This document discusses sentiment analysis of social media content. It begins by noting the abundance of opinion-rich resources online due to the social nature of Web 2.0. Sentiment analysis aims to automatically analyze opinions expressed in text to determine sentiment. This is challenging for social media due to informal language, short texts, and continuous new content. The document outlines challenges in building sentiment classifiers from social media data due to the need for large labeled training datasets but few available labels. It describes using emoticons and sentiment dictionaries to implicitly label some data for use in classifier training and evaluates the agreement between these labeling methods.

1. Machine Learning for Big Data
Prof. Dr. Eirini Ntoutsi
Leibniz University Hannover & L3S Research Center
Sentiment Analysis of Social Media Content
A multi-tool for listening to your audience and
developing sentimental content strategies
EUMade4All Workshop, Hannover, 29.9.2017

2. Outline
 A world of opinions
 Analyzing opinions for sentiment
 Using sentimental content
2Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content

3. A Web/World of opinions
 With the advent of Web 2.0 and its social character a lot of opinion-rich
resources have arise
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content 3

4. Opinions
4Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content

5. Opinions vs Facts
 Facts
 Screen: 4.7in LCD 1334x750 (326ppi)
 Processor: Apple A11 Bionic
 RAM: 2GB of RAM
 Storage: 64/256GB
 Operating system: iOS 11
 Camera: 12MP rear camera, 7MP front-facing camera
 Connectivity: LTE, Wi-Fiac, NFC, Bluetooth 5, Lightning
and GPS
 Dimensions: 138.4 x 67.3 x 7.3mm
 Weight: 148g
5Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
 Opinions

6. Opinions on everything
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content 6

7. Why we care?
 Opinions are produced at a constant basis and are (most of
the times) freely available
 Free feedback from our customers/ users 
 Valuable source of information for companies, politicians1,
decision makers
 Companies turn into social media monitoring in order to
optimize and strengthen their products and brands
 An opportunity for marketers to pay attention to
consumers’ feelings towards their brand
 People have the power to influence each other in their
decisions
 Product design could be driven by user requests
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content 7
1https://motherboard.vice.com/en_us/article/mg9vvn/how-our-likes-helped-trump-win

8. Sentiment analysis
 Opinions on Vodafone
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
 What we are interested in?
 (Automatically) Identifying the negative tweets (and reacting … customer care)
8

9. Aspect-oriented sentiment analysis
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
 It‘s not ALL good or bad
 Reviews from TripAdvisor on Vienna Marriott Hotel
2/5/2014: Great hotel, very nice rooms, perfect location, very nice staff except for a mid-aged female receptionist who tried to
charge me extra for wifi fees when checking out. It was waived at the desk when I checked-in. And she started treating me with
an attitude after she found out that I got a great deal through priceline.com. ….
26/1/2014: Spent a long weekend here. Rooms clean and functional without being spectacular and a nice pool etc. Staff in pool
weren't Good and I found them actually quite rood. Executive lounge was ok and not busy but selection of wine and beer wasn't
great. The reception has many shops and a bar at the end which kind of males it feel like a shopping centre. Overall great for
business travel but not sure id come again for leisure.
7/5/2013: The Vienna Marriott has all you expect; no frills, but solid service and they get all the basic stuff done right.
It's in a fine location, maybe 10 minute walk from the major city attractions while being in a quiet area. Breakfast buffet
exceptional and good fitness center. Very helpful and happy staff.
Lobby lounge just okay. Not a good wine selection and the Sinatra-like singer adds nothing.
Maybe just a little more expensive than it should be, too.
 What we are interested in?
 What people are talking about (items and item aspects)
 The attitude of people towards these items and aspects
9

10. (Sentiment- & aspect-based) opinion summarization
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content 10

11. (Sentiment- & aspect-based) opinion summarization
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content 11

12. Sentiment analysis: an umbrella term
 The Sentiment Analysis task
 Is a given text positive, negative, or neutral?
 Text = a sentence, a tweet, a customer review, a document …
 The Emotion Analysis task
 What emotion is being expressed in a given piece of text?
 Basic emotions: joy, sadness, fear, anger,…
 Other emotions: guilt, pride, optimism, frustration,…
 The Aspect-oriented Sentiment Analysis task
 What are the product/entity aspects discussed in a text?
 What is the sentiment of those aspects?
 The Summarization task
 What are the key aspects in users’ opinions? What is the predominant
sentiment?
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content 12

13. Outline
 A world of opinions
 Analyzing opinions for sentiment
 Using sentimental content
13Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content

14. Building a sentiment classifier
 Building a sentiment classifier requires data and algorithms
14Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
Algorithm
Model
f(x)

15. Challenges of sentiment analysis in social media
 Language-related & medium-related challenges
 Informal
 Short, 140 characters for tweets
 Abbreviations and shortenings
 Wide array of topics and large vocabulary
 Spelling mistakes and creative spellings
 Special strings like hashtags, emoticons, conjoined words
 Data properties
 Large amounts of opinions (Volume)
 Continuous flow of opinions (Velocity)
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content 15

16. Challenges of sentiment analysis in social media
 Sentiment-related challenges
 The unambiguous identification of sentiment
 Sarcasm
 Bipolarity
 Dealing with colloquial language
 tweets containing colloquial slang
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content 16

17. Building a sentiment classifier
 Building a sentiment classifier requires data and algorithms
 Two challenging parts
 Learning: How to build a classifier?
 Labeling: How to create a (class-labeled) training set?
17Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
Algorithm
Model
f(x)

18. How to build a classifier
18Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
Preprocessing part
Negations
Colloquial language
Superfluous words
Emoticons
Learning part/ Classifiers
Naïve Bayes
SVMs
Ensembles
Deep Neural Networks
KNNs
…

19. Preprocessing - Negations
19Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
 Tagging negations with verbs
 27.222.287 found verb negations (0.4%)
 Tagging negations with adjectives
 2-part adjective co-occurrences
 3-part adjective co-occurrences
 4.832.573 found adjective negations (0.1%)
I do not like  I NOT_like
It didn't fit  It NOT_fit
not pretty  ugly
not bad  good
not very young  old
Verbs negation list: www.vocabulix.com
Adverbs negation list: www.scribd.com
85%
15%
Negation verbs Negation adjectives
Iosifidis & Ntoutsi, “Large scale sentiment learning with limited labels”, KDD 2017

20. Preprocessing effect – Overall view (distinct words)
21Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
0
50.000.000
100.000.000
150.000.000
200.000.000
250.000.000
300.000.000
original slang links & mentions negations Emoticons Stopwords
Iosifidis & Ntoutsi, “Large scale sentiment learning with limited labels”, KDD 2017

21. (back to) Building a sentiment classifier
 Building a sentiment classifier requires data and algorithms
 Two challenging parts
 Learning: How to build a classifier?
 Labeling: How to create a (class-labeled) training set?
22Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
Algorithm
Model
f(x)

22. How to create a (class-labeled) training set
 Big Data but few labels
 Human labelling at this scale is impossible
 What other (machine-based) resources can we exploit to label (part of)
our data?
 At the data level
 Labels through emoticons
 Labels through sentiment dictionaries (like SentiWordNet)
 At the machine learning model level
 use both labeled and unlabeled data for learning  semi-supervised learning
23Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content

23. Labels through emoticons
 Implicit labels, through emoticons
 We assembled a list of positive, negative emoticons
 #72 positive class emoticons :-) :) :o) =) ;) (: (; (= <3 :D :-D :oD =D ;D
 #70 negative emoticons :( :-( :o( =( ;( ;-( ): ); )=
 We classified tweets based on their emoticons
 Positive  only positive emoticons (10%)
 Negative  only negative emoticons (2%)
 Mixed  both positive and negative (1%)
 No emoticon (88%)
 In total, 57.340.286 (12%) are pure-labeled.
24Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
10%
88%
2% 0%
emoticons_positive no_emoticons
emoticons_negative emoticons_mixed

24. Labels through SentiWordNet
 SentiWordNet: a lexical resource for supporting sentiment classification
 Tweet sentiment as an aggregation of the sentiment of its member words
 SentiWordNet labeling results
 Positive: only positive words
 Negative: only negative words
 Neutral: only neutral words
 Zero-sum: mix of positive and negative
 No decision: words do not exist in the lexicon
 e.g., #Iloveobama, #refugeecrisis etc
25Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content

25. Emoticons vs SentiWordNet
 For the intersection (57.340.286 = 12% tweets with pure sentiment-based labels),
we checked agreement in the labels
 Causes of disagreement
 Emoticons-based labeling
 Prone to errors: existence of positive emoticons does not imply positive words
 SentiWordNet-based labeling
 SentiWordNet is a static dictionary
 Twitter is very dynamic
 Words change polarity (also based on context)
 New words are created (e.g. hashtags) which are not part of the dictionary
26Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
Emoticon-based
labeling
SentiWordNet-based labeling
Positive Negative Neutral Zero sum No-decision
Positive 28.104.677
(49%)
10.756.225
(19%)
4.908.237
(9%)
23.297
(0.04%)
3.140.978
(5%)
Negative 4.929.947
(9%)
3.885.983
(7%)
930.075
(2%)
7.527
(0.01%)
653.340
(1%)
• We need a hybrid approach:
Campero et al, “Tracking Ephemeral Sentiment
Entities in Social Streams”, submitted 2017

26. Challenges and opportunities
 Multilinguality
 486.627.464 (English tweets) out of 1.882.387.310 total tweets  we utilize
only 26% of the dataset.
 Add multilingual content
 Transfer learning
 Exploit the content similarity
 Not everyone uses emoticons
 If tweets are similar, “inherit” the sentiment from the “neighboring” tweets
 Exploit the hashtags
 Start with a seed of positive, negative hashtags
 Data augmentation
 Iosifidis & Ntoutsi, “Data Augmentation for Polarized Textual Data for Dealing with Class
Imbalance”, Submitted 2017
27Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content

27. Challenges and opportunities
 Dealing with class imbalance
 Most of the opinions/ reviews are positive (5*, respectively). How can we build
models that learn best all classes (not just the majority)?
 Dealing with changes
 How sentiment changes with time? How can we build classifiers that react to
change (concept drifts)?
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content 28

28. Reacting to change
29Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
Part of our ongoing work on the OSCAR project
DFG project OSCAR: “Opinion Stream
Classification with Ensembles and Active
leaRners”

29. Outline
 A world of opinions
 Analyzing opinions for sentiment
 Using sentimental content
30Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content

30. Changing perspectives: Serving emotional content
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
"At the constitutional level where we work, 90% of any decision is emotional.
The rational part of us supplies the reasons for supporting our predilections.”
----Justice William O. Douglas
31

31. Rational appeal
32Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
 List benefits

32. Emotional appeals
 You will be happier, smarter or better looking if you have this item.
33Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content

33. The cultural challenge
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
 A case study of FIAT
 FIAT released an ad in Italy in which actor
Richard Gere drives a Lancia Delta from
Hollywood to Tibet.
 Gere is hated in China for being an
outspoken supporter of the Dalai Lama
 There was a huge online uproar on
Chinese message boards commenting that
they would never buy a FIAT car.
34

34. The ephemeral sentiment challenge
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
 Sentiment trajectory for refugees topic
35
Source: Multilingual Sentiment Analysis on Data of the Refugee Crisis in Europe, Shalunts and Backfried, Data Analytics 2016

35. To summarize
 Opinions convey more than just information
 They comprise a great (and free, most of the times) resource for getting to
know your audience students
 You can use opinionated words/ emotions to connect to your audience
students
 Many tools for sentiment analysis exist out there (some for free, but also
professional ones)
 From an ML point of view
 A challenging problem due to language, lack of labeled data, noisy data,
change and context
36Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content

36. Thank you! Questions/ Thoughts?
Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content 37

37. Contact
38Prof. Dr. Eirini Ntoutsi: Sentiment Analysis of Social Media Content
Prof Dr. Eirini Ntoutsi
FG Intelligent Systems
Faculty of Electrical Engineering and Computer Science
Leibniz University Hannover & L3S Research Center
http://www.kbs.uni-hannover.de/~ntoutsi/
ntoutsi@l3s.de