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Semantic Analysis in Language Technology
Lecture 3 - Semantic-Oriented Applications:
Sentiment Analysis
Course Website: http://stp.lingfil.uu.se/~santinim/sais/sais_fall2013.htm

MARINA SANTINI
PROGRAM: COMPUTATIONAL LINGUISTICS AND LANGUAGE TECHNOLOGY

DEPT OF LINGUISTICS AND PHILOLOGY

UPPSALA UNIVERSITY, SWEDEN

21 NOV 2013
Acknowledgements
2

 Thanks to Bing Liu for the many slides I borrowed

from his Tutorial on Sentiment Analysis and Opinion
Mining. Big thanks to Dan Jurafsky for his slides
from Coursera NLP course.

Lecture 3: Sentiment Analysis
3

Lecture 3: Sentiment Analysis
Why are sentiments important
(opinions/emotions/affects/attitudes/etc)
4

Lecture 3: Sentiment Analysis
5

Lecture 3: Sentiment Analysis
6

Lecture 3: Sentiment Analysis
Text Categorization Problem
7

 Different level of granularity:
 Document
 Sentence
 Summary

Lecture 3: Sentiment Analysis
8

Lecture 3: Sentiment Analysis
Opionion: Formalization: Quadruple (4 components)
9

Lecture 3: Sentiment Analysis
Whatch out!
10

 Date: The date is important in practice because one

often wants to know how opinions change with time
and opinion trends.

Lecture 3: Sentiment Analysis
11

Lecture 3: Sentiment Analysis
12

Lecture 3: Sentiment Analysis
Opionion: Formalization: Quintuple (5 components)
13

Lecture 3: Sentiment Analysis
14

Lecture 3: Sentiment Analysis
15

Lecture 3: Sentiment Analysis
16

Lecture 3: Sentiment Analysis
In which way ”sentiment” belongs to semantics?
17


Semantics is the study of
meaning:




It focuses on the relation
between signifiers, like
words, phrases, signs, and
symbols, and what they
stand for. Through a
semantics, we want to
understand human language.

Through SA we want to
automatically identify the
meaning of certain words,
phrases, etc. and how they
relate to affective states
expressed in texts (long,
short, oral, written, etc.)

Lecture 3: Sentiment Analysis
Subjectivity & Emotion
18

Lecture 3: Sentiment Analysis
Subjectivity
19

Lecture 3: Sentiment Analysis
Emotion
20

Lecture 3: Sentiment Analysis
Sentiment, Subjectivity, Emotion
21

Lecture 3: Sentiment Analysis
Affect and Affective words…
22
http://research.microsoft.com/en-us/projects/tweetaffect/

Lecture 3: Sentiment Analysis
23

Lecture 3: Sentiment Analysis
24

Lecture 3: Sentiment Analysis
Basically… Text Classification!
25

 Topic-based classification
 Genre identification
 Authorship attribution






(plagiarism,
authorship/classification of
anonymous texts)
Spam filters
Automatic email classification
(folder assignment)
Threat identification
Etc.

Lecture 3: Sentiment Analysis
26

Lecture 3: Sentiment Analysis
Opinion Mining in the real world…
27

Lecture 3: Sentiment Analysis
UnSupervised Learning
28

Lecture 3: Sentiment Analysis
Supervised Classification
29

 See Dan’s video presentation!

Lecture 3: Sentiment Analysis
30

Lecture 3: Sentiment Analysis
31

Lecture 3: Sentiment Analysis
32

Lecture 3: Sentiment Analysis
33

Lecture 3: Sentiment Analysis
34

Lecture 3: Sentiment Analysis
35

Lecture 3: Sentiment Analysis
36

Lecture 3: Sentiment Analysis
37

Lecture 3: Sentiment Analysis
38

Lecture 3: Sentiment Analysis
Team Work: 20 min; Discussion 15 min
39


You are going to apply for funding . You are interested in Horizion 2020 funding scheme (the
new European research and innovation funding framework)



You think it is a good idea to create a Mood Index App.
Plan with your team mates this new sentiment-based app. Present to the audience the following
aspects:

1)
2)
3)
4)
5)

6)
7)

Purpose: what is the main use of this new app? (ex, identification of self-distructive behavior,
depressive states, sad/happy mood, freindly attitudes, etc.)
Target users: who is going to use this app? (young people, parents, etc)
Scenario: describe a typical scenario/context where your app is going to be used with fruitful
results
Computational aspects: Which sentiment classes is the app going to identify? In which
language? Which computational model is going to be based upon?
The actors: what kind of experts do you need? (ex a computational linguist, a app developer,
a psychiatrist, a company taking care of marketing and commercialization, a social worker,
school teacher etc.)
Societal Benefits: How can the commercialization of your app contribute to decrease
unemployment in your country and/or in EU.
Any additional aspect you might find relevant.

Lecture 3: Sentiment Analysis
How to build your own Twitter Sentiment Analysis Tool
40


http://blog.datumbox.com/how-to-build-your-own-twitter-sentiment-analysis-tool/

Lecture 3: Sentiment Analysis
41

This is the end… Thanks for your attention !

Lecture 3: Sentiment Analysis

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Lecture 3: Structuring Unstructured Texts Through Sentiment Analysis