1. CIS630 Spring 2013 Lecture 2 Affect analysis in text and speech

2. Course objectives Overview theories and approaches to affect and opinion analysis in text and speech Personalized project to allow students to deepen their understanding and pursue applications outside the course Learn to comfortably discuss academic papers, with critiques, summaries and discussions Improve written and oral presentation skills

3. Grade breakdown [30%] Project 1: text-based analysis on NYT [40%] Project 2: student proposed project Proposal Progress presentation (in class) Final report (written) [15%] Questions on assigned reading and class participation [15%] Leading a discussion

4. Organization: assigned readings Readings assigned for each class Students should do the readings BEFORE class starting next week Submit three questions related to the assigned reading Due on by 9am the day of class Discussion leaders can use these to prepare their presentation

5. Project 1 General theme: Affect analysis in the NYT Project presentations: Feb 25/27 Project report due March 6 Will discuss more specifics in the second half of class today

6. Project 2 One page proposals due March 1 Students are encouraged to integrate the topic with other research or project that they are doing Proposal presentation April 1 (~10 mins) Preliminary results April 17/22 (~20mins) Final projects due: May 1 Teams of two may work together on the project

7. Reading 1 Sentiment analysis and opinion mining, Bing Liu Jan 14 (pp.1 –37) Jan 16 (pp. 37—79) Jan 21 MLK day---no class Jan 23 (pp. 79—134) After that we will start with two readings per class, questions and student-led discussions

8. Opinions are central to almost all human activities and are key influencers of our behavior What we pay attention to is determined by affect What we remember is modulated by affect. Stress impedes memory, positive attitude strengthens it May be expressed rather differently cross-culturally Opinions and related concepts such as sentiments, evaluations, attitudes, and emotions are the subjects of study of sentiment analysis and opinion mining

9. What others think Whenever we need to make a decision, we want to know others’ opinion  Is it the opinions of others, or is it facts that may help us form our opinion that we are looking for?

10. Applications Company/product image analysis Using sentiment to predict sales Using reviews to rank products and merchants Link between public opinion in blogs and Twitter and NFL betting line Twitter sentiment to predict election results Analysis of political standpoints Predicting comment volumes of political blogs Twitter data, movie reviews and blogs to predict movie box-office performance

11. Analyze sentiment flow in social networks Twitter moods to predict the stock market, trading strategies, particular stocks Assessment of global happiness based on Facebook posts

12. Levels of analysis Document level: assume the document talks about a single entity, predict the sentiment Sentence level: Determine if a sentence expresses a positive, negative or neutral opinion Subjective (express internal speaker states that cannot be verified) vs objective sentence (expressing facts) “We bought the car last month and the windshield wiper has fallen off”.  objective sentence that expresses negative opinion

13. Entity and aspect level Example from Google productsGoogle products Posted by: John Smith Date: Sep 10, 2011 (1) I bought a Canon G12 camera six months ago (2) I simply love it. (3) The picture quality is amazing. (4) The battery life is also long. (5) However, my wife thinks it is too heavy for her.

14. Opinion (definition) An opinion is a quintuple e: entity (nouns/NPs) a: aspect (nouns/NPs) s: positive, negative or neutral sentiment (explicit or implicit) h: opinion holder t: time

15. Document sentiment classification GENERAL aspect: covers the entity as a whole Binary classification: positive or negative Regression: scale, number of starts a standard baseline for such tasks is the use of the individual words in the text -- terms and their frequency -- part of speech -- sentiment words and phrases -- sentiment shifters (hardly, mostly, didn’t) -- syntactic dependencies

16. Parts of speech and sentiment Verbs: can evoke/convey sentiment by association of the results of the action Break, Lose, Win, Murder Nouns: usually the opinion/sentiment targets; some can be mixed Adjectives and adverbs: by definition convey properties, so if the properties are worth mentioning probably has some affective value

17. Project 1 proposal Use existing sentiment resources to rank texts in the NYT in terms of their opinion and affect content Find words that are distributed differently on the top and bottom ranked documents Find nouns that are modified by adjectives JJ NN, NN is JJ Rank entities by the diversity of qualifications and the ratio of times they are mentioned with and without adjective Similarly with verbs and adverbs