Sentiment Analysis in Twitter
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This document summarizes work on sentiment analysis for Twitter data. It outlines the challenges of analyzing tweets, which are informal and use slang. The described system downloads tweets, preprocesses them by handling emojis, hashtags, mentions and acronyms. It then extracts unigram and bigram features along with sentiment scores of words and emojis. A support vector machine classifier is trained on these features to predict sentiment. Testing showed the feature-based model with bigrams and lexicon features improved over a baseline model, achieving an F1 score of 61.17% for sentence-level sentiment classification.
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@growth hacking asia
http://www.slideshare.net/GrowthHackingAsia
Transform your training with simulations speaker deck - march 2011
The speaker Deck for a Keynote at the 'Financial Training Innovation' conference for Learning and Development professionals - March 15, 2011
Remote User Testing - MSU 12 Nov2021
The document provides an overview of remote user testing. It discusses why remote testing should be used, as well as when it should not be used. It then covers different types of remote testing, popular tools for remote testing, tips for effective remote testing, and answers questions from participants. The document concludes by discussing analysis and deliverables from remote testing projects.
Backchannels and broadcast
A discussion presentation on the use of teaching blogs and twitter accounts in marketing education delivery. Includes a bonus discussion of the blog as assessment item. Recommended as a download for the intra-slide links fest.
Using GradeMark to improve feedback and involve students in the marking process
GradeMark is a tool within Turnitin that allows instructors to provide electronic feedback and marks on student work. It offers various types of feedback such as in-text comments, rubrics, and general comments. Students and instructors found benefits to electronic feedback such as more detailed and positive comments. Students felt it helped them understand where to improve, while instructors could more easily link feedback to criteria and share comment libraries. Both groups preferred electronic feedback over traditional written comments. The project aimed to engage students in the marking process and received positive feedback from participants.
Machine Learning Interviews_ Lessons from Both Sides - FSDL.pptx
This document summarizes key points from a presentation on machine learning interviews. It discusses different machine learning jobs, common career paths in ML, tips for understanding interviewers' mindsets, the typical interview process, and strategies for improving one's chances of getting a job in ML. The presenter provides advice on preparing for interviews, examples of good and bad interview questions, alternative interview formats, and how companies evaluate candidates throughout the recruiting pipeline.
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Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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Temple of Asclepius in Thrace. Excavation results
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Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
Event Link:- https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-mule-event-processing-models/
Agenda
● What is event processing in MuleSoft?
● Types of event processing models in Mule 4
● Distinction between the reactive, parallel, blocking & non-blocking processing
For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
Speaker:-
Shivani Yasaswi - https://www.linkedin.com/in/shivaniyasaswi/
Organizers:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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Chapter wise All Notes of First year Basic Civil Engineering.pptx
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Mule event processing models | MuleSoft Mysore Meetup #47
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Sentiment Analysis in Twitter
- 1. Sentiment Analysis for Twitter Priyanka Bajaj priyanka.bajaj@students.iiit.ac.in Kamal Gurala kamal.gurala@students.iiit.ac.in Faraz Alam faraz.alam@students.iiit.ac.in Ritesh Kumar Gupta ritesh.kumar.gupta@in.ibm.com Guided By : Satarupa Guha satarupaguha11@gmail.com
- 2. AGENDA 1.Introduction – Sentiment Analysis 2.About Twitter and Our Goal 3.Glossary 4.Challenges 5.Approach 6.Results and Conclusion 7.Tools and Technologies
- 3. What is Sentiment Analysis? Mechanism to extract opinions, emotions and sentiments in text Enable us to track attitudes and feelings on the web based on blog posts, comments, reviews and tweets on different topics Enable to track products, brands and people and determine whether they are viewed positively or negatively on the web. acts: "The painting was more expensive than a Monet" pinions: "I honestly don't like Monet, Pollock is the better” artist"
- 4. Challenges • Tweets are highly unstructured and also non- grammatical • Out of Vocabulary Words • Lexical Variation • Extensive usage of acronyms like asap, lol, afaik
- 5. Our System
- 6. • Tweet Downloader – Download the tweets using Twitter API • Tokenisation – Twitter specific POS Tagger developed by ARK Social Media Search • Preprocessing – Replacing Emoticons by their polarity, assign scores – Remove URL, Target Mentions – Replace #text -> text, since hashtags may contribute to the sentiment – Replace Sequence of Repeated Characters eg. ‘cooooool’ by ‘cool’ and assign higher score – Twitter specific stop word removal – Acronym expansion System Details
- 7. • Feature Extractor – Unigrams and Bigrams – Polarity Score of the Tweet (f1) – Count of Positive/Negative Words (f2,f3) – Maximum Positive/Negative Score for Words (f4,f5) – Count of Positive/Negative Emoticons and assign scores(contibutes to all f1,f2,f3,f4,f5) – Positive/Negative special POS Tags Polarity Score • Classifier and Prediction – Features extracted are fed into to SVM classifier – Model built used to predict sentiment of new tweets System Details Contd.
- 8. Results and Conclusion A baseline model by taking the unigrams, and compare it with the bigrams and lexicon features model Sub-Task Baseline Model Feature Based Model Sentence Based 49.81% 57.85% Accuracy F1 Score (f-Measure) Sub-Task Baseline Model Feature Based Model Sentence Based 55.56 61.17 • We investigated two kinds of models: Baseline and Feature Based Models • For our feature-based approach, feature analysis reveals that the most important features are bigrams and those that combine the prior polarity of words and their parts- of-speech tags
- 9. Thank You