৘ใॲཧֶձ‫ڀݚ‬ใࠂ
IPSJ SIG Technical ReportιʔγϟϧϝσΟΞΛ༻͍ͨґଘ঱ऀͷൃ‫ݴ‬෼ྨͱͦͷۭؒ෼ੳଜࢁ ଠҰ1ए‫ ٶ‬ᠳࢠ1ߥ຀ ӳ࣏1֓ཁɿ͜Ε·Ͱɼ౎ಓ෎‫ݝ‬ΑΓখ͞ͳ୯ҐͰͷӸֶௐࠪ͸ࠔ೉Ͱ͋ͬͨɽ͜ͷͨΊɼχίνϯґଘ঱ͳͲ‫ڥ؀‬ͷӨ‫ڹ‬Λड͚Δ࣬‫ױ‬ͷ৔߹ɼ࣏ྍʹ͓͍ͯࢀߟͱͳΔ౷‫ࢦܭ‬ඪ͕ଘࡏ͠ͳ͍͜ͱ͕ଟ͔ͬͨɽຊ‫ڀݚ‬Ͱ͸ɼχίνϯɼΞϧίʔϧɼΪϟϯϒϧͱ͍͏ 3 ͭͷґଘ঱Λ୊ࡐʹɼιʔγϟϧϝσΟΞ͔Βґଘ঱ऀͷॴࡏ஍ͷৄࡉͳۭؒత৘ใΛநग़͠ɼ͜ΕΛՄࢹԽ͢Δ͜ͱΛ໨ࢦ͢ɽ·ͣɼιʔγϟϧϝσΟΞ্ͷґଘ঱ऀͱඇґଘ঱ऀͷൃ‫ݴ‬Λ෼ྨ͢ΔͨΊʹɼΫϥ΢υιʔγϯά͔ΒಘΒΕٖͨࣅπΠʔτσʔλΛֶशσʔλͱ͠ɼRecurrent Convolutional Neural Network Λ༻͍ͯ෼ྨ‫ث‬Λߏங͢Δɽ࣍ʹɼߏஙͨ͠෼ྨ‫ث‬Λ༻͍ͯɼҐஔ৘ใ෇͖πΠʔτ 12,237 ݅Λґଘ঱ऀͱඇґଘ঱ऀͷൃ‫ʹݴ‬෼ྨ͠ɼ஍ਤ্ʹϚοϐϯάͯͦ͠ΕͧΕͷ஍ཧత෼෍ΛՄࢹԽ͢Δɽχίνϯґଘ঱Λର৅ʹ࣮ͨ͠‫Ͱݧ‬͸ɼ٤ԎऀͷதͰ΋χίνϯґଘ঱ऀͷൃ‫ݴ‬͸ӺΛத৺ͱͨ͠ൟ՚֗ʹू·Δ܏޲͕͋ΔͳͲɼ௚‫ײ‬తʹଥ౰ͳ݁Ռ͕ಘΒΕͨɽࠓ‫ޙ‬ɼ͜ͷՄࢹԽͷଥ౰ੑͷৄࡉͳ‫ݕ‬౼΍‫༻׆‬Λߦ͏༧ఆͰ͋ΔɽΩʔϫʔυɿӸֶௐࠪɼґଘ঱ɼιʔγϟϧϝσΟΞɼΫϥ΢υιʔγϯάɼTwitterɼࣗવ‫ॲޠݴ‬ཧ

Addiction-related Tweet Classification and Spatial Analysis
Taichi Murayama1

Shoko Wakamiya1

Eiji Aramaki1

Abstract: The epidemiological survey is conducted for each prefecture and much more detailed surveys are
not done. Addiction in the epidemiology largely depends on environment. It is important for addiction
treatment to figure out the relationship between addicts and behavioral environment. The purpose of this research is to visualize and analyze addicts’ behavior and environment in detail. Firstly, we construct a classifier
based on Recurrent Convolutional Neural Network from quasi-tweet data obtained through crowdsourcing,
to distinguish addicts’ tweets from non-addicts’ tweets in social media. Next, we visualize a geographic
distribution of the classified tweets with location information on a map. The geographic distribution of thetweets by nicotine addicts suggests a pattern that nicotine addicts tend to gather in busy streets centeredon stations than non-nicotine addicts. In future work, we plan to analyze individual tweets on a long termbasis for figuring out characteristic patterns of addicts’ behavior.
Keywords: Epidemiological study, Addiction, Social media, Crowdsourcing, Twitter, Natural Language
Processing

1. ͸͡Ίʹ

ͷ 68.6 ˋͷ໿ 1,487 ສਓ [2] ʹͷ΅Δͱߟ͑ΒΕ͓ͯΓɼଟ͘ͷ೔ຊਓ͕Կ͔͠Βͷ෺࣭ͷґଘ঱ʹͳ͍ͬͯΔͱ͍ґଘ঱ʢҰൠʹ͸ɼதಟͱ‫ݺ‬͹ΕΔ͜ͱ΋͋Δ͕ɼຊߘ͑Δɽ͜ͷΑ͏ͳґଘ঱ʹର͢Δௐࠪ͸ूஂΛର৅ͱ͢ΔͰ͸ґଘ঱ͱ‫Ϳݺ‬ʣ͸ɼ෺࣭తʹ๛͔Ͱ͋Δઌਐࠃʹ‫ڞ‬௨Ӹֶͷख๏ΛऔΒΕ͓ͯΓɼ౎ಓ෎‫ݝ‬୯ҐͰґଘ঱ऀͷ࣮ͨ͠େ͖ͳࣾձ໰୊ͷҰͭͰ͋Δɽྫ͑͹ɼΞϧίʔϧґଶΛௐࠪ͢Δͷ͕ҰൠతͰ͋Δɽ͔͠͠ɼґଘ঱͸ߦಈ΍ଘ঱ऀ͸೔ຊͰ໿ 58 ສਓ [1]ɼχίνϯґଘ঱ऀ͸٤Ԏऀ‫͍ͨͬͱڥ؀‬ཁૉͱͷؔ࿈ੑ͕‫͔ͱ͍͜ڧ‬ΒɼҰൠతͳӸ1ֶௐࠪҎ্ʹɼΑΓৄࡉͳۭؒ෼ੳΛߦ͍ɼґଘ঱ऀͷߦಸྑઌ୺Պֶٕज़େֶӃେֶc 1959 Information Processing Society of Japan⃝1৘ใॲཧֶձ‫ڀݚ‬ใࠂ
IPSJ SIG Technical Reportಈͱ‫ͱڥ؀‬ͷؔ܎Λղ͖໌͔͢͜ͱ͕ॏཁͰ͋Δͱߟ͑ΒΕΔɽࠓճɼैདྷͷ౷‫ܭ‬ख๏Ͱ͸औಘͰ͖ͳ͔ͬͨΑΓৄࡉͳ

2. ؔ࿈‫ڀݚ‬
2.1 ༷ʑͳґଘ঱ґଘ঱ऀͷ৘ใऔಘͷͨΊʹɼιʔγϟϧϝσΟΞσʔλґଘ঱ʹ͸χίνϯɼΞϧίʔϧɼΪϟϯϒϧͱ͍ͬͨΛ‫͢༻׆‬ΔɽҰൠతʹιʔγϟϧϝσΟΞσʔλͷར༻༷ʑͳछྨ͕͋Δɽ͜Ε·Ͱɼґଘ঱ऀͰ͸ͳ͘୯ͳΔ٤͸ɼਓʑͷൃ‫ݴ‬΍ৼΔ෣͍ʹؔ͢ΔσʔλΛେ‫ن‬໛ʹऔಘԎऀ΍ҿञऀͷಈ޲ͷ೺Ѳ΍‫ې‬Ԏ‫ܒ‬໤‫׆‬ಈͱ͍ͬͨ໨తͰ͖Δ͜ͱ΍ɼϦΞϧλΠϜͳҐஔ৘ใΛऔಘͰ͖Δ͜ͱͰɼιʔγϟϧϝσΟΞΛ༻͍ͨ‫ߦ͕ڀݚ‬ΘΕ͖ͯͨɽ͕ಛ௃ͱͯ͠‫͛ڍ‬ΒΕɼ͜ΕΒͷར఺ʹண໨ͨ͠‫͕ڀݚ‬ଟχίνϯͱιʔγϟϧϝσΟΞΛѻͬͨ‫ͯ͠ͱڀݚ‬͸ɼ͍ɽຊ‫Ͱڀݚ‬͸ɼґଘ঱ऀͷൃ‫ݴ‬ͷҐஔ৘ใͷՄࢹԽΛߦλόίؔ࿈ͷൃ‫ݴ‬ΛδϟϯϧɼςʔϚ΍‫ײ‬৘ͱ͍ͬͨ‫఺؍‬͍ɼґଘ঱ऀͷߦಈൣғͳͲͷ৽ͨͳ஌‫ݟ‬ͷऔಘΛ໨ඪͱͰ‫ػ‬ցֶशΛ༻͍ͯ෼ྨ͠ɼλόίؔ࿈ͷ༻‫Ͳ͕ޠ‬ͷΑ͏͢ΔɽͳਓʑʹΑͬͯͲͷΑ͏ʹ༻͍ΒΕΔ͔Λ෼ੳ͢Δ͜ͱͰɼ·ͣɼΫϥ΢υιʔγϯάͰґଘ঱ऀͷςΩετσʔλެऺӴੜʹؔ͢Δӡಈ΁ͷར༻Մೳੑʹ͍ͭͯߟ࡯ͨ͠΋Λऔಘ͠ɼͦͷσʔλΛ‫ʹݩ‬ɼґଘ঱ऀऀͷൃ‫ݴ‬Λ෼ྨ͢

ͷ [3] ΍ɼϥςϯ‫ܥ‬ΞϝϦΧਓΛର৅ʹιʔγϟϧϝσΟΔ෼ྨ‫ث‬ͷ࡞੒Λߦ͏ɽ࣍ʹɼ෼ྨ‫ث‬Λ༻͍ͯιʔγϟϧΞΛ༻͍ͨ‫ې‬ԎΩϟϯϖʔϯʹؔ͢Δߟ࡯Λߦͬͨ΋ͷ [4]ϝσΟΞͷϢʔβʹΑΔൃ‫ݴ‬Λ෼ྨ͠ɼґଘ঱ऀͷۭؒతͳͲ͕‫͛ڍ‬ΒΕΔɽଞʹ΋ιʔγϟϧϝσΟΞͱλόίʹ෼ੳΛߦ͏ɽਤ 1 Ͱຊ‫ڀݚ‬ͷશମਤΛද͢ɽؔ࿈ͨ͠ଟ͘ͷ‫͕͋ڀݚ‬Γɼৄ͘͠͸γεςϚςΟοΫϨຊ‫ڀݚ‬ͷ৽‫ن‬ੑ͸ҎԼͷ 2 ఺Ͱ͋ΔɽϏϡʔ [5] ʹ·ͱΊΒΕ͍ͯΔɽ͔͠͠ɼ٤Ԏऀʹয఺Λ( 1 ) Ϋϥ΢υιʔγϯάʹΑΔٙࣅπΠʔτΛ༻͍ͯɼґଘ঱ऀͷൃ‫ʹݴ‬ର͢Δ෼ྨ‫ث‬Λߏஙͨ͠఺౰ͯͨ‫ڀݚ‬͸ଟ͋͘Δ΋ͷͷɼґଘ঱ऀʹয఺Λ౰ͯͨ΋ͷ͸ɼґଘ঱ऀͱඇґଘ঱ऀͷ൑ผ͕೉͍ͨ͠Ίଟ͘ͳ͍ɽ

( 2 ) ͜Ε·ͰΑ͘஌ΒΕ͍ͯͳ͔ͬͨґଘ঱ऀͷ஍ཧత෼Ξϧίʔϧʹؔͯ͠͸ɼΞϧίʔϧઁऔऀͷߦಈͷ܏޲෍ΛιʔγϟϧϝσΟΞσʔλΛར༻ͯ͠໌Β͔ʹ͠ΛιʔγϟϧϝσΟΞσʔλ͔ΒಡΈऔΔ΋ͷͳͲ͕͋

ͨ఺Δɽ໰୊ͱͳΔҿञߦҝͷπΠʔτͷ࣌ؒత෼ੳΛߦͬͨ΋ͷ [6] ΍ɼΠϯελάϥϜͷࣸਅ͔ΒҿΈա͗ͷ൑ఆΛߦ͏΋ͷ [7] ͳͲͷ‫͛ڍ͕ڀݚ‬ΒΕΔɽ͔͠͠ɼ٤Ԏऀʹؔ͢Δ‫ͱڀݚ‬ಉ༷ͷཧ༝͔ΒɼιʔγϟϧϝσΟΞΛ༻͍ͨΞϧίʔϧґଘ঱ʹয఺Λ౰ͯͨ‫ڀݚ‬͸‫ݟ‬ΒΕͳ͍ɽ

クラウドソーシングデータΪϟϯϒϧʹؔͯ͠͸ɼΪϟϯϒϧ͕੝ΜͳΞϝϦΧͰ͸ιʔγϟϧϝσΟΞΛ௨ͨ͡‫͕ڀݚ‬ଟ͘ߦΘΕ͍ͯΔɽͦͷதͰ΋ɼωοτΪϟϯϒϧ͕࠷ۙ੝ΜͰ͋Δ͜ͱ͔Βɼ

依存症・非依存症
データ抽出!"#$$%&データ

学習ιʔγϟϧϝσΟΞͷར༻ͱωοτΪϟϯϒϧͷؔ܎Λ෼ੳͨ͠‫[ ڀݚ‬8] ͳͲ͕‫ݟ‬ΒΕΔɽ

依存症分類器2.2 ґଘ঱ͱӸֶ分類ҩྍ෼໺ʹ͓͚Δґଘ঱‫ڀݚ‬͸ɼ‫ݸ‬ਓͷ࣏ྍʹয఺Λ౰ͯͨ΋ͷ͕ଟ͍ɽ͔͠͠ɼґଘ঱ͷ࣏ྍʹ͸ɼଞͷґଘ঱
依存症者発言非依存症者発言ऀͱͷ઀৮ͳͲ‫͕ڥ؀‬ॏཁͳཁҼ [9] ͱ͞Ε͓ͯΓɼର৅ΛूஂͱΈͳ͢େ͖ͳࢹ఺΋ॏཁͰ͋Δɽ

可視化͜ͷΑ͏ʹ‫ऀױ‬ΛूஂͰѻ͏ֶ໰ͱͯ͠͸ɼӸֶͱ‫ݺ‬͹ΕΔ෼໺͕͋ΔɽӸֶ͸ɼूஂΛର৅ʹ࣬පͷൃੜ‫ݪ‬Ҽ΍༧๷ͳͲΛ‫͢ڀݚ‬Δֶ໰Ͱ͋Γɼओʹ‫ײ‬છ঱͕ѻΘΕΔ͕ɼަ௨ࣄ‫Ͳͳނ‬ͷਓࡂɼ஍਒΍Րࡂͱ͍ͬͨఱࡂɼප‫ؾ‬΍ґଘ঱ͳͲɼਓؒͷ݈߁ΛଛͶΔ‫ݪ‬ҼͱͳΔର৅Λ෯޿͘ѻ͍ͬͯΔɽӸֶʹ͓͍ͯɼґଘ঱͕ѻΘΕΔ͜ͱ͸·ͩগͳ͍͕ɼΞϧίʔϧґଘ঱΍χίνϯґଘ঱͸ɼߦಈ΍‫͍ͨͬͱڥ؀‬ཁૉͱͷؔ࿈ੑ͕‫͔ͱ͍͜ڧ‬Βɼຊ‫Ͱڀݚ‬ਤ 1શମਤ͸ɼӸֶͱಉ༷ʹ஍Ҭ͝ͱͷಛੑΛௐࠪ͢Δɽͨͩ͠ɼҰൠతͳӸֶௐࠪͰߦΘΕΔ౎ಓ෎‫ͱ͝ݝ‬ͷௐࠪΑΓ΋ɼΑΓৄࡉͳҐஔ৘ใͷ෼ੳΛࢼΈΔɽ

c 1959 Information Processing Society of Japan⃝

2৘ใॲཧֶձ‫ڀݚ‬ใࠂ
IPSJ SIG Technical Reportங͢Δɽ

3. ख๏ɿґଘ঱ʗඇґଘ঱ͷ෼ྨ‫ߏث‬ங

Yahoo!Ϋϥ΢υιʔγϯά*1 Ͱ্‫ه‬ͷઃ໰Λ༻͍ͯɼ֤ຊ‫ڀݚ‬͸ɼґଘ঱ऀͷߦಈ෼ੳΛιʔγϟϧϝσΟΞґଘ঱ʹ͖ͭ 1000 ਓ෼ͷςΩετσʔλͷऔಘΛߦͬͨɽσʔλΛར༻ͯ͠ߦ͏͜ͱΛ໨తͱ͍ͯ͠ΔɽͦͷͨΊʹͳ͓ɼνΣοΫઃ໰ʹΑΓɼෆద੾ͳϢʔβͷϑΟϧλϦґଘ঱ऀ͔Ͳ͏͔Λ൑ผ͢Δඞཁ͕͋ΔɽຊষͰ͸ɼιʔϯάΛߦͳͬͨɽ·ͨɼʮπΠʔτ͠ͳ͍ʯ΍ʮᄁ͔ͳ͍ʯγϟϧϝσΟΞʹ͓͚ΔϢʔβͷൃ‫ݴ‬Λґଘ঱ऀʹΑΔ΋ͱ͍ͬͨൃ‫ݴ‬͸ϊΠζͱͯ͠໨ࢹʹͯ࡟আΛߦͬͨɽͦͷͷͱඇґଘ঱ऀʹΑΔ΋ͷʹ෼ྨ͢ΔͨΊͷ෼ྨ‫ث‬ͷߏங݁ՌɼχίνϯͷΫϥ΢υιʔγϯάͰ͸ 2,401 ൃ‫ݴ‬ɼΞʹ͍ͭͯड़΂ΔɽϧίʔϧͷΫϥ΢υιʔγϯάͰ͸ 2,683 ൃ‫ݴ‬ɼΪϟϯϒ·ͣɼ෼ྨ‫࡞ث‬੒༻ͷֶशσʔλͱͯ͠ɼΫϥ΢υιʔγϯάΛ༻͍ͯऩूͨ͠ςΩετσʔλΛ঺հ͢Δ (3.1ϧͷΫϥ΢υιʔγϯάͰ͸ 1,949 ൃ‫ݴ‬ΛͦΕͧΕऔಘ͠
ͨɽද 2 ʹऔಘٖͨ͠ࣅπΠʔτͷ౷‫ྔܭ‬Λࣔ͢ɽઅ)ɽ࣍ʹɼ࣮૷ͨ͠෼ྨϞσϧΛड़΂ (3.2 અ)ɼ࠷‫ʹޙ‬෼ྨ‫ث‬ͷਫ਼౓ (3.3 અ) ͱͦͷߟ࡯ (3.4 અ) Λड़΂Δɽ

ද 2

3.1 ར༻σʔλґଘ঱ऀͱඇґଘ঱ऀͷ۠ผΛߦ͍ͳ͕ΒɼେྔͷςΩൃ‫ݴ‬औಘ਺χίνϯɹΞϧίʔϧΪϟϯϒϧ

औಘൃ‫਺ݴ‬2,4012,6831,949ʢ͏ͪɼґଘ঱ऀൃ‫਺ݴ‬ʣ

(1,808)(243)(921)ετσʔλΛऩू͢ΔͨΊʹɼΫϥ΢υιʔγϯάΛར༻ͨ͠ɽࠓճ͸ɼ୅දతͳ 3 ͭͷґଘ঱Ͱ͋Δχίνϯґଘ঱ɼΞϧίʔϧґଘ঱ɼ͓ΑͼΪϟϯϒϧґଘ঱ʹؔ͢ΔςΩετσʔλͷऩूΛߦͬͨɽௐࠪର৅ͱͳΔґଘ঱ʹؔ͢Δߦҝɼͭ·Γχίνϯґଘ঱ͳΒ͹٤ԎɼΞϧίʔϧґଘ঱ͳΒ͹ҿञɼΪϟϯϒϧґଘ঱ͳΒ͹ΪϟϯϒϧΛ೔ৗతʹߦ͍ͬͯΔऀΛௐࠪର৅ͱͨ͠ɽ೥ྸ΍ੑผɼࡏॅ஍ͳͲͷ‫ج‬ຊ৘ใɼґଘ঱͔Ͳ͏͔ͷ൑ఆઃ໰ɼςΩετσʔλΛಘΔͨΊͷ࣭໰ΛͦΕͧΕઃఆͨ͠ɽґଘ঱͔Ͳ͏͔ͷ൑ఆʹ࢖༻͢ΔࢦඪΛද 1 ʹࣔ͢ɽ

3.2 ࣮૷ϞσϧΫϥ΢υιʔγϯάͰऩूͨ͠ςΩετσʔλΛֶश
σʔλͱͯ͠ɼιʔγϟϧσʔλ্ͷൃ‫͔ݴ‬Βґଘ঱ऀʹΑΔൃ‫͔ݴ‬ඇґଘ঱ऀͷൃ‫͔ݴ‬Λ൑ผ͢Δ෼ྨϞσϧΛ࡞੒͢Δɽσʔλͷલॲཧͱͯ͠ϫʔυΤϯϕσΟϯάʹ͸୯‫ޠ‬ΛϕΫτϧԽ͢Δ Word2Vec Λ༻͍ͨɽ࣮૷Ϟσϧʹ͸ɼ

Recurrent Convolutional Neural Network (RCNN)[13] ΛӅΕ૚Λ 100 ࣍‫ͯ͠ͱݩ‬ར༻ͨ͠ɽRCNN ͸ɼจষதͷ֤

ද 1 ґଘ঱൑ఆͷ‫ج‬४ର৅൑ఆࢦඪ‫ج‬४఺୯‫͕ޠ‬Ґஔ͢Δ৔ॴʹΑͬͯόΠΞε͕͔͔Δ Recurrentχίνϯґଘ঱TDS [10]5 ఺Ҏ্Neural Network Λվྑͨ͠ϞσϧͰ͋ΓɼΑΓจ຺ΛऔΞϧίʔϧґଘ঱

AUDIT [11]20 ఺Ҏ্

ಘ͠΍͍͢ͱ͍͏ར఺͕͋Δɽ࣮૷͸ɼscikit-learn*2 ͱΪϟϯϒϧґଘ঱

SOGS [12]5 ఺Ҏ্keras*3 ͷϥΠϒϥϦΛར༻ͯ͠ߦͬͨɽ

χίνϯґଘ঱༻ͷςΩετσʔλΛऩू͢ΔͨΊʹɼ

✓

3.3 ݁Ռ( 1 ) ΋ ͠ ɼ͋ ͳ ͨ ͕ π Π ο λ ʔ Ͱ✏ ΩετσʔλΛ 5 ෼ׂ͠ަࠩ‫ূݕ‬Λߦͬͨɽ݁ՌɼΞϧλόίΛָ͘͠‫͍ͯͬٵ‬Δঢ় ‫ گ‬Λ ใ ࠂ ͢ Δ঱෼ྨ‫ث‬ͷ Accuracy ͸ 63.6%ɼΪϟϯϒϧґଘ঱෼ྨ‫ث‬ͱ͢Ε͹ɼͲͷΑ͏ʹॻ͖·͔͢?

ͷ Accuracy ͸ 53.3%Ͱ͋ͬͨʢද 3ʣɽҎԼͷΑ͏ͳ࣭໰Λઃఆͨ͠ɽ

࣮૷Ϟσϧͷਫ਼౓Λ֬ೝ͢ΔͨΊʹɼґଘ঱͝ͱʹςίʔϧґଘ঱෼ྨ‫ث‬ͷ Accuracy ͸ 85.8%ɼχίνϯґଘ( 2 ) ΋ ͠ ɼ͋ ͳ ͨ ͕ π Π ο λ ʔ Ͱຊ‫͚͓ʹڀݚ‬Δ෼ྨ‫࡞ث‬੒ͷ໨త͸ɼιʔγϟϧϝσΟλόίΛ‫͍ͳ͑ٵ͍ͯͨ͘ٵ‬ঢ় ‫ گ‬Λ ใ ࠂ ͢Ξσʔλ͔Βґଘ঱ऀͷൃ‫ݴ‬Λநग़͢Δ͜ͱͰ͋ΔͨΊɼΔͱ͢Ε͹ɼͲͷΑ͏ʹॻ͖·͔͢?

ద߹཰͕ॏཁͰ͋Δɽͦ͜Ͱɼద߹཰ʹண໨͢ΔͱɼΞϧ( 3 ) ΋͠ɼ͋ͳ͕ͨπΠολʔͰλόί͕େ޷͖ͩͱ

ίʔϧґଘ঱ʹؔͯ͠͸ 24.3%ɼχίνϯґଘ঱ʹ͍ؔͯ͠͏͜ͱΛओு͢ΔͳΒɼͲͷΑ͏ʹॻ͖·͔͢?

͸ 77.6%ɼΪϟϯϒϧґଘ঱ʹؔͯ͠͸ 43.0%Ͱ͋ͬͨɽ

( 4 ) ΋͠ɼ͋ͳ͕ͨπΠολʔͰλόίͷྑ͍఺Λ‫ޠ‬͜ͷ݁Ռ͔ΒɼΞϧίʔϧґଘ঱ͱΪϟϯϒϧґଘ঱Ͱ͸ɼ✒ΔͳΒ͹ɼͲͷΑ͏ʹओு͠·͔͢?

✑ґଘ঱ऀͷൃ‫ݴ‬ͷࣝผ͸ࠔ೉Ͱ͋Δ͕ɼχίνϯґଘ঱ʹରͯ͠͸࣮༻ՄೳͰ͋Δͱ͍͑Δɽଞͷґଘ঱ʹରͯ͠͸ɼ্‫ه‬ͷԼઢ෦Λมߋͨ͠ઃ໰Λ

༻͍Δɽ͜͜ͰಘΒΕͨςΩετσʔλΛιʔγϟϧϝσΟΞ্ͷ‫ݸ‬ਓͷٖࣅతͳൃ‫ͱݴ‬Έͳ͠ɼֶशσʔλΛߏc 1959 Information Processing Society of Japan⃝

*1*2
*3

https://crowdsourcing.yahoo.co.jp/
http://scikit-learn.org/stable/
https://keras.io3৘ใॲཧֶձ‫ڀݚ‬ใࠂ
IPSJ SIG Technical ReportσʔλΛ෼ྨͨ͠ɽ͜ͷ݁Ռɼ5,246 πΠʔτΛχίνϯ

ද 3 ෼ྨਫ਼౓Accuacy ద߹཰࠶‫཰ݱ‬

F1-scoreΞϧίʔϧґଘ঱

85.8%

24.3%

15.5%

0.189

χίνϯґଘ঱

63.6%

77.6%

75.1%

0.763

Ϊϟϯϒϧґଘ঱

53.3%

43.0%

49.4%

0.460

ର৅

ґଘ঱ऀͷൃ‫ݴ‬ɼ6,991 πΠʔτΛඇґଘ঱ऀͷൃ‫͠ͱݴ‬ͯ෼ྨͨ͠ɽ

4.2 Ґஔ৘ใʹ‫͍ͨͮج‬ՄࢹԽͱͦͷߟ࡯χίνϯґଘ঱ऀͷൃ‫ݴ‬෼෍ΛՄࢹԽ͢ΔͨΊʹ Google

3.4 ߟ࡯݁Ռʹࣔͨ͠௨ΓɼΞϧίʔϧґଘ঱ͱΪϟϯϒϧґଘ঱ͷ෼ྨ‫ث‬͸௿͍ Accuracy Ͱ͋ͬͨɽΞϧίʔϧґଘ঱ʹؔͯ͠͸ɼଞͷґଘ঱ͱൺֱ͠ɼҿञ͢Δਓͷઈର਺͕ଟ͍ͨΊɼΫϥ΢υιʔγϯάͰ͸ґଘ঱ऀͷे෼ͳαϯϓϧ਺ΛऔಘͰ͖ͳ͔ͬͨ͜ͱ͕‫ݪ‬ҼͰ͋Δͱߟ͑ΒΕΔɽΞϧίʔϧ͓Αͼχίνϯґଘ঱͸෺࣭తґଘ঱Ͱ͋Γɼͦͷ෺࣭ͷઁऔΛߦΘͳ͍ͱΠϥΠϥͳͲͷ཭୤঱ঢ়ΛҾ͖‫ͨ͢͜ى‬ΊςΩετσʔλʹ΋͜ΕΛ൓өͨ͠ൃ‫( ݴ‬1)(2) ͕ଟ͍ɽҰํͰɼΪϟϯϒϧґଘ঱͸෺࣭తґଘ঱ͱൺֱ͠཭୤঱ঢ়͕গͳ͍ͨΊɼςΩετσʔλʹ΋཭୤঱ঢ়Λද‫͢ݱ‬ΔΑ͏ͳൃ‫( ݴ‬3) ͕গͳ͍ɽͦͷͨΊɼґଘ঱ऀͱඇґଘΞϧίʔϧͱΪϟϯϒϧʹର͠ɼχίνϯґଘ঱ʹؔͯ͠͸ɼΫϥ΢υιʔγϯάͰे෼ͳґଘ঱ऀͷαϯϓϧ਺ΛऔಘͰ͖ɼ཭୤঱ঢ়΋໌֬Ͱ͋Δɽ͜ͷ͜ͱ͔Βɼχίνϯґଘ঱ʹؔͯ͠͸Ұఆͷਫ਼౓Λ࣋ͭ෼ྨ‫ث‬ͷ࡞੒Λߦ͏͜ͱ͕Ͱ͖ͨͱߟ͑ΒΕΔɽͦΕͧΕͷґଘ঱ऀͷൃ‫ྫݴ‬ΛҎԼʹࣔ͢ɽ( 1 ) ҿΈͨͯͨ͘·Βͳ͍ɺञΛ౪ΜͰ͠·͏͔΋...

ϯґଘ঱ऀʹΑΔൃ‫ݴ‬Λ੺ʙ྘ɼχίνϯʹؔ࿈ͨ͠ൃ‫ݴ‬ͷ͏ͪඇґଘ঱ऀʹΑΔൃ‫ݴ‬Λೱ੨ʙ୶੨Ͱද‫͢ݱ‬Δɽ
ਤ 2 ͸౦‫౎ژ‬શମʹ͓͚Δχίνϯؔ࿈ͷൃ‫ݴ‬෼෍ΛՄࢹԽͨ݁͠Ռɼਤ 3 ͸େࡕ෎શମʹ͓͚Δൃ‫ݴ‬෼෍ΛՄࢹԽͨ݁͠ՌͰ͋ΔɽӺΛத৺ͱͨ͠ൟ՚֗ΛΑΓৄࡉʹࣔͨ͠ਤ 2(a) ͱਤ3(a) Λ‫ݟ‬Δͱɼඇґଘ঱ऀɼґଘ঱ऀ྆ऀͱ΋ൟ՚֗ͷӺΛத৺ͱͨ͠৔ॴͰͷൃ‫͕ݴ‬ଟ͘‫ݟ‬ΒΕΔɽҰํͰɼґଘ঱ऀ͸ඇґଘ঱ऀҎ্ʹൟ՚֗ͷӺͷத৺෦෼ʹू·Γ΍͍͢Α͏ʹ‫ݟ‬ΒΕΔɽ͜Ε͸ɼӺߏ಺΍Ӻपลͷ޿৔ʹ٤Ԏॴ͕ଟ͘‫ݟ‬ΒΕΔ͜ͱ͔Βɼґଘ঱ऀ͕֎ग़࣌ʹӺपลͷ٤Ԏॴʹू·Γ΍͍͢͜ͱΛ͍ࣔࠦͯ͠Δɽ঱ऀͷҧ͍͕ग़ʹ͍͘͜ͱ͕‫ݪ‬Ҽͩͱߟ͑ΒΕΔɽ

✓

Map API*5 Λ༻͍ͯɼώʔτϚοϓΛඳըͨ͠ɽχίν✏ਤ 2(b) ͱਤ 3(b) ͷΑ͏ͳൟ՚֗Ͱ͸ͳ͍஍Ҭʹ͓͍ͯ͸ɼґଘ঱ऀ͸·͹ΒʹࢄΒ͹͍ͬͯΔɽ͜Εʹର͠ɼඇґଘ঱ऀ͸ಛʹઢ࿏Ԋ͍ʹଟ͘‫ݟ‬ΒΕΔͱ͍ͬͨಛ௃͕‫ݟ‬ΒΕΔɽ

5. ߟ࡯: ՄࢹԽͷԠ༻ʹ͍ͭͯ͜Ε·Ͱɼ౎ಓ෎‫ݝ‬΍ࢢ۠ொଜͱ͍ͬͨେ͖ͳ୯Ґʹ͍ͭͯͷࢦඪʢλόίফඅྔ΍‫ې‬Ԏ֎དྷड਍ऀ਺ͳͲʣ͸ଘ( 2 ) λόί‫͍ͳ͑ٵ‬ΠϥΠϥࡏ͕ͨ͠ɼͦΕΑΓ΋খ͍͞୯ҐͰͷ౷‫͍ͯͭʹܭ‬͸Α͘( 3 ) ͋ʙύνϯίߦ͖͍͚ͨͲ͓͕ۚͳ͍ʙ ΍Γͨ஌ΒΕ͍ͯͳ͔ͬͨɽ͜ͷΑ͏ͳഎ‫ܠ‬ͷதɼຊ‫ڀݚ‬͸ɼ͜✒͍͜ͱ͕Ͱ͖ͣΠϥΠϥ͢Δ4. ݁Ռɿґଘ঱ऀͷൃ‫ݴ‬෼෍ՄࢹԽΕ·ͰΑ͘஌ΒΕ͍ͯͳ͔ͬͨχίνϯґଘ঱ऀͷৄࡉͳ✑ ҐஔΛಘΔ͜ͱ͕Ͱ͖ͨɽ͜ͷ৘ใͷԠ༻ΛຊষͰߦ͏ɽૉ๿ʹ͸ɼґଘ঱ͷ࣏ྍʹ͓͍ͯɼର৅෺΍ಉ͡ґଘ঱ऀͱͷ‫཭ڑ‬ΛऔΔ͜ͱ͕ॏཁͰ͋ΔͨΊɼ͜ͷ஍఺Λආ͚લষͰ࡞੒ͨ͠χίνϯґଘ঱ͷ෼ྨ‫ث‬Λ༻͍ͯɼ୅දΔ͜ͱ͕ޮՌతͰ͋Δɽͦ͜Ͱɼχίνϯґଘ঱ऀͷີूతͳιʔγϟϧϝσΟΞͰ͋Δ Twitter ্ͰͷҐஔ৘ใ෇஍ଳΛආ͚ΔͨΊʹՄࢹԽ݁ՌΛͦͷ··༻͍Δ͜ͱ͕͖ൃ‫ݴ‬Λɼґଘ঱ऀͱඇґଘ঱ऀͷൃ‫ʹݴ‬෼ྨ͢Δɽͦͷߟ͑ΒΕΔɽͦ͜Ͱɼൟ՚֗ಉ࢜ͷൺֱΛߦ͏ɽ۩ମతʹ݁ՌΛ༻͍ͯɼ஍ཧత෼෍ͷՄࢹԽΛߦ͏ɽ͸ɼ৽॓ʹॅΉʹͯ͠΋ɼ੢ଆͱ౦ଆͷͲͪΒ͕Α͍͔ɼ·ͨ͸ɼ৽॓ͱ඼઒͕ͲͪΒ͕Α͍͔ɼͱ͍ͬͨ৘ใΛऔ

4.1 ར༻σʔλಘ͢Δɽχίνϯґଘ঱ͷ෼ྨ‫͚͔ʹث‬Δςετσʔλͱͯ͠ɼͦ͜Ͱɼࢢ۠ொଜ·Ͱେ͖͍୯ҐͰ͸ͳ͘ɼ਺Ωϩϝʔ

2011 ೥ 7 ݄ʙ2012 ೥ 7 ݄ʹ Twitter ্ʹ౤ߘ͞ΕͨҐஔτϧఔ౓ͷ୯ҐͰՄࢹԽ৘ใΛ਺஋Խ͢Δ͜ͱ͕༗ޮͰ͋৘ใ෇͖πΠʔτ 24,817,903 ݅ͷ͏ͪɼλόίʹؔ͢ΔΔɽ༷ʑͳ਺஋Խͷํ๏͕ߟ͑ΒΕΔ͕ɼຊ‫Ͱڀݚ‬͸ɼґΩʔϫʔυʢʮλόίʯʮͨ͹͜ʯʮԎ૲ʯʮ٤ԎʯͳͲʣ͕ଘ঱ऀͱඇґଘ঱ऀͷૺ۰͢Δׂ߹Λࢦ਺Խͯ͠෼Ԏࢦඪ‫·ؚ‬ΕɼνΣοΫΠϯαʔϏε Foursquare*4 ͳͲΛ༻͍ͱͯ͠ఆٛ͠ɼ͜ΕΛ༻͍ͯ஍ҬؒͷൺֱΛߦ͏ɽͨҐஔ৘ใͷΈͷൃ‫ݴ‬Λআ͍ͨ 12,237 πΠʔτΛ༻͍Δɽ෼Ԏࢦඪͷఆٛ෼ྨ‫ʹث‬ΑΔґଘ঱ऀͷൃ‫͕ݟ‬໨తͰ͋Δ͜ͱ͔ΒɼΑґଘ঱ͷ࣏ྍʹ͓͍ͯɼґଘ঱ऀͱͷ‫͕཭ڑ‬ॏཁͰ͋ΔɽΓద߹཰͕ߴ͘ͳΔΑ͏ʹ෼ྨͷᮢ஋Λ 0.95 ʹઃఆ͠ɼ*4https://ja.foursquare.com/

c 1959 Information Processing Society of Japan⃝*5

https://developers.google.com/maps/

4৘ใॲཧֶձ‫ڀݚ‬ใࠂ
IPSJ SIG Technical Report!"#!$#

ਤ 2౦‫౎ژ‬ͷՄࢹԽɽ(a) ࢢ֗஍ɼ(b) ඇࢢ֗஍ɽ!$#!"#

ਤ 3େࡕ෎ͷՄࢹԽɽ(a) ࢢ֗஍ɼ(b) ඇࢢ֗஍ɽൟ՚֗Ͱ͸ɼґଘ঱ऀͷൃ‫ݴ‬͸ඇґଘ঱ऀͷൃ‫ݴ‬෼෍ͱൺֱ͠ɼӺͷத৺෦෼ʹू·ͬͯ‫ݟ‬ΒΕΔɽൟ՚֗Ͱ͸ͳ͍஍ҬͰ͸ɼґଘ঱ऀͷൃ‫ݴ‬͸·͹ΒʹࢄΒ͹͍ͬͯΔҰํͰɼඇґଘ঱ऀͷൃ‫ݴ‬͸ಛʹઢ࿏Ԋ͍ʹଟ͘‫ݟ‬ΒΕΔɽͦ͜Ͱɼ͋Δ஍Ҭʹ͓͍ͯɼ࠷΋͍ۙґଘ঱ऀ΁ͷ‫཭ڑ‬ͷؔΘΒͣɼ࠷΋͍ۙਓؒ΁ͷฏ‫Ͱ཭ڑۉ‬ਖ਼‫ن‬Խ͢Δ.

ฏ‫( ۉ‬M inDist) ΛҎԼͷࣜʹΑΓ‫ٻ‬ΊΔɽ

M inDist =!

N ormalizedM inDist =n∈N minp∈P DIST (n, p)" minn∈N DIST (p, n)n∈Nmina∈A DIST (n, a)|N |͜͜ͰɼA ͸ൃ‫ऀݴ‬ͷू߹ʢґଘ঱ऀͱඇґଘ঱ऀͷ࿨ू

͜͜ͰɼN ͸ඇґଘ঱ऀͷू߹ɼP ͸ґଘ঱ऀͷू߹ɼ߹ N ∩PʣɼDIST (n, a) ͸ n ͱ a ͷ‫཭ڑ‬Λ‫ٻ‬ΊΔؔ਺ʢͨDIST (n, p) ͸ n ͱ p ͷ‫཭ڑ‬Λ‫ٻ‬ΊΔؔ਺Ͱ͋Δɽͨͩ͠ɼͦ΋ͦ΋աૄͳ஍ҬͰ͸͜ͷ஋͕ඞવతʹେ͖͘ͳΔɽͦ͜ͰɼҎԼͷΑ͏ʹɼґଘ঱ऀɼඇґଘ঱ऀʹc 1959 Information Processing Society of Japan⃝

ͩ͠ɼn = a ʢ‫ ཭ڑ‬0ʣͷ৔߹͸আ͘ʣɽ݁Ռ͜ͷ෼Ԏࢦඪͷ਺஋ͷେ͖͞͸ɼඇґଘ঱ऀ͕ͦͷ஍Ҭ5৘ใॲཧֶձ‫ڀݚ‬ใࠂ
IPSJ SIG Technical ReportΛग़า͘ࡍʹґଘ঱ऀͱग़ձ͏֬཰ͷ௿͞Λࢦ͢ɽ౦‫ژ‬΍ؔ੢ͷӺΛத৺ͱͨ͠ൟ՚֗ͷ෼ԎࢦඪΛࣔͨ݁͠ՌΛද4 ʹࣔ͢ɽ͜ͷ݁Ռ͔Βɼौ୩Ӻ͸෼Ԏࢦඪ͕ߴ͘ɼग़า͘ࡍʹґଘ঱ऀͱग़ձ͏֬཰͕௿͍஍ҬͰ͋Δͱ͍͑Δɽ͜ͷࢦඪͷΑ͏ʹɼൃ‫ݴ‬ҐஔΛ͋Δఔ౓ͷ୯Ґʢࢢ۠ொଜΑΓ͸খ͍͕͞ɼৄࡉͳൃ‫ݴ‬ҐஔΑΓ΋େ͖ͳ୯ҐʣͰू‫͢ܭ‬Δ͜ͱʹΑΓɼ‫ͱڥ؀‬ґଘ঱ऀͷؔ܎ΛΑΓີʹղ͖໌͔͢͜ͱ΋ՄೳͱͳΔͩΖ͏ɽ͔͠͠ɼਤ 4 ʹࣔͨ͠৽ׁ‫ݝ‬ͷΑ͏ͳաૄ஍͸ɼσʔλ਺͕গͳ͘ɼՄࢹԽʹΑͬͯಡΈऔΕΔ৘ใ͕গͳ͘ͳΔ
ͱ͍ͬͨ໰୊͕ଘࡏ͢ΔͨΊɼ͍͔ʹσʔλ਺Λ૿Ճͤ͞Δ͔ͷ‫ݕ‬౼͕ඞཁͱͳΔɽ

ਤ 4ࢀߟจ‫ݙ‬ಉ࣌ʹɼ͜ͷ෼Ԏࢦඪͷଞʹ΋ɼґଘ঱ऀͷ෼෍Λࠞ߹[1]෼෍ͱΈͳ͠ɼґଘ঱ऀͷूத͠΍͍͢஍఺Λ‫ٻ‬ΊΔͳͲɼ[2]༷ʑͳࢦඪԽ͕ߟ͑ΒΕɼ໨త΍σʔλ‫ن‬໛ʹԊͬͨࢦඪ[3]ͷσβΠϯ͕ࠓ‫ޙ‬ͷ՝୊ͱͳΔɽ
ද 4 ӺΛத৺ͱͨ͠஍Ҭͷ෼Ԏࢦඪɹ ֤ӺΛத৺ͱͨ͠ɹ
֤ล 1km ਖ਼ํ‫ܗ‬ͷ஍Ҭ

෼Ԏࢦඪ

ौ୩Ӻ

5.847

കాӺ

2.791

஑ାӺ

2.626

৽॓Ӻ

2.094

‫౎ژ‬Ӻ

2.073

࿡ຊ໦Ӻ

1.921

඼઒Ӻ

1.798

ळ༿‫ݪ‬Ӻ

1.707

౦‫ژ‬Ӻ

1.675

6. ͓ΘΓʹ

[4]

[5]

[6]

[7]

[8]

ຊ‫ڀݚ‬͸ɼιʔγϟϧϝσΟΞ্ͷґଘ঱ऀͱඇґଘ঱ऀͷൃ‫ݴ‬Λ෼ྨ͠ɼՄࢹԽͨ͠ɽ·ͣɼΫϥ΢υιʔγ[9]ϯά͔ΒಘΒΕٖͨࣅπΠʔτσʔλΛֶशσʔλͱ͠ɼ

Recurrent Convolutional Neural Network Λ༻͍ͯ෼ྨ‫ث‬Λߏஙͨ͠ɽ࣍ʹɼ͜ΕΛ༻͍ͯɼҐஔ৘ใ෇͖πΠʔτ[10]12,237 ݅Λґଘ঱ऀͱඇґଘ঱ऀͷൃ‫ʹݴ‬෼ྨ͠ɼ஍ਤ্ʹՄࢹԽͨ͠ɽχίνϯґଘ঱Λର৅ʹ࣮ͨ͠‫Ͱݧ‬͸ɼ٤ԎऀͷதͰ΋χίνϯґଘ঱ऀͷൃ‫ݴ‬͸ӺΛத৺ͱͨ͠ൟ

[11]՚֗ʹू·Δ܏޲ͳͲɼ௚‫ײ‬తʹ΋ଥ౰ͳ݁Ռ͕ಘΒΕͨɽࠓ‫ޙ‬͸ɼֶशσʔλͷվળʹΑΔ෼ྨ‫ث‬ͷਫ਼౓ͷ޲্ͱɼͦͷࢦඪԽ͕՝୊Ͱ͋Δɽँࣙ৽ׁ‫ݝ‬ͷՄࢹԽϚοϓ

[12]

[13]

ຊ‫ڀݚ‬ͷҰ෦͸ɼ೔ຊҩྍ‫ڀݚ‬։ൃ‫ڀݚߏػ‬අ ৽‫ڵ‬ɾ࠶ඉ‫ޱ‬ਐɿ̬̝̤ੈքઓུΛ౿·͑ͨΞϧίʔϧͷ༗֐࢖༻ରࡦʹؔ͢Δ૯߹త‫*ڀݚ‬6 .
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https://mhlw-grants.niph.go.jp/niph/search/NIDD00.do?resrchNum=201315050A (accessed:2017/08/08)
http://www.pfizer.co.jp/pfizer/company/press/2014/2014
10 31.html (accessed:2017/08/08)

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