AI for sentiment analysis - An Overview.pdfStephenAmell4
Sentiment analysis, also referred to as opinion mining, is a method to identify and assess sentiments expressed within a text. The primary purpose is to gauge whether the attitude towards a specific topic, product, or service is positive, negative, or neutral. This process utilizes AI and natural language processing (NLP) to interpret human language and its intricacies, allowing machines to understand and respond to our emotions.
Defining Personas is an introduction to the usage of "Personas" in User Experience.
Helps identifying the user groups of the website we're developing...by selecting characteristics of those groups.
AI for sentiment analysis - An Overview.pdfStephenAmell4
Sentiment analysis, also referred to as opinion mining, is a method to identify and assess sentiments expressed within a text. The primary purpose is to gauge whether the attitude towards a specific topic, product, or service is positive, negative, or neutral. This process utilizes AI and natural language processing (NLP) to interpret human language and its intricacies, allowing machines to understand and respond to our emotions.
Defining Personas is an introduction to the usage of "Personas" in User Experience.
Helps identifying the user groups of the website we're developing...by selecting characteristics of those groups.
Social Listening and Intelligence is Predictive! Now What?Rob Key
new approaches to filtering and annotating social listening data, together with more advanced modeling, shows clearly that this data has predictive value -- if done correctly. This presentation reviews key data issues and was presented at the Advertising Research Foundation's 2015 Rethink Conference.
Do numbers speak louder than words? The rapidly growing industry of ‘People Analytics’ suggests as much. But, there are big risks in failing to maintain a healthy scepticism in the face of the ‘facts’.
Determine the sentiment of sentence that is positive or negative based on the presence of part of
speech tag, the emoticons present in the sentences. For this research we use the most popular microblogging sit
twitter for sentiment orientation. In this paper we want to extract tweets form the twitter related to the product
like mobile phones, home appliances, vehicle etc. After retrieving tweets we perform some preprocessing on it
like remove retweets, remove tweets containing few words with minimum threshold of length five, remove tweets
containing only urls. After this the remaining tweets are pre-processed like that transform all letters of the
tweets to the lower case then remove punctuation from the tweets because it reduces the accuracy of result.
After this remove extra white spaces from the tweets, then we apply a pos tagger to tag each word. The tuple
after the applying above steps contain (word, pos tag, English-word, stop-word). We are interested in only
tweets that contain opinion and eliminate the remaining non-opinion tweets from the data set. For this we use
the Naïve Bays classification algorithm. After this we use short text classification on tweets i.e., the word having
different meaning in different domain. In order to solve this problem we use two different feature selection
algorithms the mutual information (MI) and the X2 feature selection. At final stage predicting the orientation of
an opinion sentence that is positive or negative as we mentioned above. For this we use two model like unigram
model and opinion miner.
Some insights into social media analytics tool, including a high-level overview of the technology behind the data configuration - or how the tools filter social media conversations.
Sentiment Analysis: The Marketplace and ProvidersSeth Grimes
Short tutorial presentation by Seth Grimes, presented as part of the Practical Sentiment Analysis tutorial on May 7, 2013, prior to the Sentiment Analysis Symposium, http://sentimentsymposium.com/
Thinking like Humans - Tools to improve how we solve problems for our usersLenae Storey
We all have our biases, perspectives, assumptions, and naturally, we bring these views with us into our everyday thinking. The same plays out in how we design solutions, strategies, and businesses. This presentation highlights the need for ethnographic research and relatable tools to drive improved impact for our humans (the users and customers of our outputs).
A Sententia Gamification Design Tool
www.SententiaGames.com
What is a Learner Persona?
Learner Personas are a fictional representation of your targeted learners. They are based on real data about learner demographics and behavior, along with educated speculation about their personal histories, motivations, and concerns.
Learn More at www.SententiaGames.com
Monica Cornetti
CEO Sententia Gamification
guru@sententiagames.com
@monicacornetti
Personalization, Going Beyond the Technology (Como envolver os clientes, sem ...E-Commerce Brasil
Edward Chenard fala sobre "Como envolver os clientes, sem deixar que a tecnologia fique no caminho da relação" no Congresso E-commerce Brasil de Experiência do Cliente 2014.
How cognitive computing is transforming HR and the employee experienceRichard McColl
As a co-sponsor of a recently published IBM Institute of Business Value (IBV) I was pleased to see the insights support our own IBM HR strategy of aligning cognitive solutions to cloud platforms to create fantastic experiences for our IBM'ers.
UXPA 2023: Learn how to get over personas by swiping right on user rolesUXPA International
This session walks through the concept of user roles as an alternative to personas as a means to generate and disseminate user insights for product development teams. We will describe the tools and methods used to create a research database organized by user roles, along with examples and short exercises to help attendees think through user roles within their own context.
By the end of the session, attendees should be aware of tools and approaches for:
Organizing user research information in a database
Disseminating user role information to product and design teams
Managing a user roles database as part of a long term UX Research program
If you’re ready to ditch personas but don’t know how, this session is for you!
This presentation aims to teach others how to use the user centered design methodology known as personas.
Personas are archetypes (models) that represent groups of real users who have similar behaviors, attitudes, and goals. A persona describes an archetypical user of software as it relates to the area of focus or domain you are designing for as a lens to highlight the relevant attitudes and the specific context associated with the area of work you are doing.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
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Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
2. Problem: We have chosen the first challenge:
Increase the revenue growth with top ecommerce
players
The aim is to design and deliver best in class shopper
experience across this channel and make more data-
driven decisions.
Our Approach: HUL IT division at present has 5 core
capabilities across the market value chain: Live Wire-
Granular Data Analysis, JARVIS-Advanced Analytics,
People Data Centre (PDC)-Social Conversation, Smart
Pick - Consumer Journey, and Maxima-Precision
Targeting & Deployment.
Having understood the 5 core capabilities of HUL IT
division across the market value chain, we tried to
arrive at a solution to create a new capability to enrich
the shopper experience and boost the ecommerce
revenue growth.
Over the years, traditional recommender algorithms
were used to analyse the search patterns and digital
foot prints across different channels to perform
‘remarketing’ and recommender ads.
Our Idea: Taking further, our big ideais “to analyse the
Personality of the customers using ‘Personality
Prediction Algorithms’ and match that withthe existing
HUL Brands’ persona”.
Any user on social media would leave their foot print
and content in the form of likes,comments and status
updates etc.,. We want to use this ‘Content and
Linkage data’ to predict the personality of a user and
correlate it with the Big 5 personality traits (Openness,
Extraversion, Neuroticism, Agreeableness,
Conscientiousness (Refer- Appendix for more
information on Big 5 personality traits).
A Machine Learning Algorithm is developed to
understand the personality traits of a customer and
match him with the existing HUL Brands.
This ML Algorithm would include 4 processes:
LinguisticFeature Extraction:Extractwordsorsequences
of words. For example – Key words on statuses or
comments like – ‘Can’t Wait’, ‘Weekend Party’, ‘lit’ etc.,
will be extracted.
CorrelationAnalysis:Developrelationbetweenlanguage
used and Personality Variables. The words that were
extracted by the algorithm are grouped into different
linguisticfeatureslikeSwearwords,AnxietyWords,Work
relatedwordsetc.Once grouped,acorrelationanalysisis
done by linking these groups with 5 personality traits.
(Refer- Appendix Table 3forsample correlationanalysis)
Matching personality with HUL brands’ persona: The
difficult job is to understand HUL brands’ persona and
match that with the personality traits. Different brands
have different TGs, different brand architypes and
positioning. This data needs to be properly normalized
across 5 personality traits and matched with the
customer’spersonality traits using ‘Precision Targeting’
For example, a customer who is popularly tagged, who
agrees with much of the content (liking, commenting
positively on posts more than an average) and has less
openness like – not updating status, not sharing his
momentsetc.,canhave the above personalitytraits. The
otherside,fromthe communicationsof Liptongreentea,
it is evident that customers trust the recommendations
given to them regarding the usage of the brand. This
reflects that they are highly agreeable. Hence, there is a
strong match between the customer traits and Lipton
green tea traits.
Personalized ecommerce promotions: For this person
(exampled above), a personalized Lipton Green Tea
ecommerce banner/native adispromotedoverdifferent
social channels to him (using Maxima).
(Source: http://www.pnas.org/content/114/48/12714 )
Here we explainedthe ideawith just5 personalitytraits.
But consideringthe bandwidthof HULIT, these traitscan
be extended further to improve Precision Targeting
CTRs and conversion rates were 1.3 and 1.2 times
higher when the persuasive advertisingmessage was
tailoredtothe psychological profile of the preexisting
behavioral audience
Linguistic Feature
Extraction
Correlation
Analysis
Match Personality
with brand persona
PersonalizedeCom
promotions
Customer Traits Lipton green Tea Traits
3. Appendix
Sources:
H.V. Zhao, W.S. Lin and K.J.R Liu, “Behavior modeling and forensics for multimedia social networks” Proc. Signal
Processing Magazine, IEEE (Volume:26 , Issue: 1 ), pp.118-139, 2009.
Y. Bachrach, M. Kosinski,T.Graepel,P.Kohli andD. Stilwell,“PersonalityandPatternsof FacebookUsage”, Proc. the
3rd Annual ACMWeb Science Conference, pp. 24-32, 2012.
T. DuBois,J.Golbeck,J.Kleint,andA.Srinivasan. ImprovingRecommendationAccuracybyClusteringSocial Networks
with Trust. In Recommender Systems & the Social Web, 2009.
Big 5 Personality Traits:
1. Personality traits and their social Media Behavior
PersonalityTrait PersonalityBehavior Facebook Behavior
Openness
Highopenness meansappreciationfor
art, adventure,more creative andhas
newideas.
Positivelycorrelatedwith
numberof likes,group
associationandnumberof status
update
Conscientiousness
Spontaneousapproachinlife,well
organized,reliable andconsistent.
Negativelycorrelatedtonumber
of likesandgroupassociations
but positivelyrelatedtophotos
uploaded
Extraversion
Prone to live inexternal world,positive
emotions,expressive,friendlyand
sociallyactive
Positivelyassociatedwithstatus
update, tendencytouse “Like”
and Numberof Facebookfriend
and Facebookgroup
Agreeableness
Friendlyandcompassionate,
cooperative
Negativelycorrelatedwiththe
numberof likesbutpositivelyco
relatedwithtagged.
Neuroticism
Relatedtoemotionsandmood swing.
Negative emotionssuchasanger,
anxiety,depression
Positivelycorrelatedwith
numberof likesandnumberof
groups.
2. Personality traits and their social Media Behavior
Personality Traits Behavior on Facebook
Extraversion Frequent user of Facebook
More use Facebook Component
More number of Facebook
Friends
More Facebook groups
Neuroticism Spend more time on Facebook,
more use of Facebook wall
Less use of private message
Share more information
Agreeableness More number of friends
Openness More use of Facebook for
communication
More number of components
More knowledge of features
Conscientiousness Limited Facebook activity
4. 3. Table 3
Correlation between social media foot prints and Personality traits
Opennes
s
Consc
.
Extraver
t
Agreeabl
e
Neurocitis
m
Linguistic Features
Swear Words 0.006 0. 0.032 0. --0.120
Social Processes (e.g. Mate, talk, they, child) 0.010 0.264 0.091 0. 0.
Human Words (e.g. baby, man) 0.078 0.203 0.070 --0.050 0.
Affective Processes (e.g. Happy, cried, abandon) 0.105 0. 0.136 0.203 0.038
Positive Emotions (e.g. Love, nice, sweet) 0.052 0.045 0.117 0.167 0.
Anxiety Words (e.g. Worried, fearful. nervous) 0.044 --
0.150
0.008 0.101 0.192
Perceptual Processes (e.g. Observing. heard, feeling) --0.040 0. 0. 0. 0.096
Seeing Words (e.g. View, saw, seen) 0.060 0. 0. 0.013 0.067
Biological Processes (e.g. Eat, blood, pain) 0. 0.042 0.038 0.154 0.067
Ingestion Words (e.g. Dish, eat, pizza) 0. --
0.050
0.029 0.031 0.207
Work Words (e.g. Job, majors, xerox) 0.134 0.096 0.154 0.048 0.
Money Words(e.g. Audit, cash, owe) -0.161 0.024 0.012 0. 0.029
Structural Features
Number of Friends 0. 0. 0.186 0.013 0.
Egocentric Network Density 0. 0.050 0. 0.059 0.032
Activities and Preferences
Activities (char length) 0.115 0.095 0.188 0.066 0.
Favorite Books (char length) 0.158 0. 0.019 0.082 0.028
Personal Information
Relationship Status ( none listed,single, not single) 0.093 0.071 0.194 0.040 0.
Last Name length in characters 0.012 0. 0.000 0. 0.184
4. Linkage Data basedanalysis:Social Networkcanbe analyzed withmappingandmeasuringof relationshipsbetween
various entities
5. Contentbased analysis:Social networkingsiteshave large amountof contentinthe formof text,image,audioand
video. This huge database can be used for various researches.