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Identifying people's affective responses to environments from social media data Haosheng Huang Research Group Cartography Vienna University of Technology, Austria Zürich, 26 May 2014 Introduction • Humans perceive and evaluate environments affectively. – Unsafe places, attractive places, … • Collecting these kinds of affective responses to environments enables many interdisciplinary applications. – Geography (GIScience), Environmental Psychology, Urban Planning, Architecture, Information and communications technology (ICT), Policy Making, … Affect: definition and modeling • Affect refers to the experience of feeling or emotion in our everyday lives: “How do you feel [about …]” • Modeling of affective responses – Approach 1: defining basic distinct affective responses such as happiness, anger, fear, disgust, and sadness (Ekman and Friesen 1971) – Approach 2: describing affective responses on the dimensions of valence and arousal (Russell 2003) • Valence (“the hedonic tone of feeling”): pleasantunpleasant, comfortable-uncomfortable, positivenegative, … • Arousal (“a sense of mobilization or energy”): activationdeactivation • Barrett et al. (2006): valence is a basic component of affective responses Social media data: Flickr Photo title: “Boring sign” Photo description: “A dark and beautiful church in Wien” Photo description: Some really nice apartments Photo description: “this is a stressful station” • Research Question: How can people’s affective responses to the environment be extracted from social media data? – Flickr photo metadata: titles and descriptions (not the photos) • Using geotagged photos (with lat./lon.) – Affective responses: focusing on valence (positive-negative) Sentiment analysis • Sentiment analysis, opinion mining – Computational study of opinions, sentiments, evaluations, attitudes, affects, views, emotions, etc., expressed in text. • Text: Reviews, blogs, discussions, news, comments, feedback, … – Machine learning approaches • Positive sentiment vs. Negative sentiment • Naive Bayes, maximum entropy – Lexicon-based approaches (keyword-based) • Natural Language Processing pipeline – tokenize & lemmatize • Word lexicon – ANEW: Affective Norms for English Words – AFINN – EMOT • “This is a terrible building.” : 1.93 (very negative) Affective responses in Flickr photo metadata • Kisilevich et al. (2010): the adjective (adj) – noun pattern – A beautiful and interesting place, a dirty street, … • We are interested in people‘s affective responses to the environment. – “This was taken with my wonderful camera. What a boring tower.” – Wonderful camera, boringtower – Affective response to this environment (spatial object: tower): boring (2.82, negative) Methodology (1) • 1. For each geotagged photo‘s title and description: tokenize, lemmatize, POS tag, remove stop words – Results of this step are a list of words. – Stanford CoreNLP library For each geotagged photo, extract its title and description Apply NLP: Tokenize, Lemmatize, remove stop words Extract adjective-noun sets Filter out adj-noun sets that are not placerelated Compute valence, using affective lexicon and WordNet (synonyms) Methodology (2) • 2. Extract adjective-noun sets – Part-of-speech (POS) tagging, adjectival modifier (amod) – Stanford CoreNLP library – “interesting building” = amod (interesting, building) For each geotagged photo, extract its title and description Apply NLP: Tokenize, Lemmatize, remove stop words Extract adjective-noun sets Filter out adj-noun sets that are not placerelated Compute valence, using affective lexicon and WordNet (synonyms) Methodology (3) • 3. Filter out adj-noun sets that are not place-related – English place nouns: building, street, restaurant, park, museum, opera... – Study-area-specific placenames: Stephansdom, Karlskirche, … • Placenames from GeoNames (http://www.geonames.org/) For each geotagged photo, extract its title and description Apply NLP: Tokenize, Lemmatize, remove stop words Extract adjective-noun sets Filter out adj-noun sets that are not placerelated Compute valence, using affective lexicon and WordNet (synonyms) Methodology (4) • 4. Compute valence – Using affective lexicon • ANEW: 2476 words • AFFIN: 2477 words • ANEWplusAFFIN: 4426 words – For each adjective in adj-noun sets, look up its valence in ANEWplusAFFIN. – If not found, use WordNet Library to get synonyms of the adjective, and look up the valence of the synonyms. – Average all the adjectives‘ valence, and assign the result as the valence value of this photo For each geotagged photo, extract its title and description Apply NLP: Tokenize, Lemmatize, remove stop words Extract adjective-noun sets Filter out adj-noun sets that are not placerelated Compute valence, using affective lexicon and WordNet (synonyms) • Case study (Vienna) • January 2007 and October 2011 • 107.353 data rows (only using geotagged photos) • focusing on English posts (57% of all posts) Results Freyung & Am Hof Vienna – mood map example Results • 107.353 data rows • January 2007 and October 2011 • only using English posts (57% of all posts) Summary • A methodology was created to extract people’s affective responses to the environment form Flickr user posts (titles and descriptions of geotagged photos). • It is able to differentiate between “affective responses to the environment” and “affective responses to other aspects (camera)” – “This was taken with my wonderful camera. What a boring tower.” Work in progress • Improving the methodology – Extended for other languages – “I feel good in Karlsplatz.” – Data quality: Validating location of user post • Factors influencing affective responses – “What makes people feel comfortable?” “What makes an environment attactive/unattractive?” – Environmental characeristics, place semantics, … • Correlating spatial behavior and affect Thank you! & Comments? Haosheng Huang Research Group Cartography Vienna University of Technology http://cartography.tuwien.ac.at http://tiny.cc/hhuang [email protected] 16
