A Hybrid Approach for Supervised Twitter Sentiment Classification ....................................................1
K. Revathy and Dr. B. Sathiyabhama
A Survey of Dynamic Duty Cycle Scheduling Scheme at Media Access Control Layer for Energy
Conservation .....................................................................................................................................1
Prof. M. V. Nimbalkar and Sampada Khandare
A Survey on Privacy Preserving Data Mining Techniques ....................................................................1
A. K. Ilavarasi, B. Sathiyabhama and S. Poorani
An Ontology Based System for Predicting Disease using SWRL Rules ...................................................1
Mythili Thirugnanam, Tamizharasi Thirugnanam and R. Mangayarkarasi
Performance Evaluation of Web Services in C#, JAVA, and PHP ..........................................................1
Dr. S. Sagayaraj and M. Santhosh Kumar
Semi-Automated Polyhouse Cultivation Using LabVIEW......................................................................1
Prathiba Jonnala and Sivaji Satrasupalli
Performance of Biometric Palm Print Personal Identification Security System Using Ordinal Measures 1
V. K. Narendira Kumar and Dr. B. Srinivasan
MIMO System for Next Generation Wireless Communication..............................................................1
Sharif, Mohammad Emdadul Haq and Md. Arif Rana
Current trends of opinion mining and sentiment analysis in social networkseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A Hybrid Approach for Supervised Twitter Sentiment Classification ....................................................1
K. Revathy and Dr. B. Sathiyabhama
A Survey of Dynamic Duty Cycle Scheduling Scheme at Media Access Control Layer for Energy
Conservation .....................................................................................................................................1
Prof. M. V. Nimbalkar and Sampada Khandare
A Survey on Privacy Preserving Data Mining Techniques ....................................................................1
A. K. Ilavarasi, B. Sathiyabhama and S. Poorani
An Ontology Based System for Predicting Disease using SWRL Rules ...................................................1
Mythili Thirugnanam, Tamizharasi Thirugnanam and R. Mangayarkarasi
Performance Evaluation of Web Services in C#, JAVA, and PHP ..........................................................1
Dr. S. Sagayaraj and M. Santhosh Kumar
Semi-Automated Polyhouse Cultivation Using LabVIEW......................................................................1
Prathiba Jonnala and Sivaji Satrasupalli
Performance of Biometric Palm Print Personal Identification Security System Using Ordinal Measures 1
V. K. Narendira Kumar and Dr. B. Srinivasan
MIMO System for Next Generation Wireless Communication..............................................................1
Sharif, Mohammad Emdadul Haq and Md. Arif Rana
Current trends of opinion mining and sentiment analysis in social networkseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Detection and Analysis of Twitter Trending Topics via Link-Anomaly DetectionIJERA Editor
This paper involves two approaches for finding the trending topics in social networks that is key-based approach and link-based approach. In conventional key-based approach for topics detection have mainly focus on frequencies of (textual) words. We propose a link-based approach which focuses on posts reflected in the mentioning behavior of hundreds users. The anomaly detection in the twitter data set is carried out by retrieving the trend topics from the twitter in a sequential manner by using some API and corresponding user for training, then computed anomaly score is aggregated from different users. Further the aggregated anomaly score will be feed into change-point analysis or burst detection at the pinpoint, in order to detect the emerging topics. We have used the real time twitter account, so results are vary according to the tweet trends made. The experiment shows that proposed link-based approach performs even better than the keyword-based approach.
The size of the Internet enlarging as per to grow the users of search providers continually demand search
results that are accurate to their wishes. Personalized Search is one of the options available to users in
order to sculpt search results based on their personal data returned to them provided to the search
provider. This brings up fears of privacy issues however, as users are typically anxious to revealing
personal info to an often faceless service provider along the Internet. This work proposes to administer
with the privacy issues surrounding personalized search and discusses ways that privacy can be improved
so that users can get easier with the dismissal of their personal information in order to obtain more precise
search results.
VIDEO OBJECTS DESCRIPTION IN HINDI TEXT LANGUAGE ijmpict
Video activity recognition has grown to be a dynamic location of analysis in latest years. A widespread
information-driven approach is denoted in this paper that produces descriptions of video content into
textual content description inside the Hindi language. This method combines the final results of modern
item with "real-international" records to pick the in all subject-verb-object triplet for depicting a video. The
usage of this triplet desire technique, a video is tagged via the trainer, mainly, Subject, Verb, and object
(SVO) and then this data is mined to improve the result of checking out video clarification by using pastime
as well as item identity. Contrasting preceding approaches, this method can annotate arbitrary videos
deprived of wanting the large series and annotation of a similar schooling video corpus. The proposed
work affords initial and primary text description within the Hindi language that is producing easy words
and sentence formation. But the fundamental challenging attempt on this work is to extract grammatically
accurate and expressive text records in Hindi textual content regarding video content.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
Scalable recommendation with social contextual informationeSAT Journals
Abstract Recommender systems are used to achieve effective and useful results in a social networks. The social recommendation will provide a social network structure but it is challenging to fuse social contextual factors which are derived from user’s motivation of social behaviors into social recommendation. Here, we introduce two contextual factors in recommender systems which are used to adopt a useful results namely a) individual preference and b) interpersonal influence. Individual preference analyze the social interests of an item content with user’s interest and adopt only users recommended results. Interpersonal influence is analyzing user-user interaction and their specific social relations. Beyond this, we propose a novel probabilistic matrix factorization method to fuse them in a latent space. The scalable algorithm provides a useful results by analyzing the ranking probability of each user social contextual information and also incrementally process the contextual data in large datasets. Keywords: social recommendation, individual preference, interpersonal influence, matrix factorization
KnowMe and ShareMe: Understanding Automatically Discovered Personality Trai...Wookjae Maeng
There is much recent work on using the digital footprints left by people on social media to predict personal traits and gain a deeper understanding of individuals. Due to the veracity of social media, imperfections in prediction algorithms, and the sensitive nature of one’s personal traits, much research is still needed to better understand the effectiveness of this line of work, including users’ preferences of sharing their com- putationally derived traits. In this paper, we report a two- part study involving 256 participants, which (1) examines the feasibility and effectiveness of automatically deriving three types of personality traits from Twitter, including Big 5 per- sonality, basic human values, and fundamental needs, and (2) investigates users’ opinions of using and sharing these traits. Our findings show there is a potential feasibility of automati- cally deriving one’s personality traits from social media with various factors impacting the accuracy of models. The re- sults also indicate over 61.5% users are willing to share their derived traits in the workplace and that a number of factors significantly influence their sharing preferences. Since our findings demonstrate the feasibility of automatically infer- ring a user’s personal traits from social media, we discuss their implications for designing a new generation of privacy- preserving, hyper-personalized systems.
Identifying ghost users using social media metadata - University College LondonGreg Kawere
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information a joint research project of the Alan Turing Institute and University College in London
Big five personality prediction based in Indonesian tweets using machine lea...IJECEIAES
The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the Big Five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.
There are various online networking sites such as Facebook, twitter where students casually discuss their educational
experiences, their opinions, emotions, and concerns about the learning process. Information from such open environment can
give valuable knowledge for opinions, emotions and help the educational organizations to get insight into students’ educational
life. Analysing down such data, on the other hand, can be challenging therefore a qualitative research and significant data
mining process needs to be done. Sentiment classification can be done using NLP (Natural Language Processing). For a social
network that provides micro blogging services such as twitter, the incoming tweets can be classified into News, Opinions,
Events, Deals and private Messages based on authors information available in the tweets. This approach is similar to
Tweetstand, which classifies the tweets into news and non-news. Even for e-commerce applications virtual customer
environments can be created using social networking sites. Since the data is ever growing, using data mining techniques can get
difficult, hence we can use data analysis tools
Detection and Analysis of Twitter Trending Topics via Link-Anomaly DetectionIJERA Editor
This paper involves two approaches for finding the trending topics in social networks that is key-based approach and link-based approach. In conventional key-based approach for topics detection have mainly focus on frequencies of (textual) words. We propose a link-based approach which focuses on posts reflected in the mentioning behavior of hundreds users. The anomaly detection in the twitter data set is carried out by retrieving the trend topics from the twitter in a sequential manner by using some API and corresponding user for training, then computed anomaly score is aggregated from different users. Further the aggregated anomaly score will be feed into change-point analysis or burst detection at the pinpoint, in order to detect the emerging topics. We have used the real time twitter account, so results are vary according to the tweet trends made. The experiment shows that proposed link-based approach performs even better than the keyword-based approach.
The size of the Internet enlarging as per to grow the users of search providers continually demand search
results that are accurate to their wishes. Personalized Search is one of the options available to users in
order to sculpt search results based on their personal data returned to them provided to the search
provider. This brings up fears of privacy issues however, as users are typically anxious to revealing
personal info to an often faceless service provider along the Internet. This work proposes to administer
with the privacy issues surrounding personalized search and discusses ways that privacy can be improved
so that users can get easier with the dismissal of their personal information in order to obtain more precise
search results.
VIDEO OBJECTS DESCRIPTION IN HINDI TEXT LANGUAGE ijmpict
Video activity recognition has grown to be a dynamic location of analysis in latest years. A widespread
information-driven approach is denoted in this paper that produces descriptions of video content into
textual content description inside the Hindi language. This method combines the final results of modern
item with "real-international" records to pick the in all subject-verb-object triplet for depicting a video. The
usage of this triplet desire technique, a video is tagged via the trainer, mainly, Subject, Verb, and object
(SVO) and then this data is mined to improve the result of checking out video clarification by using pastime
as well as item identity. Contrasting preceding approaches, this method can annotate arbitrary videos
deprived of wanting the large series and annotation of a similar schooling video corpus. The proposed
work affords initial and primary text description within the Hindi language that is producing easy words
and sentence formation. But the fundamental challenging attempt on this work is to extract grammatically
accurate and expressive text records in Hindi textual content regarding video content.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
Scalable recommendation with social contextual informationeSAT Journals
Abstract Recommender systems are used to achieve effective and useful results in a social networks. The social recommendation will provide a social network structure but it is challenging to fuse social contextual factors which are derived from user’s motivation of social behaviors into social recommendation. Here, we introduce two contextual factors in recommender systems which are used to adopt a useful results namely a) individual preference and b) interpersonal influence. Individual preference analyze the social interests of an item content with user’s interest and adopt only users recommended results. Interpersonal influence is analyzing user-user interaction and their specific social relations. Beyond this, we propose a novel probabilistic matrix factorization method to fuse them in a latent space. The scalable algorithm provides a useful results by analyzing the ranking probability of each user social contextual information and also incrementally process the contextual data in large datasets. Keywords: social recommendation, individual preference, interpersonal influence, matrix factorization
KnowMe and ShareMe: Understanding Automatically Discovered Personality Trai...Wookjae Maeng
There is much recent work on using the digital footprints left by people on social media to predict personal traits and gain a deeper understanding of individuals. Due to the veracity of social media, imperfections in prediction algorithms, and the sensitive nature of one’s personal traits, much research is still needed to better understand the effectiveness of this line of work, including users’ preferences of sharing their com- putationally derived traits. In this paper, we report a two- part study involving 256 participants, which (1) examines the feasibility and effectiveness of automatically deriving three types of personality traits from Twitter, including Big 5 per- sonality, basic human values, and fundamental needs, and (2) investigates users’ opinions of using and sharing these traits. Our findings show there is a potential feasibility of automati- cally deriving one’s personality traits from social media with various factors impacting the accuracy of models. The re- sults also indicate over 61.5% users are willing to share their derived traits in the workplace and that a number of factors significantly influence their sharing preferences. Since our findings demonstrate the feasibility of automatically infer- ring a user’s personal traits from social media, we discuss their implications for designing a new generation of privacy- preserving, hyper-personalized systems.
Identifying ghost users using social media metadata - University College LondonGreg Kawere
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information a joint research project of the Alan Turing Institute and University College in London
Big five personality prediction based in Indonesian tweets using machine lea...IJECEIAES
The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the Big Five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.
There are various online networking sites such as Facebook, twitter where students casually discuss their educational
experiences, their opinions, emotions, and concerns about the learning process. Information from such open environment can
give valuable knowledge for opinions, emotions and help the educational organizations to get insight into students’ educational
life. Analysing down such data, on the other hand, can be challenging therefore a qualitative research and significant data
mining process needs to be done. Sentiment classification can be done using NLP (Natural Language Processing). For a social
network that provides micro blogging services such as twitter, the incoming tweets can be classified into News, Opinions,
Events, Deals and private Messages based on authors information available in the tweets. This approach is similar to
Tweetstand, which classifies the tweets into news and non-news. Even for e-commerce applications virtual customer
environments can be created using social networking sites. Since the data is ever growing, using data mining techniques can get
difficult, hence we can use data analysis tools
Microblogging today has gotten an acclaimed specific instrument among Internet clients. Endless clients share assessments on various bits of life dependably. Accordingly, microblogging districts are rich wellsprings of information for assessment mining and tendency assessment. Since microblogging has shown up by and large lately, there several investigation works that were given to this point. In our paper, we base on using Twitter, the most notable microblogging stage, for the task of feeling examination. We advise the most ideal approach to thus accumulate a corpus for assessment and evaluation mining purposes. We play out a semantic assessment of the amassed corpus and clarify found wonders. Utilizing the corpus, we build up an end classifier, that can pick positive, negative, and honest evaluations for an annual. Test assessments show that our proposed strategies are convincing and act in a way that is better than actually proposed procedures. In our appraisal, we worked with English, in any case, the proposed procedure can be utilized with some other language. Krunal Dhardev | Dr. Kamalraj R "Twitter Sentiment Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42385.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42385/twitter-sentiment-analysis/krunal-dhardev
Insights to Problems, Research Trend and Progress in Techniques of Sentiment ...IJECEIAES
The research-based implementations towards Sentiment analyses are about a decade old and have introduced many significant algorithms, techniques, and framework towards enhancing its performance. The applicability of sentiment analysis towards business and the political survey is quite immense. However, we strongly feel that existing progress in research towards Sentiment Analysis is not at par with the demand of massively increasing dynamic data over the pervasive environment. The degree of problems associated with opinion mining over such forms of data has been less addressed, and still, it leaves the certain major scope of research. This paper will brief about existing research trends, some important research implementation in recent times, and exploring some major open issues about sentiment analysis. We believe that this manuscript will give a progress report with the snapshot of effectiveness borne by the research techniques towards sentiment analysis to further assist the upcoming researcher to identify and pave their research work in a perfect direction towards considering research gap.
Applying Clustering Techniques for Efficient Text Mining in Twitter Dataijbuiiir1
Knowledge is the ultimate output of decisions on a dataset. The revolution of the Internet has made the global distance closer with the touch on the hand held electronic devices. Usage of social media sites have increased in the past decades. One of the most popular social media micro blog is Twitter. Twitter has millions of users in the world. In this paper the analysis of Twitter data is performed through the text contained in hash tags. After Preprocessing clustering algorithms are applied on text data. The different clusters formed are compared through various parameters. Visualization techniques are used to portray the results from which inferences like time series and topic flow can be easily made. The observed results show that the hierarchical clustering algorithm performs better than other algorithms.
A Baseline Based Deep Learning Approach of Live Tweetsijtsrd
In this scenario social media plays a vital role in influencing the life of people. Twitter , Facebook, Instagram etc are the major social media platforms . They act as a platform for users to raise their opinions on things and events around them. Twitter is one such micro blogging site that allows the user to tweet 6000 tweets per day each of 280 characters long. Data analyst rely on this data to reach conclusion on the events happening around and also to rate a product. But due to massive volume of reviews the analysts find it difficult to go through them and reach at conclusions. In order to solve this problem we adopt the method of sentiment analysis. Sentiment analysis is an approach to classify the sentiment of user reviews, documents etc in terms of positive good , negative bad , neutral surprise . I suggest an enhanced twitter sentiment analysis that retrieves data based on a baseline in a particular pre defined time span and performs sentiment analysis using Textblob . This scheme differs from the traditional and existing one which performs sentiment analysis on pre saved data by performing sentiment analysis on real time data fetched via Twitter API . Thereby providing a much recent and relevant conclusion. Anjana Jimmington ""A Baseline Based Deep Learning Approach of Live Tweets"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23918.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/23918/a-baseline-based-deep-learning-approach-of-live-tweets/anjana-jimmington
Detailed Investigation of Text Classification and Clustering of Twitter Data ...ijtsrd
As of late there has been a growth in data. This paper presents a methodology to investigate the text classification of data gathered from twitter. In this study sentiment analysis has been done on online comment data giving us picture of how to discover the demands of a people. Ziya Fatima | Er. Vandana "Detailed Investigation of Text Classification and Clustering of Twitter Data for Business Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38527.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38527/detailed-investigation-of-text-classification-and-clustering-of-twitter-data-for-business-analytics/ziya-fatima
In the age of social media communication, it is easy to
modulate the minds of users and also instigate violent
actions being taken by them in some cases. There is a need
to have a system that can analyze the threat level of tweets
from influential users and rank their Twitter handles so
that dangerous tweets can be avoided going public on
Twitter before fact-checking which can hurt the sentiments
of people and can take the shape of violence. The study
aims to analyse and rank twitter users according to their
influential power and extremism of their tweets to help
prevent major protests and violent events. We scraped top
trending topics and fetched tweets using those hashtags.
We propose a custom ranking algorithm which considers
source based and content based features along with a
knowledge graph which generates the score and rank the
twitter users according to the scores. Our aim with this
study is to identify and rank extremist twitter users with
regards to their impact and influence. We use a technique
that takes into consideration both source based and
content-based features of tweets to generate the ranking of
the extremist twitter users having a high impact factor
A MODEL BASED ON SENTIMENTS ANALYSIS FOR STOCK EXCHANGE PREDICTION - CASE STU...csandit
Predicting the behavior of shares in the stock market is a complex problem, that involves variables not always known and can undergo various influences, from the collective emotion to high-profile news. Such volatility, can represent considerable financial losses for investors. In order to anticipate such changes in the market, it has been proposed various mechanisms to try to predict the behavior of an asset in the stock market, based on previously existing information.
Such mechanisms include statistical data only, without considering the collective feeling. This article, is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an attempt to predict the active behavior. Nevertheless it is important to note that such approach is not intended to be the main factor in the decision making process, but rather an aid tool, which combined with other information, can provide higher accuracy for the solution of this problem
A MODEL BASED ON SENTIMENTS ANALYSIS FOR STOCK EXCHANGE PREDICTION - CASE STU...cscpconf
Predicting the behavior of shares in the stock market is a complex problem, that involves variables not always known and can undergo various influences, from the collective emotion to the high-profile news. Such volatility can represent considerable financial losses for investors. In order to anticipate such changes in the market, it has been proposed various mechanisms to try to predict the behavior of an asset in the stock market, based on previously existing information. Such mechanisms include statistical data only, without considering the collective feeling. This article is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an
attempt to predict the active behavior. Nevertheless it is important to note that such approach is not intended to be the main factor in the decision making process, but rather an aid tool, which combined with other information, can provide higher accuracy for the solution of this problem.
Sentiment analysis using machine learning and deep LearningVenkat Projects
Sentiment analysis using machine learning and deep Learning
With the increasing rate at which data is created by internet users on various platforms, it becomes necessary to analyze and make use of the data by the Defense and other Government Organizations and know the sentiment of the people. This shall help the organizations take control of their actions and decide the steps to be taken shortly. Added to it, when something crucial is happening in the nation, it is of paramount importance to decide every step without hurting/violating the sentiments of the people. In the era of Microblogging, which has become quite a popular tool of communication, millions of users share their views and opinions on various day-to-day life issues concerning them directly or indirectly through social media platforms like Twitter, Reddit, Tumblr, Facebook. Data from these sites can be efficiently used for marketing or social studies. In this paper, we have taken into account various methods to perform sentiment analysis. Sentiment Analysis has been performed by using Machine Learning Classifiers. Polarity-based sentiment analysis, and Deep Learning Models are used to classify user's tweets as having `positive' or `negative' sentiment. The idea behind taking in various model architectures was to account for the variance in the opinions and thoughts existing on such social media platforms. These classification models can further be implemented to classify live tweets on twitter on any topic
With the rise of social networking epoch, there has been a surge of user generated content. Micro blogging sites have millions of people sharing their thoughts daily because of its characteristic short and simple manner of expression. We propose and investigate a paradigm to mine the sentiment from a popular real-time micro blogging service, Twitter, where users post real time reactions to and opinions about “everything”. In this paper, we expound a hybrid approach using both corpus based and dictionary based methods to determine the semantic orientation of the opinion words in tweets. A case study is presented to illustrate the use and effectiveness of the proposed system.
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
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
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However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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
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Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Free Complete Python - A step towards Data Science
E017433538
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. III (July – Aug. 2015), PP 35-38
www.iosrjournals.org
DOI: 10.9790/0661-17433538 www.iosrjournals.org 35 | Page
A review of the existing state of Personality prediction of Twitter
users with Machine Learning Algorithms
Kanupriya Sharma1
, Amanpreet Kaur2
1
(Computer Science Department, Chandigarh University, India)
2
(Computer Science Department, Chandigarh University, India)
Abstract: Twitter is a popular social media platform with millions of users. The tweets shared by these users
have recently attracted the attention of researchers from diverse fields. In this paper, we focus primarily on
predicting user’s personality from the analysis of tweets shared by the user. However, different techniques have
been used to predict a user’s personality from tweets but there are certain shortcomings which still need to be
addressed. The aim of this paper is to consider the current state of this research and explore the need of
predicting personality from tweets by crucially reviewing the literature done till date and provide an overview
of the different measures taken to alleviate the issues faced by researchers in this field.
Keywords: Classification, Machine Learning Algorithms, Personality Prediction, Supervised Learning, Twitter
I. Introduction
The advent of the internet into the world gave an endless variety of ways to its users to indulge and
savor themselves with the rich pool of knowledge and entertainment. The way social platforms have burgeoned
in the recent years has provided an opportunity to study and harvest enormous data which is being produced
rapidly at a continuous rate per second of time. Millions of users create profiles about themselves on social
media platforms such as Twitter and use the services to connect with their friends and relatives all around the
world. At Twitter, 288 million active users per month express themselves with short informal text messages
called tweets which amounts to 500 million tweets in a single day. [1] It can be easily figured the amount of data
generated by such volume of users at a daily basis. Thus, such factors have triggered research in opinion mining,
sentiment analysis and predicting various aspects of real world using social media. Since, knowing what the
world actually thinks provides solutions and insights in shaping the fate of economies, business ventures of
enterprises and debatable issues of the world. [2] Moreover, the likelihood of a movie to perform and even the
future of a commercial product to succeed in market have been quantified by predictive analysis of tweets. [3]
Sentiment analysis using social media has been establishing itself as an explicit field of study under Natural
Language Processing and it’s relation with psychological sciences has been said to bound firmly with insights
from the outcome of various researches done till date. [4] The relationship between personality and the factors
on which the personality of an individual affects such as job satisfaction, success in personal and professional
aspects of life is shown to be beneficial in making predictions using machine learning techniques for an
automated process of integrating different fields together. [5][6][7][8] However, the tweets are basically
informal and unstructured text therefore, they bring along certain constraints associated with text analysis.
Significant use of slang, emoticons, abbreviations and short term words pose a greater difficulty for
standardizing the entire process. All these issues have been widely faced and accepted with grace in research
work done till date. Not only these issues have held back in generalizing the entire process of personality
prediction but have also adulterated with the results and insights of different research attempts made to achieve
adequate efficiency. For instance, various approaches have been made to predict personality from social media
platforms using different machine learning techniques such as Zero R, Gaussian, Naïve Bayes and a few more.
These approaches have helped to push forward the research of predicting personality from tweets but still lack in
certain areas due to earlier mentioned constraints. [9][10][11][12] However, in the next sections we will discuss
in depth about the various approaches that have been followed for predicting personality from Twitter and their
different levels of efficiency as per the potential of resources used and the factors undertaken to perform
analysis on tweets text.
II. Anatomy Of The Research
The above section provides a general overview of the entire text however, the aim of this section is to
provide an elaborated view of the entire framework of personality prediction which has been divided into the
following subsections.
2. A review of the existing state of Personality prediction of Twitter users with Machine …..
DOI: 10.9790/0661-17433538 www.iosrjournals.org 36 | Page
1. What is personality prediction from social media platforms such as Twitter?
Predicting ones personality is not a recent venture and different approaches have existed so far already
in context to it. The famous Big Five Personality Inventory standardizes the personality of an individual into
five categories i.e. Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism. [100] The
authors of [101] conducted a research on 279 subjects by using the Big Five Inventory and by collecting
different statistics from their Facebook profiles and were able to construct a model which predicted personality
of the subjects to within 11% of their actual values. Later, the authors of [9] based on insights of [101]
conducted a research by collecting 2000 recent tweets of 279 subjects and then by linguistic analysis of the
tweets text they were able to correlate the results with Big Five inventory and achieved 11% to 18% of their
actual values. Both these researches laid the foundation of predicting personality traits from social media such as
Facebook and Twitter. The authors of [102] further extended the relationship between personality prediction and
Twitter by conducting a research with 142 participants which resulted into realization of new associations
between linguistic cues and personality.
2. Why personality prediction is a significant area of research and its scope of applications in the real
world?
There exists a vast array of real world implications that have been and can be made using insights from
personality prediction of the global audience that is available with Twitter. Previous researches have suggested
different areas which are directly affected with a user’s personality. These include marketing, user interface
designs and recommendation systems. Users can be viewed customized web pages, advertisements and products
which suits their personalities. These applications may as well be scaled to customized search engines and user
experience with different matrimonial and online dating websites. [9] [10] Also, in [11] authors have realized
darker traits of a user’s personality which can help in understanding criminal and psychopathic behaviors of
different personalities. The authors of [12] have suggested that personality prediction can also aid in
understanding collective behaviors which provides a qualitative view to social media text mining such as
sentiment analysis and clustering of text. All these applications fit well into the present world and hence make
personality prediction an interesting field to pursue.
3. How personality prediction of a Twitter user is done using machine learning?
The entire process of personality prediction of a Twitter user can be divided into following steps
starting data collection to creating a classification model using an appropriate machine learning algorithm.
Tweets can easily analyzed following the text mining and classification techniques as tweets are also mere
textual data.
Data Collection – Twitter provides an API (Application Programming Interface) which facilitates the users to
retrieve data, public info and user info. There are two API namely Search API and Stream API where the former
provides access to limited recent tweets while the latter provide access of real time messages flow. Twitter4J (a
java library) and certain Python scripts also provide easy access to data for tweets collection. After an adequate
amount of tweets are collected which usually is a time consuming process, tweets are transferred into a single
corpus of text.
Data Preprocessing – Tweets are unstructured, informal short messages and hence contain certain parts of text
which are not necessary for analysis. These parts are nothing but emoticons, slang, short abbreviations, stop
words and special characters. All these parts are removed from text after text tokenization. The remaining text is
further processed by either stemming, dimension reduction or a few more techniques. This part is also called as
feature extraction.
Model Training – If we consider supervised learning, the learning algorithm should be trained first with labeled
examples so as to learn classification for new test unlabeled examples.
Classification/Regression Model – The personality prediction of tweets can be performed using two methods. In
classification, the main aim is to classify the text based on certain set of rules into different labels or classes of
personalities while in regression, the main aim is to find a value or score of an individual for each of the
personality traits. Both these models can be used as per the nature of problem undertaken.
Model Evaluation – After the model has been built it needs to be evaluated using certain techniques such as
Accuracy, Recall, Precision, and Root Mean Square Error based upon the nature of problem undertaken.
The above subsection intends to provide a basic framework of steps required to perform personality
prediction using tweets. However, selection of machine learning algorithms, their evaluation metrics and textual
features selection and linguistic analysis varies from the choice of the researcher to nature of problem
undertaken. In the next section we will discuss about the different research literatures done in context with
personality prediction using tweets. The next section aims to provide a brief image of the approaches followed
by the researchers, different statistics that were used, shortcomings and future scope of their respective works.
3. A review of the existing state of Personality prediction of Twitter users with Machine …..
DOI: 10.9790/0661-17433538 www.iosrjournals.org 37 | Page
III. Literature Survey
J. Golbeck et al. [9] stated in their research that they were the first ones to look at the relationship
between personality traits and social profile statistics. They created a Twitter application through which they
undertook recent 2000 tweets of fifty subjects. The subjects were presented with the 45 question version of the
Big Five Personality Inventory. The collected tweets corpus was processed with the help of two tools, first
LIWC (Linguistic Inquiry and Word Count) from which they were able to extract a total of 79 features. Second,
MRC Database which yielded 14 language features. They also performed a word by word sentiment analysis
with the help of General Inquirer dataset. Then the authors ran a Pearson Correlation analysis between features
obtained and personality scores of each user. However, little weightage is given to correlation in this research
and is left open for analysis over larger datasets. The authors used regression analysis to predict the score of
specific personality features in WEKA. Two algorithms, Gaussian Process and ZeroR were used with 10 fold
cross validation iterated 10 times. The authors were able to predict scores from 11% to 18% of their actual
values. However, smaller sample size did affect the overall efficiency of the model as mentioned in the paper.
Although, authors were able to open a new pathway to predict personality of Twitter users and provide insights
about real world applications of their research in the field of marketing and interface design. The results are very
likely to improve if larger datasets are taken for consideration.
D. Quercia et al. [10] conducted a research on 335 subjects having both Twitter and Facebook profiles.
The data was gathered from a Facebook application called myPersonality and was mapped on the basis of the
fact that the same person had profiles on both the platforms. The relationship between personality and different
type of Twitters users was analyzed and the personality scores were predicted on the basis of input of three
counts namely, following (count if people the user is following), followers (count of people following the user)
and listed counts (number of times the user is listed). The authors performed regression analysis for each
personality trait with 10 fold cross validation iterated 10 times using M5’ rules. The authors also measured Root
Mean Square Error between predicted and observed values with a maximum of 0.88. The authors were able to
establish important personality relationships among different Twitter users and also provided an insight on
accurately predicting personality based on simple profile attributes. This laid a foundation for using this research
into good use at marketing, user interface designs and recommender systems. However, discussion were done
regarding the extent of revelations that users make on such public profiles. This provides an insight of
conducting research with verified users as can be seen now a days on Twitter so that exact predictions can be
made efficiently without a doubt of adulterations in the resources.
C. Sumner et al. [11] extended the research of personality prediction beyond the Big Five Personality
traits to the anti-social traits of narcissism, Machiavellianism and psychopathy, collectively known as the Dark
Triads of personality. Language use and profile attributes were analyzed of 2927 Twitter users to predict dark
triads of their personalities. The authors stated that they were the first to study the relationship between Twitter
use and dark triads of personality. The study was conducted using custom made Twitter application which
collected self-reported ratings on the Short Dark Triad. A maximum of 3200 tweets were collected and were
analyzed using LIWC which resulted into a selection of 337 features for machine prediction usage. A
comparative study of total six models was conducted by the authors namely, SVM using SMO and a polynomial
kernel, Random Forest, J48 algorithm, Naïve Bayes Classifier and two Kaggle models, standard benchmark
model and a competition winner model which was held by the authors respectively in context to predict
psychopathy and other seven traits together in two different competitions. As stated by the authors, predictive
models may not work well for predicting an individual’s personality but may work well for predicting the trend
of anti-social traits over a subset of population. However, the study resulted into new findings of strong
relationships between anti-social traits and language use. The study also showed certain limitations such as
selection bias of the subjects and the ever existing issues related to linguistic usage in social media. On the other
hand, this study provides endless ports of opportunities for researchers to refine personality prediction from
tweets and profile attributes. Rigorous work is required in linguistics used in social media and refinements on
individual level predictions is open for study to build robust models of individual’s personality prediction. The
study also puts forward a greater need of better evaluation metrics for prediction models.
Ana C.E.S Lim et al. [12] have proposed personality traits prediction in text groups and extended the
problem of personality prediction into a multi label classification problem. This is a novel approach as an
individual may possess more than one personality traits. The authors used the Naïve Bayes Algorithm to analyze
tweets and named their model as Bayesian Personality Predictor. Their approach was divided into three steps
namely, preprocessing, transformation and classification, where in preprocessing certain attributes were
extracted from the tweets and then in the second phase multi label sets were mapped into five single label
training sets. Finally, in the third phase semi supervised classification takes place with the help of these training
sets and meta-attributes as input to the classifier. The algorithm was evaluated with k-fold cross validation and
metrics Accuracy, Recall and Precision. Brazilian TV shows were used as a benchmarking for personality
4. A review of the existing state of Personality prediction of Twitter users with Machine …..
DOI: 10.9790/0661-17433538 www.iosrjournals.org 38 | Page
analysis tool. The authors stated to achieve an average of 84% accuracy with their approach. However, more
training sets are required to train classification models to achieve a high level of accuracy.
Research Paper Sample Size Algorithms Evaluation Metrics
J. Golbeck et al. 279 Gaussian Process,
ZeroR
11% to 18% of actual
values
D. Quercia et al. 335 M5’ Algorithm with 10
fold-cross validation
RMSE maximum 0.88
C. Sumner et al. 2927 SVM using SMO and a
polynomial kernel,
Random Forest, J48
algorithm, Naïve Bayes
Classifier
Top 10% of distribution
Ana C.E.S Lim et al. 30 groups of users Naïve Bayes Algorithm 84% average accuracy
across classifiers
Table: Literature Overview
IV. Research Gap
This section aims to provide certain areas which need refinements in their respective manner since
fewer approaches have been introduced to predict personality from Twitter. As the need to do so has been
already made lucid in these different literatures, but still there exist specific areas which can be pursued and
improved by finding new insights using an array of available resources such as
1. Tweets are nothing but textual data which can be analyzed for personality prediction based on linguistic
analysis hence different approaches can be followed to improve recognition of linguistic constraints such as
slang usage, communal bias, abbreviations and sentiment of tweets.
2. A rigorous research effort is required to make predictive models based on regression or classification
algorithms and evaluation methods must be robust enough to complement these approaches.
3. Most of the work done is limited to English language and hence involvement of different language experts
can open new pathways to make feature extraction and personality prediction language independent based
upon semantic, lexical and grammatical rules.
4. The predictive models must be scalable and dynamic enough to meet the requirements of ever growing data
and vast possibilities of considering different viewpoints based on certain groups of users belonging to
different ethnicities, geographic regions and recognition of emerging training sets over time.
V. Conclusion
Predicting personalities using tweets is surely a real life problem due to its vast applications in diverse
fields and must be recognized as a significant field of study under natural language processing and must be
harnessed with the predictive potential of machine learning. A lot of work is still to be done which can only be
accomplished by overcoming the constraints put forward by language use and intent of users based on their own
choices. In this paper, we intend to put forward the need of research communities to come forward and gather
enough resources to make machine learning a feasible method for prediction on both macro and micro levels.
References
[1]. Twitter Stats 2015, Twitter Inc.
[2]. Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis." Foundations and trends in information retrieval 2.1-2 (2008):1-
135
[3]. Asur, Sitaram, and Bernardo A. Huberman. "Predicting the future with social media." Web Intelligence and Intelligent Agent
Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on Vol 1 IEEE, 2001
[4]. Cambria, Erik, et al. "New avenues in opinion mining and sentiment analysis. “IEEE Intelligent Systems 28.2(2013): 15-21
[5]. Golbeck, Jennifer, Cristina Robles, and Karen Turner. "Predicting personality with social media." CHI'11 extended abstracts on
human factors in computing systems. ACM, 2011.
[6]. Barrick, Murray R., and Michael K. Mount. "The Big Five personality dimensions and job performance: A meta-analysis." (1991).
[7]. Judge, Timothy A., et al. "The big five personality traits, general mental ability, and career success across the life span." Personnel
psychology 52.3 (1999): 621-652.
[8]. Shaver, Phillip R., and Kelly A. Brennan. "Attachment styles and the" Big Five" personality traits: Their connections with each
other and with romantic relationship outcomes." Personality and Social Psychology Bulletin 18.5 (1992): 536-545.
[9]. Golbeck, Jennifer, et al. "Predicting personality from twitter." Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third
Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on. IEEE, 2011.
[10]. Quercia, Daniele, et al. "Our Twitter profiles, our selves: Predicting personality with Twitter." Privacy, Security, Risk and Trust
(PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International
Conference on. IEEE, 2011.
[11]. Sumner, Chris, et al. "Predicting dark triad personality traits from Twitter usage and a linguistic analysis of tweets." Machine
Learning and Applications (ICMLA), 2012 11th International Conference on. Vol. 2. IEEE, 2012.
[12]. Lima, Ana CES, and Leandro N. De Castro. "Multi-label Semi-supervised Classification Applied to Personality Prediction in
Tweets." Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), 2013
BRICS Congress on. IEEE, 2013.