Seminários Avançados de Técnicas de Análise de Sentimentos (2016/1)

Professor: Fabrício Benevenuto

    

 

Motivação

Mais de 7.000 artigos foram escritos sobre o tema análise de sentimento. Centenas de startups já surgiram especificamente para propor soluções de análise de sentimento e principais pacotes estatísticos incluem módulos de análise de sentimento dedicados. Parte desse enorme interesse tem se acentuado nos últimos anos devido ao enorme volume de sentimentos disponíveis a partir de dados de mídias sociais, incluindo Twitter, Facebook, fóruns de mensagens, blogs e fóruns de usuários. Esses trechos de texto são uma mina de ouro para as empresas e indivíduos que desejam monitorar sua reputação e obter feedback em tempo útil sobre os seus produtos e ações. Análise de sentimentos oferece essas organizações a capacidade de monitorar os diferentes sites de mídia social, em tempo real e agir em conformidade. Gerentes de marketing, empresas de relações públicas, gestores de campanha, políticos, e até mesmo investidores e compradores on-line são os beneficiários diretos da tecnologia de análise de sentimento. Existe um grande número de técnicas utilizadas para inferir sentimentos, que variam desde escala psicométricas adaptadas para modelos computacionais, dicionários léxicos até mesmo técnicas de aprendizado e processamento de linguagem natural. Essa disciplina visa prover ao aluno um entendimento dessas técnicas e avaliar como elas têm sido aplicadas em cenários atuais.

 

Cronograma

 

Date

Class #

Description

References

Read to class

Speakers

09/Mar/16 

Class 01

Introduction: Goals and course dynamics

 

 

Fabrício

16/Mar/16 

Class 02

Overview of Terms and Techniques.  

1,2,3,4

1,4

Fabrício

23/Mar/16 

Class 03

Review of Sentence-level methods. Emoticon-based, psychometric scales, LIWC, WordNet-based, ANEW, Hapiness index, Sentistrength,

5-32

 

Fabrício

30/Mar/16 

Class 03

Approaches for Multiple languages

52,53,54

53,54

Matheus

06/April/16 

Class 04

Classification-Based sentiment analysis

 

 

Marcos

13/April/16

Class 05

Analysis of Comparative Opinions

50 e 51

50,51

Jaqueline e Felipe

20/April/16

Class 06

Sasa, SenticNet, SANN, OpinionFinder, OpinionObserver, Vader. Ifeel 2.0

22, 56

22, 56

Fabrício, Túlio

27/April/16

Class 07

Sarcasm and Irony

57 e 58

57,58

Derick

04/May/16

Class 08

Part of Speech Tagging.

59

59

Vinícius

11/May/16

Class 09

Traveling.

 

 

 

18/May/16

Class 10

Traveling.

 

 

 

25/May/16

Class 11

Project Proposal Presentations (5 min)

 

 

 

01/June/16

Class 12

Sentiment Lexicon Generation

61

61

Glender

08/june/15

Class 13

Word2vec

63,64,65

63,65

Rodrigo,Salatiel

15/june/15

Class 14

Person-to-Person Sentiment Analysis

32

32

Gianlucca

22/june/15

Class 15

Detection of Fake or Deceptive Opinions

62,66

62,66

Rodrigo Lemos, Yuri

29/june/15

Class 16

Project Presentations

 

 

 

 

References

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42.    De Choudhury, Munmun, et al. "Characterizing and predicting postpartum depression from shared facebook data." Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing. ACM, 2014.

43.    De Choudhury, Munmun, Andres Monroy-Hernandez, and Gloria Mark. "Narco emotions: affect and desensitization in social media during the mexican drug war." Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 2014.Sentiment analysis of user comments for one-class collaborative filtering over ted talks

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55.    Exploiting New Sentiment-Based Meta-level Features for Effective Sentiment Analysis. Sérgio Canuto, Marcos André Gonçalves and Fabrício Benevenuto.
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