Lecture 3: Structuring Unstructured Texts Through Sentiment Analysis

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This document summarizes a lecture on sentiment analysis. The lecture discusses why sentiment analysis is important, how it relates to semantics, and different approaches like supervised classification. It also provides examples of sentiment analysis applications in areas like opinion mining and developing a mood index app. The lecture materials come from Marina Santini's course on computational linguistics and language technology at Uppsala University.

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

Why are sentiments important
(opinions/emotions/affects/attitudes/etc)
4

Lecture 3: Sentiment Analysis

Text Categorization Problem
7

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

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

Opionion: Formalization: Quintuple (5 components)
13

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

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

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

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 !

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