Trend detection and analysis on TwitterLukas Masuch
By Henning Muszynski, Benjamin Räthlein & Lukas Masuch
The popularity of social media services has increased exponentially in the last few years. The combination of big social data and powerful analytical technologies makes it possible to gain highly valuable insights that otherwise might not be accessible. The Twitter Analyzer comprises several components to collect, analyze and visualize Twitter data. Therefore, we explored various related technologies to implement this tool. We collected about 38 million english tweets related to various and analyzed those data with machine learning techniques to compute the respective sentiment and detect common topics. Furthermore, we visualized the results using varying visualization techniques to emphasize different aspects such as a wordcloud, several chart-types and geospatial visualizations. Used technologies: MongoDB, Python, Twython, Python NLTK, wordcloud2.js, wordfreq, amCharts, Google BigQuery, Google Cloud Storage, CartoDB, EtcML.
This is small twitter sentiment analysis project which will take one keyword(which is the primary way of storing the tweet in Twitter) and number of tweets, and gives you the pictorial representation of the overall sentiment.
Sentiment analysis over Twitter offers organisations and individuals a fast and effective way to monitor the publics' feelings towards them and their competitors. To assess the performance of sentiment analysis methods over Twitter a small set of evaluation datasets have been released in the last few years. In this paper we present an overview of eight publicly available and manually annotated evaluation datasets for Twitter sentiment analysis. Based on this review, we show that a common limitation of most of these datasets, when assessing sentiment analysis at target (entity) level, is the lack of distinctive sentiment annotations among the tweets and the entities contained in them. For example, the tweet ``I love iPhone, but I hate iPad'' can be annotated with a mixed sentiment label, but the entity iPhone within this tweet should be annotated with a positive sentiment label. Aiming to overcome this limitation, and to complement current evaluation datasets, we present STS-Gold, a new evaluation dataset where tweets and targets (entities) are annotated individually and therefore may present different sentiment labels. This paper also provides a comparative study of the various datasets along several dimensions including: total number of tweets, vocabulary size and sparsity. We also investigate the pair-wise correlation among these dimensions as well as their correlations to the sentiment classification performance on different datasets.
Project Report for Twitter Sentiment Analysis done using Apache Flume and data is analysed using Hive.
I intend to address the following questions:
How raw tweets can be used to find audience’s perception or sentiment about a person ?
How Hadoop can be used to solve this problem?
How Apache Hive can be used to organize the final data in a tabular format and query it?
How a data visualization tool can be used to display the findings?
Trend detection and analysis on TwitterLukas Masuch
By Henning Muszynski, Benjamin Räthlein & Lukas Masuch
The popularity of social media services has increased exponentially in the last few years. The combination of big social data and powerful analytical technologies makes it possible to gain highly valuable insights that otherwise might not be accessible. The Twitter Analyzer comprises several components to collect, analyze and visualize Twitter data. Therefore, we explored various related technologies to implement this tool. We collected about 38 million english tweets related to various and analyzed those data with machine learning techniques to compute the respective sentiment and detect common topics. Furthermore, we visualized the results using varying visualization techniques to emphasize different aspects such as a wordcloud, several chart-types and geospatial visualizations. Used technologies: MongoDB, Python, Twython, Python NLTK, wordcloud2.js, wordfreq, amCharts, Google BigQuery, Google Cloud Storage, CartoDB, EtcML.
This is small twitter sentiment analysis project which will take one keyword(which is the primary way of storing the tweet in Twitter) and number of tweets, and gives you the pictorial representation of the overall sentiment.
Sentiment analysis over Twitter offers organisations and individuals a fast and effective way to monitor the publics' feelings towards them and their competitors. To assess the performance of sentiment analysis methods over Twitter a small set of evaluation datasets have been released in the last few years. In this paper we present an overview of eight publicly available and manually annotated evaluation datasets for Twitter sentiment analysis. Based on this review, we show that a common limitation of most of these datasets, when assessing sentiment analysis at target (entity) level, is the lack of distinctive sentiment annotations among the tweets and the entities contained in them. For example, the tweet ``I love iPhone, but I hate iPad'' can be annotated with a mixed sentiment label, but the entity iPhone within this tweet should be annotated with a positive sentiment label. Aiming to overcome this limitation, and to complement current evaluation datasets, we present STS-Gold, a new evaluation dataset where tweets and targets (entities) are annotated individually and therefore may present different sentiment labels. This paper also provides a comparative study of the various datasets along several dimensions including: total number of tweets, vocabulary size and sparsity. We also investigate the pair-wise correlation among these dimensions as well as their correlations to the sentiment classification performance on different datasets.
Project Report for Twitter Sentiment Analysis done using Apache Flume and data is analysed using Hive.
I intend to address the following questions:
How raw tweets can be used to find audience’s perception or sentiment about a person ?
How Hadoop can be used to solve this problem?
How Apache Hive can be used to organize the final data in a tabular format and query it?
How a data visualization tool can be used to display the findings?
Sentiment analysis of Twitter data using pythonHetu Bhavsar
Twitter is a popular social networking website where users posts and interact with messages known as “tweets”. To automate the analysis of such data, the area of Sentiment Analysis has emerged. It aims at identifying opinionative data in the Web and classifying them according to their polarity, i.e., whether they carry a positive or negative connotation. We will attempt to conduct sentiment analysis on “tweets” using various different machine learning algorithms.
Sentiment mining- The Design and Implementation of an Internet PublicOpinion...Prateek Singh
Sentiment mining paper presentation, database mining and business intelligence.
The Design and Implementation of an Internet PublicOpinion Monitoring and Analysing System
Twitter Sentiment Analysis Project Done using R.
In these Project we deal with the tweets database that are avaialble to us by the Twitter. We clean the tweets and break them out into tokens and than analysis each word using Bag of Word concept and than rate each word on the basis of the score wheter it is positive, negative and neutral.
We used Naive Baye's Classifier as our base.
I created this presentation to present my research work to the committee. My research was on extracting tweets and analyzing it with an previously created ontology model. The results of the ontology model will help in identifying the domain area of the problem for which use had shared negative sentiments on tweeter. This system along with the ontology model developed for Postal service domain. The next step in research will be to generate automated responses on twitter to the user who shares negative sentiments.
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Sentiment analysis in twitter using python
The big data phenomenon has confirmed the achievement of data access transformation. Sentiment analysis (SA) is one of the most exploited area and used for profit-making purpose through business intelligence applications. This paper reviews the trends in SA and relates the growth in the area with the big data era.
SentiTweet is a sentiment analysis tool for identifying the sentiment of the tweets as positive, negative and neutral.SentiTweet comes to rescue to find the sentiment of a single tweet or a set of tweets. Not only that it also enables you to find out the sentiment of the entire tweet or specific phrases of the tweet.
Twitter analytics: some thoughts on sampling, tools, data, ethics and user re...Farida Vis
Keynote delivered at the SRA Social Media in Social Research conference, London, 24 June, 2013. The presentation highlights some thoughts on sampling, tools, data, ethics and user requirements for Twitter analytics, including an overview of a series of recent tools.
Sentiment analysis of Twitter data using pythonHetu Bhavsar
Twitter is a popular social networking website where users posts and interact with messages known as “tweets”. To automate the analysis of such data, the area of Sentiment Analysis has emerged. It aims at identifying opinionative data in the Web and classifying them according to their polarity, i.e., whether they carry a positive or negative connotation. We will attempt to conduct sentiment analysis on “tweets” using various different machine learning algorithms.
Sentiment mining- The Design and Implementation of an Internet PublicOpinion...Prateek Singh
Sentiment mining paper presentation, database mining and business intelligence.
The Design and Implementation of an Internet PublicOpinion Monitoring and Analysing System
Twitter Sentiment Analysis Project Done using R.
In these Project we deal with the tweets database that are avaialble to us by the Twitter. We clean the tweets and break them out into tokens and than analysis each word using Bag of Word concept and than rate each word on the basis of the score wheter it is positive, negative and neutral.
We used Naive Baye's Classifier as our base.
I created this presentation to present my research work to the committee. My research was on extracting tweets and analyzing it with an previously created ontology model. The results of the ontology model will help in identifying the domain area of the problem for which use had shared negative sentiments on tweeter. This system along with the ontology model developed for Postal service domain. The next step in research will be to generate automated responses on twitter to the user who shares negative sentiments.
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Sentiment analysis in twitter using python
The big data phenomenon has confirmed the achievement of data access transformation. Sentiment analysis (SA) is one of the most exploited area and used for profit-making purpose through business intelligence applications. This paper reviews the trends in SA and relates the growth in the area with the big data era.
SentiTweet is a sentiment analysis tool for identifying the sentiment of the tweets as positive, negative and neutral.SentiTweet comes to rescue to find the sentiment of a single tweet or a set of tweets. Not only that it also enables you to find out the sentiment of the entire tweet or specific phrases of the tweet.
Twitter analytics: some thoughts on sampling, tools, data, ethics and user re...Farida Vis
Keynote delivered at the SRA Social Media in Social Research conference, London, 24 June, 2013. The presentation highlights some thoughts on sampling, tools, data, ethics and user requirements for Twitter analytics, including an overview of a series of recent tools.
Big Data and the Social Sciences
Ex-Google engineer Abe Usher presents a talk about Big Data technology and methods applicable to social science.
Participants will learn techniques that are used by Google engineers to collect, clean, analyze, and visualize Big Data.
Additionally Mr. Usher will provide URLs to sample data, open source applications, and code to those interested in applying these Big Data methods themselves.
Big Data Analytics and Open Data : The presentation aim is to enhance the awareness about big data analytics by process and importance of open data. Two case studies overview with accuracy and introduction is presented by Sharjeel Imtiaz.
PhD from University of East London
Lecture given at the University of Catania on December 2nd, 2014.
Start from Big Data definitions, continue with real life examples of successful Big Data Projects, go a little bit deeper with Sentiment Analysis, and conclude with a brief overview of Big Data tools and Big Data with Microsoft.
Summary:
1. What is Big Data? (includes the 5Vs of Big Data)
2. Big Data Examples (includes 6 Real Life Examples and comments on Privacy concerns)
3. How to Tackle a Big Data Problem (my 4 Universal Steps to follow)
4. Sentiment Analysis (what is sentiment analysis? Why do we care? A Technique and a plan)
5. Big Data tools (Hadoop, Hadoop Ecosystem, Hive, Pig, Sqoop, Oozie; Azure HDInsight, Excel Power Query, Power Pivot, Power View, Power Map)
Data-Driven Threat Intelligence: Metrics on Indicator Dissemination and SharingAlex Pinto
For the past 18 months, Niddel has been collecting threat intelligence indicator data from multiple sources in order to make sense of the ecosystem and try to find a measure of efficiency or quality in these feeds. This initiative culminated in the creation of Combine and TIQ-test, two of the open source projects from MLSec Project. These projects have been improved upon for the last year and are able to gather and compare data from multiple Threat Intelligence sources on the Internet.
We take this analysis a step further and extract insights form more than 12 months of collected threat intel data to verify the overlap and uniqueness of those sources. If we are able to find enough overlap, there could be a strategy that could put together to acquire an optimal number of feeds, but as Niddel demonstrated on the 2015 Verizon DBIR, that is not the case.
We also gathered aggregated usage information from intelligence sharing communities in order to determine if the added interest and "push" towards sharing is really being followed by the companies and if its adoption is putting us in the right track to close these gaps.
Join us in an data-driven analysis of over an year of collected Threat Intelligence indicators and their sharing communities!
Online social media services like Facebook witness an exponential increase in user activity when an event takes place in the real
world. This activity is a combination of good quality content like information, personal views, opinions, comments, as well as
poor quality content like rumors, spam, and other malicious content. Although, the good quality content makes online social
media a rich source of information, consumption of poor quality content can degrade user experience, and have inappropriate
impact in the real world. In addition, the enormous popularity, promptness, and reach of online social media services across
the world makes it essential to monitor this activity, and minimize the production and spread of poor quality content. Multiple
studies in the past have analyzed the content spread on social networks during real world events. However, little work has
explored the Facebook social network. Two of the main reasons for the lack of studies on Facebook are the strict privacy
settings, and limited amount of data available from Facebook, as compared to Twitter. With over 1 billion monthly active
users, Facebook is about five times bigger than its next biggest counterpart Twitter, and is currently, the largest online social
network in the world. In this literature survey, we review the existing research work done on Facebook, and study the techniques
used to identify and analyze poor quality content on Facebook, and other social networks. We also attempt to understand the
limitations posed by Facebook in terms of availability of data for collection, and analysis, and try to understand if existing
techniques can be used to identify and study poor quality content on Facebook.
Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...Alexandre Sieira
Sessão apresentada no Mind The Sec no dia 26 de Agosto de 2015.
Esta sessão vai fazer uma exploração tecnológica bem-humorada de feeds de threat intelligence abertos e comerciais que têm sido tratados pelo mercado de segurança como a nova panacéia para resolver os desafios de monitoramento e resposta a incidentes. Mesmo que nem todo o mercado de threat intelligence possa ser reduzido a feeds de indicadores, eles têm atraído atenção suficiente do mercado para merecer uma análise científica e factual para que os tomadores de decisão possam maximizar os resultados obtidos com os dados disponíveis.
Nos últimos 18 meses, a Niddel vem coletando indicadores de threat intelligence de múltiplas fontes, visando entender o ecossistema e desenvolver métricas de eficiência e qualidade para avaliar os diferentes feeds. Serão apresentadas análises factuais e baseadas em dados do viés estatístico, sobreposição, representatividade, idade de indicadores e unicidade entre diferentes feeds. Todos os dados utilizados será publicado e o código para geração dos gráficos está disponível em projetos open source chamados Combine e TIQ-Test. Estas são as mesmas técnicas e análises por trás da contribuição da Niddel no Verizon Data Breach Incident Report (DBIR) de 2015, um dos mais respeitados relatórios de segurança da informação do mundo.
Esta apresentação também irá apresentar dados agregados de uso de comunidades de compartilhamento de threat intelligence, de forma a identificar os padrões reais de uso e as preocupações e benefícios que os gestores podem esperar deste tipo de iniciativa.
Online text data for machine learning, data science, and research - Who can p...Fredrik Olsson
This slide deck concerns online text data for machine learning, artificial intelligence, data science, and scientific research. After this talk, you’ll know who can provide online text data, what types of data are hard to get, and principal data hygiene factors.
Updated in August 2019.
The CIPR's Artificial Intelligence (AI) panel has published new research revealing the impact of technology, and specifically AI, on public relations practice. It predicts the impact on skills in the profession in the next five years.
Open Source Insight: Drupageddon, Heartbleed Problems & Open Source 360 Surve...Black Duck by Synopsys
Open source insight this week on CVE-2014-3704, aka “Drupageddon” and CVE-2014-0160, the everlasting Heartbleed, plus results of our Open Source 360 Survey.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
1. Is a Twitter user’s location
correlated with their opinion on
#COVID-19?
Rong-Ching Chang
Chun-Ming Lai, Assist Prof
Information Security Lab X Social Computing And Information Security Lab (SCIS)
Tunghai University
Twitter@AnnCC12
2. Agenda
• General introduction of How you get data from Twitter
• Data, Data Pre-processing
• Mythology
• What top 5 countries of English users tweets about during Feb to May
• How is it different from getting data from Facebook?
3. How do you get data from Twitter
Twitter API Hydration Web Scrapping Tools
5. Open data & Covid & Hydration
Citation Dataset from: Umair Qazi, Muhammad
Imran, Ferda Ofli. GeoCoV19: A Dataset of
Hundreds of Millions of Multilingual COVID-19
Tweets with Location Information. ACM
SIGSPATIAL Special, May 2020. doi:
https://doi.org/10.1145/3404820.3404823
6. Basic Data Pre-processing
A -> a
@ mail
# hashtags
https://
@mention
Dooing -> doing
Doing - > do
:$%#@
n, nan
stopwords
Filter lang
Filter geography
Remove top 20,
tail 20 words
Tokenization
Word frequency
WorldCloud, Trigram
8. Word Cloud
Word Cloud
Word
Frequency
Categorization Mixed
A word cloud is a kind
of weighted list to
visualize language or
text data
the size of font
indicates
the number of
subcategories of a
collection.
the size of font
represents the
number of keywords
that appears in the
collection.
Cite:Yuping Jin, “Development of Word Cloud Generator Software Based on Python”, GCMM 2016
14. Top 5 Countries (ISO 3166-1 alpha-2)
• 'us’ United States of America
• ‘in’ India
• ‘gb’ United Kingdom
• ‘cn’ China
• 'au’ Australia
• Date
• 2/1
• 3/16
• 3/25
• 4/25
31. Thank you
Rong-Ching Chang
Chun-Ming Lai, Assist Prof
Information Security Lab X Social Computing And Information Security Lab
(SCIS)
Tunghai University
Twitter@AnnCC12
Editor's Notes
解釋為什麼選四個點
把confirm cases
Mis spelling
Lemmatization
Optional common word removal, you can also add stopwords or additional words
In the categorization type, the size of font indicates the number of subcategories of a collection.
For the capture of stylometric features, they based their approach on trigrams, arguing that trigrams capture stylometric features well and are more extensible to unknown text when using a small training set, comparing to a bag of words approach.
Daniel Ricardo Jaimes Moreno. Et al. “Prediction of Personality Traits in Twitter Users with Latent Features “, IEEE, 2019
For the capture of stylometric features, they based their approach on trigrams, arguing that trigrams capture stylometric features well and are more extensible to unknown text when using a small training set, comparing to a bag of words approach.
Daniel Ricardo Jaimes Moreno. Et al. “Prediction of Personality Traits in Twitter Users with Latent Features “, IEEE, 2019
The polarity score is a float within the range [-1.0, 1.0]
The sentiment polarity can be determined as positive, negative and neutral.
Subjective 主觀
Objective 客觀
The subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.
'us','in','gb','cn','au’
解釋為什麼選四個點
把confirm cases
'us','in','gb','cn','au'
In the end of March, you can clearly see the positive sentiment
In the end of April 25, covid is clearly being tweeted with some political messages
放number of tweets