Proposal Daniel Michlits h Research Seminar System Analyses

Agenda  Related works  Problem statement  Movie Review Mining and Summarization  Experiments  Summary of the base article  Research questions

base article  title: „Movie Review Mining and Summarization“  authors: Li Zhuang, Feng Jing and Xiao- Yan Zhu published by: CIKM’06 November 5-11,2006, Arlington,Virginia

Summary Related works: Most of the existing work is focused on produkt reviews  Subjective classification distinguishes between sentences that contain objective information and sentences that describe opinions  Sentiment classification Defines the semantic orientation of words

Summary Problem statement  Definine classes for features  Divide the classes into two groups Elements: OA (overall), ST (screenplay), CH (character design), VP (vision effects), MS (music and sound),… People: PPR (producer), PDF (director), PSC (screenwriter), PAC (actor and actress),…

Summary Movie Review Mining and Summarization  Generating a Keyword list including the most important feature & opinion words relating the keywords to the element classes nouns, such as movie names or people names, can also be feature words  Mining explicit feature-opinion pairs Using the keyword list and dependency relation templates  Mining implicit feature-opinion pairs Only in short sentences at the beginning or end of the review

Summary Movie Review Mining and Summarization  Generating the final summary  Collecting sentences that express opinions on a specific feature class  Identifying semantic orientation of the relevant opinion in each sentence is identified  Creating an organized sentence list

Summary

Research Questions 1.How much are movie reviews influencing the decision of consumers? Do they also influence the perception during a movie? 2. Which keywords have the biggest impact on the consumer? 3.Which are the most important elements of movie reviews regarding the consumer purchase decision process?

Summary Movie Review Mining and Summarization  Generating the final summary  Collecting sentences that express opinions on a specific feature class  Identifying semantic orientation of the relevant opinion in each sentence is identified  Creating an organized sentence list