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November 16, 2016 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews Case Study on Theme Parks Youngmin Lee GIS/LBS LAB Seoul National University CONTENTS 01 Background and purpose of the study 02 How to construct spatial sentiment lexicon 03 Experiment and result 04 Conclusion 01 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks Background and purpose of the study The trend of these days The use of location-based services(LBS) is constantly increasing. and, reviewing and grading places through the use of LBS has also become a common practice among users. [ Airbnb review example ] [ Google review example ] Previous reviews of a place can significantly affect their potential visitors. 3 01 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks Background and purpose of the study Visitor’s sentiment expression A user-created review is the result of a visitor's actual positive or negative sentiment expression, and the sentiment could be expressed as a positive, negative, or neutral opinion. In order to perform sentiment analysis, each word should be separated by its POS (part-of-speech) through natural language processing. To do this, a database of spatial sentiment words should be constructed. auxiliary adverb verb verb adjective pronoun Noun Noun pronoun verb [ Google review example ] 4 01 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks Background and purpose of the study There is no spatial sentiment lexicon In the past, sentiment analysis has been used mainly for product reviews (Chang 2009, Hu & Liu 2004, Myung et al 2008, Scaffidi et al). No spatial sentiment lexicon for sentiment analysis of places has been constructed yet. Specifically, Korean lacks in terms of research on sentiment word analysis, compared to English. Korean, unlike English, has complicated characteristics and is composed of complex adjectives and suffixes (Jang et al. 2015). [ Korean(Hangeul) alphabet ] • Chang J (2009) A sentiment Analysis Algorithm for Automatic Product Reviews Classification in On-Line Shopping Mall. 14(4):19-33 • Hu M, & Liu B (2004) Mining and Summarizing Customer Reviews. In proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. 168-177 • Myung J, Lee D, & Lee S (2008) A Korean Product Review Analysis System Using a Semi-Automatically Constructed Semantic Dictionary. Journal of KIISE:Software and Applications. 35(6): 392-403 • Scaffidi C, Bierhoff K, Chang E, Felker M, Ng H, & Jin C (2007) Red Opal: Product-Feature Scoring from Reviews. In proceedings of the 8th ACM conference on Electronic commerce. 182-191 • Jang K, Park S, & Kim W (2015) Automatic Construction of a Negative/positive Corpus and Emotional Classification using the Internet Emotional Sign. Journal of KIISE. 42(4):512-521 5 01 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks Background and purpose of the study The purpose of the study Therefore, in this study, we propose a method to construct a spatial sentiment lexicon using place reviews written in Korean, and we focused primarily on a ‘theme parks’ out of the many possible place categories. Other types of spatial sentiment lexicon could be constructed using the same methodology. 6 02 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks How to construct spatial sentiment lexicon Which are sentiment words? 7 02 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks How to construct spatial sentiment lexicon 3 aspects to consider 1) The polarity of the sentiment word is analyzed. To do this, we calculated sentiment polarity and probability using the results of the survey. 2) It should be taken into account that some sentiment words are associated with properties of a place. many green Positive little people Negative There is 3) The spatial features and predicate characteristics of a place should be classified. We defined the combination of the spatial feature and the predicate as the ‘spatial feature sentence’. 8 02 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks How to construct spatial sentiment lexicon Research workflow Naver map search API of Google maps Collecting theme park POIs Collecting place reviews Natural language process (morphological analysis) Extracting candidate of spatial sentiment words Survey Vote Sentiment classification Positive Negative Neutral Combination of feature sentence Collective intelligence Calculation of sentiment probability Spatial sentiment lexicon [ Research workflow ] 9 03 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks Experiment and result Naver map search API of Google maps Collecting theme park POIs Collecting place reviews Natural language process (morphological analysis) Naver map searching Extracting candidate of spatial sentiment words Survey Vote Sentiment classification Positive Negative Neutral Combination of feature sentence Collective intelligence Calculation of sentiment probability Number of search results : 118 Spatial sentiment lexicon Theme park POIs Place reviews of Google maps Collecting reviews using Google API using java script Number of collected reviews : 204 10 03 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks Experiment and result Naver map search API of Google maps Collecting theme park POIs Collecting place reviews Natural language process (morphological analysis) Extracting candidates of spatial sentiment words through NLP using R Survey for sentiment classification Extracting candidate of spatial sentiment words Survey Vote Sentiment classification Positive Negative Neutral Combination of feature sentence Collective intelligence Calculation of sentiment probability Candidates of spatial sentiment words Spatial sentiment lexicon positive negative neutral inappropriate Survey result Sentiment polarity and probability calculation using R # Step 1: inappropriate if ((count(data[inappropriate]) > (count(data[positive]) && count(data[inappropriate]) > (count(data[negative]) && count(data[inappropriate]) > (count(data[neutral])){ Score = 100 * count(data[inappropriate]) / count(data[positive]) + count(data[negative] ) + count(data[neutral]) + count(data[inappropriate]) Sentiment = ‘inappropriate’} # Step 2: neutral_1 else if ((count(data[neutral]) > (count(data[positive]) && count(data[neutral]) > (count(data[negative])){ Score = 100 * count(data[neutral]) / count(data[positive]) + count(data[negative]) + co unt(data[neutral]) Sentiment = ‘neutral’} # Step 3: positive & negative else if ((count(data[positive]) > count(data[negative])){ Score = 100 * count(data[positive]) / count(data[positive]) + count(data[negative]) Sentiment = ‘positive’} else if ((count(data[negative]) > count(data[positive])){ Score = 100 * count(data[negative]) / count(data[positive]) + count(data[negative]) Sentiment = ‘negative’} # Step 4: when positive = negative = neutral else Score = 100 Sentiment = ‘neutral’ 11 03 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks Experiment and result The result of spatial sentiment lexicon_01 (single adjective, verb, and noun) • PA: Adjective, PV: Verb, NC: Noun • Positive: 1, Negative: -1, Neutral: 0 Naver map search API of Google maps Collecting theme park POIs Collecting place reviews Natural language process (morphological analysis) Extracting candidate of spatial sentiment words Survey Vote Sentiment classification Positive Negative Neutral Combination of feature sentence Collective intelligence Calculation of sentiment probability 100% positive words Spatial sentiment lexicon c 12 03 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks Experiment and result The result of spatial sentiment lexicon_01 (single adjective, verb, and noun) • PA: Adjective, PV: Verb, NC: Noun • Positive: 1, Negative: -1, Neutral: 0 Naver map search API of Google maps Collecting theme park POIs Collecting place reviews Natural language process (morphological analysis) Extracting candidate of spatial sentiment words Survey Vote Sentiment classification Positive Negative Neutral Combination of feature sentence Collective intelligence Calculation of sentiment probability Spatial sentiment lexicon Words determined by ‘inappropriate’ 13 03 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks Experiment and result The result of spatial sentiment lexicon_02 (combination with ‘many’ and ‘little’) • PA: Adjective, PV: Verb, NC: Noun • Positive: 1, Negative: -1, Neutral: 0 Naver map search API of Google maps Collecting theme park POIs Collecting place reviews Natural language process (morphological analysis) Extracting candidate of spatial sentiment words Survey Vote Sentiment classification Positive Negative Neutral Combination of feature sentence Collective intelligence Calculation of sentiment probability Spatial sentiment lexicon 14 03 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks Experiment and result The result of spatial sentiment lexicon_03 (combination of feature sentence) • PA: Adjective, PV: Verb, NC: Noun • Positive: 1, Negative: -1, Neutral: 0 Naver map search API of Google maps Collecting theme park POIs Collecting place reviews Natural language process (morphological analysis) Extracting candidate of spatial sentiment words Survey Vote Sentiment classification Positive Negative Neutral Combination of feature sentence Collective intelligence Calculation of sentiment probability Spatial sentiment lexicon 15 04 Method for Construction of Spatial Sentiment Lexicon Using Place Reviews: Case Study on Theme Parks Conclusion Contribution and future works Sentiment dictionaries have been constructed for analyzing product reviews, but no sentiment dictionary has been established for the places. Therefore, it is meaningful that this study constructed the sentiment lexicon for place and calculated the sentiment polarity and probability score of by word. The spatial sentiment lexicon could be utilized as a reference when performing sentiment analysis on the contents of various social media platforms, and could offer useful information to those who want to visit a place. In future work, we will study a method used to 1) Extending the lexicon by adding synonyms for pre-constructed sentiment words. 2) Developing the methodology to analyze syntax more precisely. 16 THANK YOU FOR LISTENING Youngmin Lee [email protected]