuable for upcoming buyers in helping them make decisions. To this end, different opinion mining techniques have been proposed, where judging a review sentence’s orientation (e.g. positive or negative) is one of their key challenges. Recently, deep learning has emerged as an effective means for solving sentiment classification problems. A neural network intrinsically learns a useful representation automatically without human efforts. However, the success of deep learning highly relies on the availability of large-scale training data. We propose a novel deep learning framework for product review sentiment classification which employs prevalently available ratings as weak supervision signals. The framework consists of two steps: (1) learning a high level representation (an embedding space) which captures the general sentiment distribution of sentences through rating information; (2) adding a classification layer on top of the embedding layer and use labeled sentences for supervised fine-tuning. We explore two kinds of low level network structure for modeling review sentences, namely, convolutional feature extractors and long short-term memory. To evaluate the proposed framework, we construct a dataset containing 1.1M weakly labeled review sentences and 11,754 labeled review sentences from Amazon. Experimental results show the efficacy of the proposed framework and its superiority over baselines.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2. INTRODUCTION
Tweet Classification model categorizes the input tweets into one of the genres like
politics, sports, music, technology, health and business.
Model was trained from a set of predefined tweets.
Based on this training model, the classifier makes decision regarding which class
the test input belongs to.
3. APPROACHES
•First challenge was to collect a proper set of tweets which was going to be
utilized for training the model.
• Next step was to identify a set of keywords for each category based on which
tweets were fetched.
Two Approaches were used:
1) Naive Baye’s
2) SVM (Support Vector Machine)
Relative comparison of performance of both Algorithms.
4. NAÏVE BAYE’S MODEL
• A high dimensional dense vector for each tweet is constructed.
• Vector is constructed using each unique word of training tweets.
• Each word is treated as an independent feature.
• These features are treated as independent of each other and they contribute equally
in classification of any tweet.
5. SUPPORT VECTOR MACHINE
• A high dimensional dense vector is constructed for input tweet.
• Multiclass variant of SVM model was created for having multi-class classification.
Feature Selection
Here each word in the tweet is taken as independent feature which contributes in
the decision of classifying the tweet into any class.
We are using Unigram approach in this techique.
Tools/libraries used
LIBSVM : Used to scale train and test file.
WEKA : Used for implementing Naive Bayes classification.
6. Over Fitting issues
There is high probability that this classification model will be highly biased
towards its training set data. So the impact on the classification is one particular
tweet will be classified in its correct class because words used in were present in
training set but tweet with similar meaning but containing different set of words
might not be classified in the same class.
8. EXPERIMENTS AND RESULTS
•The model has been experimented with a certain amount of test data separated
from the training data. The model, in turn, was verified for accuracy levels.
•The final result is the graph / chart categorizing the user tweets on various genres.
9. Tweet : microsoft 's cortana assistant personalization comes to bing on the web
Result : Technology Class (Naïve Bayes Model)
10. Tweet : Lady Gaga released a new album
Result : Music Class (SVM model)
11. CONCLUSION
Using the above described approaches(SVM and Naïve Bayes) tweets are
classified into their respective categories with a very little percentage of error.
12. REFERENCES
•A Machine Learning Approach to Twitter User Classification by Marco
Pennacchiotti and Ana-Maria Popescu
http://coitweb.uncc.edu/~anraja/courses/SMS/SMSBib/2886-14198-1-PB.pdf
•Short Text Classification in Twitter to Improve Information Filtering by Bharath
Sriram, David Fuhry, Engin Demir, Hakan Ferhatosmanoglu
http://www.cs.bilkent.edu.tr/~hakan/publication/TweetClassification.pdf
•Twitter Trending Topic Classification by Kathy Lee, Diana Palsetia, Ramanathan
Narayanan, Md. Mostofa Ali Patwary, Ankit Agrawal, and Alok Choudhary
http://cucis.ece.northwestern.edu/publications/pdf/LeePal11.pdf
•Analysis and Classication of Twitter messages by Christopher Horn
http://know-center.tugraz.at/wp-content/uploads/2010/12/Master-Thesis-
Christopher-Horn.pdf