1. Sentiment/opinion analysis
Author: Martin Mikula
Supervisor: Xiaoying Sharon Gao

2. Which one of you have read a review before you bought a new product?

3. Outline Motivation Tasks Domains Approaches Lexicons
My lexicon approach

4. Motivation the shopping behaviour analysis
the analysis towards politicians and government policy
to contact people with same opinions
WordCupinion
the medicine
patient‘s health
analysis

5. Sentiment vs. emotion analysis
sentiment/opinion analysis
is a given piece of text positive, negative or neutral?
the text may be a sentence, a tweet, an SMS message, a customers review, a document and so on
emotion analysis
what emotion is being expressed in a given piece of text?
basic emotions: joy, trust, fear, anger
other emotions: guilt, pride, frustration, optimism

6. Tasks

7. Tasks what is the sentiment of the speaker/writer
is the speaker explicitly expressing sentiment
what sentiment is evoked in the listener/reader
what is the sentiment of an entity mentioned in the text
consider the above questions with the examples below
General Tapioca was ruthlessly executed today.
Mass-murderer General Tapioca finally killed in battle.
General Tapioca was killed in an explosion.

8. Domains newspaper texts novels e-mails customer reviews blog posts
SMS messages
tweets
facebook posts
... and so on

9. Quirks of Social Media Texts
informal
short (140 characters for tweets or SMS messages)
abbreviations and shortenings
wide array of topics
spelling mistakes and creative spelling
special strings (hashtags, emoticons, conjoined words)
huge volume (over 500 million tweets a day)
contain meta-information (date, location, links)
often express sentiment

10. Approaches the lexicon based approaches
use lexicons – the lists of positive and negative words
the machine learning approaches
use machine learning techniques for sentiment analysis
the hybrid approaches
combine the lexicon based approaches with the machine learning techniques

11. Sentiment lexicons the lists of words may contain weights
for every word
may contain shifters
intensification
negation
How do you want to use the dictionary?

12. Sentiment lexicons manually created automatically created
General Inquirer (1966) – 3600 words
Turney and Littman (2003)
MPQA (2005) – 8000 words
SentiWordNet (2006) – synsets
Hu a Liu lexicon (2004) – 6800 words
MSOL (2009) – 60,000 words
NRC emotion lex. (2010) – 14,000 words
Hashtag sentiment lexicon (2013) – 220,000 unigrams and bigrams
Afinn ( ) – 2400 words
Sentiment140 (2013) – 330,000 unigrams and bigrams
MaxDiff (2014) – 1500 words

13. My sentiment lexicon range from -3 to 3
customized Lancaster stemming algorithm
Word
Weight
Subjectivity
good 1
p (positive)
bad -1
n (negative)
quite
1,25
i (intensifier)
not
o (opposite)
good-goodness
care-careless

14. Results contains 2500 reviews influence of negation
Dictionary
Accuracy (%)
posit + negat
86.2
intensification
85.9
negation
86.1
all together
85.7
contains 2500 reviews
2324 positive reviews
176 negative reviews
influence of negation
contains 5242 reviews
2572 positive reviews
2668 negative reviews
current accuracy is aroud 71%
Dictionary
Accuracy (%)
shift negation
60.7
switch negation
60.6
both hegations 61
Dictionary
Accuracy (%)
posit + negat
55.8
all together
61.6