1. AMANDA COHEN MOSTAFAVI Applying Entity Discovery and Assignment to video games in order to mine opinions

2. Project Purpose Many differing opinions on a video game’s quality, difficult to determine general opinion Usually look to professional video game reviews Can gather review scores, normalize and average score in order to determine general consensus – Done on GameRankings.com However, this ignores the discussion by everyday players – Debate takes place most commonly on message boards

3. Project Purpose Solution: Mine opinions expressed on message board posts and derive a consensus from the results Using the algorithm for entity discovery and assignment and opinion mining as defined in this paper:  Entity Discovery and Assignment for Opinion Mining Applications. Xiaowen Ding, Bing Liu, Lei Zhang. SIGKDD, 2009

4. Goal: To mine opinions on selected games expressed on video game message boards, derive an average opinion and compare results to the review scores gathered by GameRankings.com

5. Games Total games examined: 10 All released in 2007 5 were top-selling games of the year, according to the NPD group (market research group that studies the video game industry, among other things) 5 are among the highest reviewed games according to GameRankings.com Ensures a mix of critically and commercially successful Note: Duplicate Games are removed

6. Games High selling – Halo 3 (360, Microsoft) - 4.82 million – Wii Play with Wii Remote (Wii, Nintendo) - 4.12 million – Call of Duty 4: Modern Warfare (360, Activision) - 3.04 million – Guitar Hero III: Legends of Rock (PS2, Activision) - 2.72 million – Super Mario Galaxy (Wii, Nintendo) - 2.52 million Highly Reviewed – The Orange Box (PC, Xbox 360) – 96% – BioShock (PC, Xbox 360) – 94% – Elder Scrolls IV: Oblivion (PS3) - 92% – God of War II (PS2) – 92% – Team Fortress 2 (PC) – 92%

7. Game Issues Alternate Names: Games are often referenced by shorthand or abbreviation Solution: include an array of possible alternate names in defining the entity object

8. Message Boards Principally from video game websites, or websites with large portions devoted to video games Looking at comments in relation to articles about top selling games or reviews to ensure that the posts are relevant to the games  Lots of comparative statements as well

9. Message Board Posts 1UP.com: 26 posts Gamespot.com: 14 posts IGN.com: 20 posts Total: 60 posts

10. Post Issues Unusual ways of expressing opinions: message board posters may not express their opinions in the same way as someone writing a review would. For instance:  “Call of Duty 4 was a very good game” <- this sentence would make for very easy opinion mining  “COD4 IS TEH WIN, OMG!!!!111” <- more likely on a message board, and much harder to mine Solution: The opinion mining algorithm allows for “opinion grammar”. More later…

11. The Process Implements Entity Discovery and assignment algorithm, with a couple modifications:  Entity discovery section reduced to better fit purposes of the project  Ordinarily would use pattern mining in order to find entities, not an issue in this case since there are a predetermined set of games examined

12. Data preprocessing Each word in every post is given a part-of-speech tag  Designates the grammatical role of each word  Done with Stanford’s POS tagger, developed by the Stanford Natural Language Processing group  http://nlp.stanford.edu/software/tagger.shtml http://nlp.stanford.edu/software/tagger.shtml A list of the entities used are created, and their alternate names are define

13. Entity Discovery and Assignment Each post is parsed to separate sentences and find each entity If entity is found, and matches the game title, the entity is assigned to that sentence If there is no entity, the entity of the previous sentence is assigned  Works on the assumption that when someone starts talking about an entity, subsequent sentences deal with the same entity without explicitly stating it If an alternate name for the entity is found, it is replaced with the original title to reduce future processing time

14. Opinion Grammar The original authors suggest that hard-coding every possible opinion words is not recommended Instead, they suggest using a system to define grammar that will pick out opinion words and statements A combination of hard-coded word list and grammar rules were used for this project  Hard coded words for regular English grammar, defined rules for more unexpected words and phrases

15. Indicator Word Symbols Po: Positive Ne: Negative Neu: Neutral Ng: Negation But: But-like

16. Opinion Mining Step 1: Apply indicator word symbols Step 2: Apply phrase rules Step 3: Search for negations, and change the opinion of the subsequent word (if it was positive, it would be negative and vice versa) Step 4: Aggregate opinions  Search for indicators, Po = 1, Ne = -1, Neu = 0

17. Comparative Sentences If a sentence has more than one entity, it is a comparative sentence  This sentence compares one entity to another, i.e. “Game-A is better than Game-B” In order to find the superior and inferior entities, look for comparative or superlative words (according to POS tags) and whether it’s a positive or negative word  If negative, the entity after the comparative word is superior. If positive the entity before the comparative word is superior

19. Up next: demo and results…