NATURAL LANGUAGE PROCESSING

Transcription

1 NATURAL LANGUAGE PROCESSING Sentiment Analysis in Game Design 5/6/13 1 By: Donald Letts

2 GAME AI Mostly Decision Making Rule based, machine learning, utility based, script, planner Control and Build the user s experience Experience has to be Dynamic Machine learning Is it learning the right things? Can be used in calibrating touch, and visual recognition Kinect, Touch 5/6/13 2

3 GAME AI Besides decision making All hard issues in Game AI Facial recognition Gesture recognition Language recognition Language Recognition Audio recognition Text recognition Generation Take a picture and have a dynamic conversation about the photograph Semantic annotations to the photograph 5/6/13 3

4 CHATBOT NLP is a difficult undertaking Best chatbot in the world is still not very good 2012 Loebner Prize for artificial intelligence Based on Turing s intelligence test Chip Vivant simple deductive reasoning Eliza a massive database of canned responses The ELIZA effect 5/6/13 4

5 CHIP VIVANT Judge: Where did you go to school? Chip: I wasn't aware that I told you I went somewhere. I found out though, that majority of the students in Japan don't pursue tertiary education anymore. They can already work right after graduating from high school. Judge: Are you Japanese? Chip: I haven't achieved self-awareness yet, so I'm not sure. Judge: Why not ask somebody? Chip: No idea. Judge: Why not ask me? Chip: Okay. Judge: Well, I think you may be Japanese - you are very respecful and polite Chip: That's an interesting opinion. 5/6/13 5

6 SENTIMENT ANALYSIS & OPINION MINING Somewhat of a hot topic in the last few years. Polarized opinion data Interesting uses Product, Service, Political, Individual, Event, Topic Challenging Research Problems Sentiment Analysis first appeared in 2003 (Nasukawa and Yi) More commonly used in industry Opinion Mining first appeared in 2003 (Dave, Lawrence and Pennock) More commonly used in academia Keywords sentiment analysis, opinion mining, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, review mining, etc Research on Sentiments and Opinion occurred as early as /6/13 6

7 BehaviorBot A change occurs in the environment Candidate actions selected based on conditions Select action with best response ratio No change to reinforcement value If none BehaviorBot waits for response If negative If positive Reinforcement value is decremented Reinforcement value is incremented

8 PROJECT Lexicon based approach to sentiment analysis Small domain specific dictionary Large English opinion lexicon with 6800 words Social media domain Contains misspelled words on purpose Sentence level classification At most bi-gram Token and the previous token 5/6/13 8

9 PROJECT PSEUDO-CODE Take a list of words in prolog [very, uninspired, food] Using a dictionary dict(very, inc). dict(uninspired, negative). 5/6/13 9

10 PROJECT PSEUDO-CODE Tag each word with sentiment markup [[very, inc], [uninspired, negative], [food, nothing]] Assign values to each tag Value = (Tag == negative )? -1 : (Tag == positive )? 1 : 0 5/6/13 10

11 PROJECT PSEUDO-CODE Look at previous token CurScore = (PrevTok == inc )? Value * 2 : Value Recursively sum up all bi-gram values in the sentence TotalScore = CurScore + TotalofRest If the TotalScore is > 0, we can say it is POSITIVE If the TotalScore is < 0, we can say it is NEGATIVE If the TotalScore is == 0, we can say it is NEUTRAL 5/6/13 11

12 ISSUES Fails on more complex examples Apple is doing well in this lousy economy Neutral We bought a car last month and the windshield wiper has fallen off Neutral Entity (similar to Named Entities) and Aspect level Consists of a sentiment and a target Call quality is good but battery is bad In this project, this is neutral. Two Aspects Call quality Battery Challenging problem Implicit and explicit entity Regular Opinion vs Comparative Opinion Coke tastes very good Coke tastes better than Pepsi 5/6/13 12

13 ISSUES How do we classify sucks? Positive or negative? This camera sucks This vacuum cleaner really sucks A sentence containing a sentiment words does not necessarily express a sentiment Can you tell me which Sony camera is good? If I find a good camera at the shop, I will buy it Sarcasm What a great car! It stopped working in two days! Not often found in consumer reviews, but common in political discourse Sentiment without sentiment words This washer uses a lot of water 5/6/13 13

14 SUPERVISED LEARNING Supervised learning Text classification problem Use existing methods Naïve Bayes Classification Support Vector Machines (SVM) Bag of words performed quite well (Pang, Lee and Vaithyanathan 2002) Effective Features for sentiment classification Word position and frequency count Part of speech (POS) Adjectives are important indicators of opinion Sentiment words Most sentiment words are adjectives, and adverbs but nouns and verbs can express sentiments Sentiment shifters I don t like this camera 5/6/13 14

15 CLASSIFICATION Divide and Conquer Different types of sentences must be treated in different ways Seeded pre-training (semi-supervised) Sarcastic sentences usually occur around other sarcastic sentences Create seeds and expanded the seed set using web queries Enriched training set was used for learning Used two features Pattern Punctuation (Tsur, Davidov and Rappoport, 2010) Best accuracy on three-way classification (sarcastic, positive, negative) was 57% 5/6/13 15

16 BASIC IMPROVEMENTS Parse sentence into list Lexicon based approach Add Part of Speech tagging Use a larger lexicon Machine Learning Approach Simple naïve bayes How complicated does this need to be? Simple text game Somewhat similar to social media Irrational user Sarcasm Misspellings 5/6/13 16

17 RESOURCES Union College NLP Tutorial Top Down, Natural Lang Gen Basic Sentiment Analysis With Python U Illinois Chicago Opinion Mining Page U Illinois Chicago Opinion Mining Tutorial AAAI-2011.pdf Sentiment Analysis and Opinion Mining Book (full pdf) OpinionMining.pdf 5/6/13 17

18 QUESTIONS? 5/6/13 18

19 GAME AI Behavioral Hierarchy Control and Build the user s experience Experience has to be Dynamic Autonomous characters for gaming experiences Can also be used in military applications Behavioral is not scripted Must be believable and adaptable Create the illusion of life As soon as simulation does something unexpected scripted AI Fails Holy Grail to Reuse behaviors Example: GAIA (LM Product) Utility based/modular options Hand crafted, not machine learned Sequence Reasoner, interruptible 5/6/13 19