WORLD EMOTION GLOBAL TREND

WEAK +0.5%
Tomorrow: ANGRY +2.9%
09/Nov/2016: HAPPY +1.7%
Last Data-set:
07/Nov/2016
05:13 UTC
Diyarbakir, Turkey strong +22.7% − Guilin, China strong +3.0% − ROAD TOWN, British Virgin Islands confused +22.0% − Chungju, Korea, South sad +4.6% − Remscheid, Germany strong +19.6% − San Cristóbal, Venezuela weak +18.5% − Mönchengladbach, Germany happy +14.7% − Tarragona, Spain strong +21.8% − Giza, Egypt confused +23.8% − Loudi, China weak +17.8% − Susano, Brazil strong +1.7% − Tegal, Indonesia confused +4.6% − Waverley, United Kingdom weak +24.4% − Medellín, Colombia strong +21.8% − Eastleigh, United Kingdom happy +19.0% − Ndola, Zambia happy +17.9% − Pinar del Río, Cuba confused +18.6% − Silay, Philippines happy +19.9% − ST. GEORGES, Grenada strong +19.2% − Phoenix, United States strong +11.7% − Tacoma, United States confused +20.3% − Kure, Japan happy +17.9% − Kofu, Japan confused +20.9% − Tanga, Tanzania confused +20.3% − Xichang, China confused +20.3% − Yavatmal, India guilty +24.6% − Erbil, Iraq strong +20.1% − Boksburg, South Africa confused +18.1% − Ubon Ratchathani, Thailand confused +23.5% − Chon Buri, Thailand strong +23.4% − Burgos, Spain strong +19.8% − Chillán, Chile strong +21.7% − Abeokuta, Nigeria happy +19.5% − Chungju, Korea, South sad +4.6% − Laval, Canada strong +10.7% − Caruaru, Brazil strong +18.6% − Raniganj, India confused +23.2% − Smolensk, Russia happy +17.6% − Jhang, Pakistan strong +23.9% − Ulan-Ude, Russia confused +2.6% − Gurgaon, India strong +14.2% − Baranovichi, Belarus strong +18.5% − Nizhnekamsk, Russia confused +21.0% − Lapu-Lapu, Philippines strong +24.6% − Vallejo, United States confused +21.9% − Xichang, China confused +20.3% − Zonguldak, Turkey strong +22.9% − Tyumen, Russia strong +7.1% − Oberhausen, Germany weak +19.1% − Várzea Grande, Brazil strong +21.5% − Tameside, United Kingdom confused +19.1% − Beaumont, United States confused +12.1% − Fukuoka, Japan weak +13.4% − The Wrekin, United Kingdom happy +23.6% − La Spezia, Italy confused +22.0% − Vallejo, United States confused +21.9% − Pegu, Myanmar confused +24.1% − Tacoma, United States confused +20.3% − Shaoyang, China weak +21.8% − Yokkaichi, Japan strong +19.6% − Sikar, India confused +24.6% − East Hampshire, United Kingdom confused +19.0% − Remscheid, Germany strong +19.6% − Fresno, United States strong +21.7% − Engels, Russia strong +21.7% − Botou, China strong +24.2% − Ambala, India happy +21.8% − Jequié, Brazil strong +5.6% − Mbeya, Tanzania confused +22.8% − Monywa, Myanmar confused +22.1% − Guarapuava, Brazil strong +18.9% − Abeokuta, Nigeria happy +19.5% − Regensburg, Germany strong +16.0% − Vadakara, India sad +6.6% − Jhang, Pakistan strong +23.9% − Hamilton, Canada confused +21.0% − Amiens, France confused +22.5% − The Wrekin, United Kingdom happy +23.6% − BASSE-TERRE, Guadeloupe strong +24.8% − Bobo Dioulasso, Burkina Faso angry +20.4% − TIRANA, Albania sad +18.7% − Hachinohe, Japan strong +21.2% − Zonguldak, Turkey strong +22.9% − Malabon, Philippines happy +23.0% − Kharagpur, India strong +17.6% − Anyang, China confused +1.0% − Jamalpur, Bangladesh confused +17.7% − Botou, China strong +24.2% − North York, Canada strong +20.2% − BRIDGETOWN, Barbados confused +23.8% − Maiduguri, Nigeria confused +21.6% − Ichikawa, Japan strong +4.5% − Daxian, China sad +23.8% − Arad, Romania strong +11.8% − Cardiff, United Kingdom strong +16.1% − Grodno, Belarus sad +19.8% − LA HABANA, Cuba strong +18.2% − Warren, United States strong +22.1% − Oberhausen, Germany weak +19.1% − Anjo, Japan strong +2.2% − Springfield, United States confused +5.1% −
Leipzig, Germany strong -21.8% − Mojokerto, Indonesia strong -13.8% − Brescia, Italy strong -18.6% − Peshawar, Pakistan strong -24.4% − Muntinlupa, Philippines strong -19.8% − LISBON, Portugal strong -7.5% − Aguascalientes, Mexico strong -17.6% − Alexandria, United States strong -11.9% − Ingolstadt, Germany strong -14.9% − Durango, Mexico strong -25.0% − Braila, Romania strong -17.5% − Batangas, Philippines strong -22.8% − Magdeburg, Germany strong -23.7% − Birmingham, United States confused -22.6% − Petrópolis, Brazil strong -18.6% − Toluca, Mexico confused -22.6% − San Jose, United States confused -24.0% − Sukabumi, Indonesia happy -9.2% − Kawachinagano, Japan happy -22.9% − Geelong, Australia confused -2.1% − Adana, Turkey confused -23.4% − South Cambridgeshire, United Kingdom strong -17.7% − Santiago de los Caballeros, Dominican Republic confused -18.9% − Anand, India strong -3.1% − Jinan, China strong -8.9% − Luohe, China happy -22.9% − Gent, Belgium strong -4.2% − Darlington, United Kingdom strong -24.3% − Teignbridge, United Kingdom confused -20.2% − Sergiev Posad, Russia happy -17.8% − Cochabamba, Bolivia strong -23.2% − Kitchener, Canada strong -15.2% − Zaria, Nigeria confused -18.6% − Jersey City, United States strong -23.2% − Dourados, Brazil strong -1.2% − Tanjung Balai, Indonesia weak -4.9% − Richmond, United States confused -21.8% − Valencia, Venezuela confused -8.0% − Sefton, United Kingdom strong -5.2% − Bridgeport, United States strong -18.9% − Curitiba, Brazil strong -24.8% − San Pablo, Philippines strong -18.3% − Crewe & Nantwich, United Kingdom strong -17.6% − Bareilly, India confused -23.5% − MONROVIA, Liberia happy -23.4% − Serra, Brazil strong -5.4% − Amagasaki, Japan happy -24.2% − Palakkad, India strong -17.6% − Gujrat, Pakistan strong -22.0% − Chimbote, Peru strong -22.8% − Kochi, India strong -24.8% − Richmond, United States confused -21.8% − Chicago, United States strong -18.5% − Quezon City, Philippines strong -4.0% − Irving, United States strong -20.4% − Iseyin, Nigeria happy -22.8% − Nhatrang, Vietnam happy -22.9% − Chiba, Japan strong -24.8% − NOUMEA, New Caledonia strong -18.8% − Hampton, United States strong -12.1% − Fukuyama, Japan strong -22.7% − Queimados, Brazil strong -20.4% − Manchester, United Kingdom strong -9.2% − Jiamusi, China strong -18.6% − Halifax, Canada strong -19.1% − Rouen, France strong -6.3% − Floridablanca, Colombia strong -22.9% − Sapucaia, Brazil strong -18.8% − Kotte, Sri Lanka confused -1.5% − Yingcheng, China happy -22.9% − Kisumu, Kenya confused -19.7% − Tampa, United States confused -19.1% −
=
Niiza, Japan happy − Xiaocan, China happy − Khouribga, Morocco sad − Artux, China happy − Jastrzebie - Zdrój, Poland confused − Holon, Israel confused − Sao José do Rio Prêto, Brazil happy − Kadhimain, Iraq happy − Novocherkassk, Russia happy − Korla, China strong − Chongli, China weak − Nasariya, Iraq happy − Bihar Sharif, India happy − Shuangyashan, China happy − Guikong, China happy − Kanhangad, India happy − Taian, China confused − Pinxiang, China happy − Pórto Velho, Brazil happy − Guangshui, China happy − Meizhou, China strong − Mudangiang, China happy − Sialkote, Pakistan happy − Jinin, China happy − Shanwei, China confused − Lipetsk, Russia strong − Debrezit, Ethiopia happy − Shaown, China happy − Syktivkar, Russia happy − Ferraz de Vasconcelos, Brazil happy − Sao Joao de Meriti, Brazil happy − Dlepmealow, South Africa happy − Evpatoriya, Ukraine happy − Changweon, Korea, South happy − Kurume, Japan guilty − Kashihara, Japan happy − Sirjan, Iran happy − Huaiyin, China happy − Reggio di Calabria, Italy happy − Yuncheng, China weak − Basingstoke & Deane, United Kingdom happy − Nandyal, India happy − Dayuan, China happy − Durg, India weak − Calithèa, Greece happy − Kiselevsk, Russia happy − Moji-Guaçu, Brazil happy − Sidi-bel-Abbès, Algeria happy − San Fernando de Apure, Venezuela strong − Juazeiro do Norte, Brazil happy − Taldikorgan, Kazakstan happy − Angren, Uzbekistan confused − Xuchang, China happy − Chinhae, Korea, South happy − Al-Rakka, Syrian Arab Republic happy − Salamanca, Mexico strong − Alagoinhas, Brazil strong − Soyapango, El Salvador happy − Taiyan, China happy − Colimas, Mexico happy − Deir El-Zor, Syrian Arab Republic happy − Rubtsovsk, Russia happy
Select a group to display an individual emotion layer:
happy
excited
overjoyed
thrilled
exuberant
ecstatic
weak
helpless
hopeless
beat
overwhelmed
impotent
confused
bewildered
trapped
troubled
desperate
lost
afraid
terrified
horrified
scared stiff
petrified
fearful
guilty
sorrowful
remorseful
ashamed
unworthy
worthless
sad
depressed
disappointed
alone
hurt
left out
strong
powerful
aggressive
gung ho
potent
super
angry
furious
enraged
outraged
aggravated
irate