input stringlengths 398 2.94k | output stringlengths 175 425 | emotional bool 2
classes |
|---|---|---|
[admiration] admired:0.746, i:0.266, the:0.115, own:0.061, honestly:0.060, which:-0.041
[amusement] admired:-0.016, the:-0.015, honestly:0.014, which:-0.012, like:0.012, i:0.011
[disapproval] honestly:0.023, wit:-0.021, the:-0.020, ccs:0.019, which:-0.017, feel:-0.015
[disgust] own:-0.000, results:-0.000, admired:-0.00... | The words admired, i contributed significantly to the feeling of admiration.
The model chose admiration because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] schedule:-0.006, own:-0.006, crappy:-0.005, training:0.005, feel:0.004, my:0.004
[amusement] back:0.000, job:-0.000, i:0.000, own:-0.000, my:0.000, racing:0.000
[disapproval] due:-0.023, like:-0.017, crappy:0.016, i:0.016, job:-0.015, giving:0.015
[disgust] crappy:0.000, from:-0.000, angle:0.000, at:0.000,... | The words crappy, feel contributed significantly to the feeling of disappointment.
The model chose disappointment because the positively contributing words were more relevant than those for other emotions. | true |
[business] s:-0.048, Will:-0.047, players:-0.040, Draft:-0.030, is:0.026, scouting:-0.026
[entertainment] won:-0.005, scouting:-0.005, AP:-0.005, association:-0.005, collective:-0.004, the:-0.004
[politics] s:-0.010, Will:-0.009, won:-0.008, AP:-0.008, said:0.007, players:-0.007
[sport] s:0.067, Will:0.061, players:0.0... | The model classified this input as sport because of the words s.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[admiration] truth:-0.064, may:-0.058, others:-0.054, beauty:0.050, society:-0.041, garage:0.038
[amusement] beauty:0.000, we:0.000, thearchitecturality:-0.000, of:-0.000, others:-0.000, strive:0.000
[disapproval] beauty:0.000, we:0.000, others:-0.000, truth:-0.000, duty:-0.000, it:-0.000
[disgust] beauty:0.000, we:0.0... | The words it, feel, beauty, society contributed significantly to the feeling of approval.
The model chose approval because the positively contributing words were more relevant than those for other emotions. | true |
[business] software:-0.403, intellectual:-0.308, property:-0.148, companies:0.087, sue:0.086, right:0.077
[entertainment] companies:-0.012, property:-0.012, software:-0.011, they:-0.011, and:-0.009, 39:0.006
[politics] property:-0.010, companies:-0.009, software:-0.008, sue:-0.008, to:-0.006, Trends:-0.006
[sport] prop... | The model classified this input as tech because of the words software, intellectual.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
[admiration] irritable:-0.000, obligated:0.000, million:0.000, i:0.000, from:-0.000, being:-0.000
[amusement] from:-0.000, irritable:-0.000, inspired:-0.000, i:0.000, like:0.000, one:0.000
[disapproval] irritable:-0.006, obligated:0.004, being:-0.004, like:0.004, bothered:-0.003, one:0.003
[disgust] from:-0.002, inspir... | The words irritable contributed significantly to the feeling of annoyance.
The model chose annoyance because the positively contributing words were more relevant than those for other emotions. | true |
[business] Electoral:-0.147, Bush:-0.098, College:-0.058, polling:-0.052, President:0.049, system:0.029
[entertainment] Bush:-0.021, Electoral:-0.015, election:-0.012, College:-0.012, 151:0.009, details:0.009
[politics] Electoral:0.184, Bush:0.158, polling:0.124, College:0.093, Doubts:0.075, technology:-0.067
[sport] p... | The model classified this input as politics because of the words Electoral.
These weaker contributions were outweighed by the stronger relevance of terms linked to politics, leading to the final prediction. | false |
[business] defection:0.235, assured:0.144, North:0.123, Two:0.116, rimmed:0.086, self:0.074
[entertainment] defection:-0.085, North:-0.057, assured:-0.050, rimmed:-0.045, Openness:-0.039, television:0.034
[politics] Korea:-0.023, ago:-0.019, old:-0.013, television:0.013, and:-0.013, millions:-0.011
[sport] self:-0.006,... | The model classified this input as business because of the words defection.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[business] Players:-0.126, players:-0.091, negotiating:0.066, own:-0.061, hour:-0.059, 3:-0.052
[entertainment] Union:-0.029, rejected:-0.026, negotiating:-0.025, proposal:-0.025, its:0.025, session:-0.023
[politics] Players:-0.033, 2:0.029, had:-0.027, players:-0.025, hour:-0.023, by:-0.020
[sport] Players:0.187, play... | The model classified this input as sport because of the words Players.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[admiration] told:-0.030, loved:-0.029, countless:0.020, ways:-0.019, people:0.016, that:0.016
[amusement] i:-0.000, loved:-0.000, you:0.000, people:-0.000, blogging:0.000, blessed:0.000
[disapproval] loved:-0.000, ways:-0.000, blessed:0.000, you:-0.000, the:0.000, blogging:0.000
[disgust] loved:-0.000, blessed:0.000, ... | The words loved, i contributed significantly to the feeling of love.
The model chose love because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] feel:0.000, jaded:0.000, i:0.000, more:0.000
[amusement] i:0.000, feel:0.000, more:-0.000, jaded:-0.000
[disapproval] feel:0.000, more:0.000, i:0.000, jaded:-0.000
[disgust] feel:0.000, i:-0.000, jaded:0.000, more:0.000
[embarrassment] feel:0.004, jaded:0.004, i:-0.001, more:0.000
[excitement] feel:0.001, ... | The words feel, jaded contributed significantly to the feeling of disappointment.
The model also considered neutral due to words like more.
The model chose disappointment because the positively contributing words were more relevant than those for other emotions. | true |
[business] Reuters:0.237, State:0.195, Hamas:-0.130, militant:0.108, Have:0.092, leaders:0.085
[entertainment] State:-0.003, treason:-0.003, Syria:-0.003, helped:-0.002, group:-0.002, Palestinian:-0.002
[politics] Reuters:-0.247, State:-0.177, Hamas:0.155, militant:-0.093, Monday:0.092, Have:-0.090
[sport] Reuters:-0.0... | The model classified this input as business because of the words Reuters.
It also considered politics due to Hamas.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[admiration] annoyed:-0.000, feeling:-0.000, suddenly:-0.000, was:-0.000, i:0.000
[amusement] annoyed:-0.000, i:0.000, suddenly:-0.000, was:0.000, feeling:-0.000
[disapproval] annoyed:-0.000, feeling:-0.000, suddenly:0.000, i:0.000, was:0.000
[disgust] suddenly:-0.000, annoyed:-0.000, i:0.000, was:0.000, feeling:0.000
... | The words annoyed contributed significantly to the feeling of annoyance.
The model also considered realization due to words like suddenly, feeling.
The model chose annoyance because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] like:-0.014, beloved:0.014, dont:-0.013, harry:-0.012, again:-0.012, potter:0.011
[amusement] i:0.000, otherwise:-0.000, feel:0.000, kinda:-0.000, again:-0.000, more:-0.000
[disapproval] dont:0.260, write:-0.156, want:0.141, otherwise:0.137, my:-0.123, like:-0.115
[disgust] dont:0.000, kinda:-0.000, i:0.00... | The words dont, feel contributed significantly to the feeling of disappointment.
The model also considered disapproval due to words like dont.
The model also considered neutral due to words like kinda.
The model also considered desire due to words like want.
The model chose disappointment because the positively contrib... | true |
[business] user:-0.084, handsets:-0.082, latest:-0.053, warning:0.049, using:-0.045, mobile:0.044
[entertainment] user:-0.015, handsets:-0.014, flaw:-0.013, mobile:-0.011, Siemens:-0.011, suggesting:0.007
[politics] user:-0.004, flaw:-0.004, handsets:-0.004, mobile:-0.003, Siemens:-0.003, series:-0.003
[sport] user:-0.... | The model classified this input as tech because of the words user.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
[admiration] of:-0.001, i:-0.001, kind:0.000, sorry:-0.000, for:-0.000, feel:-0.000
[amusement] of:-0.000, sorry:0.000, her:0.000, for:0.000, feel:-0.000, kind:0.000
[disapproval] sorry:0.000, of:-0.000, i:-0.000, her:0.000, kind:0.000, feel:-0.000
[disgust] of:-0.000, sorry:0.000, her:0.000, feel:-0.000, for:0.000, ki... | The words sorry, of, i contributed significantly to the feeling of remorse.
The model also considered caring due to words like feel, her, for.
The model chose remorse because the positively contributing words were more relevant than those for other emotions. | true |
[business] buying:0.053, 69:0.037, out:0.033, of:0.033, exporters:0.025, opening:0.024
[entertainment] buying:-0.002, exporters:-0.002, interest:-0.002, 69:-0.002, Nikkei:-0.001, out:-0.001
[politics] buying:-0.002, exporters:-0.001, interest:-0.001, 69:-0.001, out:-0.001, Nikkei:-0.001
[sport] buying:-0.001, an:-0.001... | The model classified this input as business because of the words buying.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[admiration] a:-0.001, and:0.001, was:0.000, determined:0.000, matter:0.000, of:-0.000
[amusement] i:0.000, a:-0.000, the:0.000, university:-0.000, and:0.000, or:-0.000
[disapproval] or:-0.000, racking:0.000, ago:-0.000, cry:0.000, started:0.000, feel:0.000
[disgust] and:0.000, which:0.000, i:0.000, determined:0.000, r... | The words determined contributed significantly to the feeling of approval.
The model also considered realization due to words like i, and, of.
The model chose approval because the positively contributing words were more relevant than those for other emotions. | true |
[business] report:0.181, trawler:0.132, 30:0.121, a:0.109, new:0.107, mystery:-0.097
[entertainment] report:-0.219, Report:-0.168, mystery:0.123, new:-0.099, a:-0.097, published:0.092
[politics] Report:0.062, report:0.060, fate:0.058, a:-0.048, is:-0.037, s:-0.032
[sport] new:-0.037, fate:0.032, is:-0.030, published:-0... | The model classified this input as business because of the words report.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[business] Jenson:-0.269, Formula:-0.228, Williams:-0.129, s:0.114, contract:0.100, BAR:-0.068
[entertainment] Board:-0.048, strategy:-0.038, Jenson:-0.036, arbitration:-0.035, goes:0.033, Williams:0.031
[politics] arbitration:-0.006, Jenson:-0.005, lodging:0.004, formal:-0.003, apace:-0.002, dispute:0.002
[sport] Jens... | The model classified this input as sport because of the words Jenson, Formula.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[admiration] attacked:0.000, my:-0.000, was:-0.000, wallet:0.000, a:0.000, boy:0.000
[amusement] wallet:0.002, when:0.002, attacked:0.001, my:0.001, and:-0.001, i:-0.001
[disapproval] attacked:0.000, when:0.000, boy:0.000, wallet:0.000, by:-0.000, was:-0.000
[disgust] was:-0.034, by:-0.030, had:0.020, attacked:0.017, i... | The words attacked, stolen, wallet, was contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other emotions. | true |
[business] the:-0.007, McCants:-0.006, Invitational:-0.006, 27:-0.005, semifinals:-0.005, over:-0.005
[entertainment] scores:-0.007, points:-0.007, half:-0.006, 27:-0.006, Carolina:-0.006, Tennessee:-0.005
[politics] Invitational:-0.005, 27:-0.005, half:-0.004, the:-0.004, McCants:-0.004, victory:-0.003
[sport] Invitat... | The model classified this input as sport because of the words Invitational.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[admiration] feel:-0.099, very:0.063, successful:0.062, i:-0.050, my:-0.041, life:-0.033
[amusement] successful:0.000, feel:0.000, life:0.000, and:-0.000, i:0.000, my:-0.000
[disapproval] successful:0.000, feel:0.000, in:-0.000, very:0.000, work:0.000, both:0.000
[disgust] successful:0.001, both:-0.000, family:0.000, v... | The words successful, feel contributed significantly to the feeling of caring.
The model also considered sadness due to words like successful.
The model chose caring because the positively contributing words were more relevant than those for other emotions. | true |
[business] U2:-0.044, iTunes:-0.038, New:0.022, advertising:0.018, the:-0.018, during:0.015
[entertainment] rockers:0.252, U2:0.225, airing:0.178, games:-0.123, New:-0.106, iTunes:0.101
[politics] U2:-0.003, iTunes:-0.002, feature:-0.002, Tuesday:-0.002, rockers:-0.002, New:0.002
[sport] ads:-0.075, airing:-0.075, adve... | The model classified this input as entertainment because of the words rockers, U2.
These weaker contributions were outweighed by the stronger relevance of terms linked to entertainment, leading to the final prediction. | false |
[admiration] into:-0.000, markets:0.000, because:0.000, curious:-0.000, feeling:0.000, depths:-0.000
[amusement] depths:-0.000, markets:0.000, i:0.000, the:-0.000, into:-0.000, curious:-0.000
[disapproval] into:-0.000, markets:0.000, depths:-0.000, of:0.000, feeling:0.000, the:-0.000
[disgust] curious:-0.000, into:-0.0... | The words curious contributed significantly to the feeling of curiosity.
The model chose curiosity because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] not:-0.000, am:-0.000, out:0.000, seriously:0.000, ill:-0.000, that:0.000
[amusement] not:-0.001, finding:-0.001, out:-0.000, i:0.000, seriously:0.000, am:-0.000
[disapproval] finding:-0.000, not:-0.000, seriously:0.000, ill:-0.000, out:-0.000, am:-0.000
[disgust] not:-0.000, ill:-0.000, seriously:0.000, o... | The words not contributed significantly to the feeling of neutral.
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
[business] jobs:0.155, year:0.087, Highway:0.066, Congress:0.059, Welfare:0.054, Bills:0.049
[entertainment] jobs:-0.008, Welfare:-0.007, bills:-0.007, tens:-0.005, welfare:-0.005, Lawmakers:-0.005
[politics] jobs:-0.143, year:-0.089, Highway:-0.070, Congress:-0.061, Bills:-0.047, bills:0.045
[sport] jobs:-0.005, AP:-0... | The model classified this input as business because of the words jobs.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[business] eurozone:0.071, budget:0.063, said:0.062, BRUSSELS:0.055, membership:0.051, data:0.032
[entertainment] said:-0.031, eurozone:-0.021, membership:-0.021, BRUSSELS:-0.021, Commission:-0.019, data:-0.018
[politics] budget:-0.016, eurozone:-0.015, the:-0.013, Greek:-0.012, of:-0.011, review:0.010
[sport] Greece:0... | The model classified this input as business because of the words eurozone.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[business] won:-0.005, FOXBOROUGH:-0.004, row:-0.004, that:-0.004, most:-0.004, in:-0.003
[entertainment] 17:-0.008, most:-0.008, that:-0.008, muster:-0.007, have:-0.006, the:-0.006
[politics] won:-0.002, most:-0.002, 17:-0.002, FOXBOROUGH:-0.002, that:-0.002, they:-0.002
[sport] the:0.019, games:0.018, most:0.015, wee... | The model classified this input as sport because of the words the.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[business] Agriculture:0.052, Rise:0.043, prices:0.043, Prices:0.041, Reuters:0.029, given:-0.022
[entertainment] clean:-0.026, Agriculture:-0.023, Cattle:-0.022, said:-0.022, health:-0.020, second:-0.017
[politics] Agriculture:-0.003, Prices:-0.003, Rise:-0.002, cow:-0.002, prices:-0.002, An:-0.001
[sport] Agriculture... | The model classified this input as business because of the words Agriculture.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[business] Cisco:-0.232, network:-0.188, businesses:0.104, complexity:-0.088, of:-0.079, Company:0.069
[entertainment] Cisco:-0.004, switch:-0.003, cost:-0.003, operating:-0.002, with:-0.002, complexity:-0.002
[politics] Cisco:-0.002, switch:-0.002, operating:-0.001, cost:-0.001, network:-0.001, complexity:-0.001
[spor... | The model classified this input as tech because of the words Cisco.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
[admiration] my:0.000, in:-0.000, road:-0.000, i:-0.000, frozen:-0.000, when:-0.000
[amusement] when:-0.000, relatives:-0.000, in:-0.000, frozen:-0.000, my:0.000, on:-0.000
[disapproval] relatives:-0.000, and:-0.000, when:-0.000, in:-0.000, going:0.000, my:-0.000
[disgust] when:-0.000, frozen:-0.000, and:-0.000, relati... | The words in contributed significantly to the feeling of neutral.
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] a:0.003, all:-0.003, supporting:-0.003, has:-0.003, feel:-0.003, rotation:-0.003
[amusement] them:-0.001, i:0.001, proper:-0.000, supporting:-0.000, all:0.000, rotation:-0.000
[disapproval] for:0.000, proper:-0.000, chairs:-0.000, management:-0.000, a:0.000, that:0.000
[disgust] chairs:-0.000, for:0.000, i... | The words chairs contributed significantly to the feeling of neutral.
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
[business] Kashmir:0.320, in:0.078, killing:0.076, kills:0.071, roadside:0.059, today:0.048
[entertainment] territory:-0.010, said:-0.008, killing:-0.006, Kashmir:-0.006, attack:-0.006, up:-0.005
[politics] Kashmir:-0.279, roadside:-0.061, patrol:0.052, kills:-0.050, torn:-0.042, in:-0.041
[sport] Kashmir:-0.006, killi... | The model classified this input as business because of the words Kashmir.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[admiration] fab:0.001, relief:0.001, im:-0.001, feel:-0.000, sense:-0.000, also:0.000
[amusement] and:-0.000, oh:0.000, sense:-0.000, fab:0.000, also:0.000, so:-0.000
[disapproval] relief:0.000, because:-0.000, and:-0.000, sadness:-0.000, colleagues:0.000, im:-0.000
[disgust] also:0.000, sadness:-0.000, oh:0.000, endi... | The words sadness contributed significantly to the feeling of sadness.
The model also considered relief due to words like relief.
The model chose sadness because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] i:-0.311, cute:0.273, like:-0.136, wear:0.130, when:-0.110, bunnysuit:-0.100
[amusement] i:-0.001, bunnysuit:0.000, when:0.000, feel:0.000, wear:0.000, it:0.000
[disapproval] i:-0.000, bunnysuit:0.000, cute:0.000, when:0.000, like:-0.000, new:-0.000
[disgust] i:-0.001, bunnysuit:0.000, cute:0.000, new:-0.0... | The words i, like, cute contributed significantly to the feeling of love.
The model also considered admiration due to words like cute.
The model chose love because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] this:-0.000, parenting:-0.000, if:-0.000, wonder:0.000, as:0.000, im:-0.000
[amusement] wonder:0.000, defeated:-0.000, doing:-0.000, parenting:0.000, as:0.000, did:-0.000
[disapproval] wonder:-0.037, im:-0.032, i:-0.031, wrong:0.025, parenting:0.021, as:0.013
[disgust] today:-0.000, feel:0.000, doing:-0.00... | The words defeated, wrong, feel contributed significantly to the feeling of disappointment.
The model also considered surprise due to words like wonder, i, if.
The model chose disappointment because the positively contributing words were more relevant than those for other emotions. | true |
[business] the:-0.018, Serif:-0.015, firms:0.015, offers:0.014, wrinkle:-0.013, color:-0.013
[entertainment] FONT:-0.000, search:-0.000, size:-0.000, king:-0.000, Search:-0.000, wrinkle:0.000
[politics] FONT:-0.000, search:-0.000, size:-0.000, gt:-0.000, king:-0.000, own:-0.000
[sport] FONT:-0.001, Search:-0.000, Unive... | The model classified this input as tech because of the words Serif.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
[admiration] feel:-0.000, rat:-0.000, fed:-0.000, have:-0.000, like:0.000, a:0.000
[amusement] angry:-0.000, have:-0.000, been:0.000, i:0.000, rat:0.000, like:-0.000
[disapproval] angry:0.000, feel:-0.000, have:-0.000, i:-0.000, a:-0.000, for:0.000
[disgust] angry:-0.006, feel:0.005, like:-0.005, rat:0.005, that:-0.004... | The words angry contributed significantly to the feeling of anger.
The model chose anger because the positively contributing words were more relevant than those for other emotions. | true |
[business] Web:-0.107, financial:0.064, Phishing:-0.059, technology:-0.057, phishing:-0.055, Cyber:-0.055
[entertainment] security:-0.001, and:-0.001, enforcers:-0.001, trick:-0.001, information:-0.001, companies:-0.001
[politics] SAN:-0.004, Web:-0.003, U:-0.003, Sees:-0.003, S:-0.003, trick:-0.003
[sport] into:-0.000... | The model classified this input as tech because of the words Web.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
[business] Reuters:0.097, military:0.076, Pakistan:0.068, said:0.060, troubled:0.058, forces:-0.039
[entertainment] said:-0.004, military:-0.002, Wednesday:-0.002, in:-0.002, troubled:-0.001, Afghan:-0.001
[politics] Reuters:-0.077, Pakistan:-0.051, forces:0.038, troubled:-0.032, Killed:0.027, Afghan:-0.026
[sport] nea... | The model classified this input as business because of the words Reuters.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[business] Player:-0.025, Baseball:-0.023, attack:-0.020, Major:-0.019, Valuable:-0.018, New:0.014
[entertainment] steroids:-0.016, New:0.014, in:-0.014, MVP:-0.014, Most:-0.011, Ken:-0.010
[politics] Player:-0.012, reported:0.010, Baseball:-0.010, steroids:-0.009, Major:-0.008, Valuable:-0.008
[sport] steroids:0.093, ... | The model classified this input as sport because of the words steroids.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[admiration] paranoid:0.000, remember:-0.000, feeling:0.000, i:0.000
[amusement] paranoid:0.000, feeling:0.000, remember:-0.000, i:0.000
[disapproval] paranoid:0.000, feeling:0.000, remember:-0.000, i:-0.000
[disgust] paranoid:0.000, feeling:0.000, remember:-0.000, i:-0.000
[embarrassment] feeling:0.000, paranoid:0.000... | The words paranoid, feeling contributed significantly to the feeling of nervousness.
The model also considered fear due to words like paranoid.
The model also considered realization due to words like i, remember.
The model chose nervousness because the positively contributing words were more relevant than those for oth... | true |
[admiration] strange:-0.007, slightly:-0.006, something:0.005, of:0.005, sorrow:-0.004, i:-0.004
[amusement] feel:-0.000, sorrow:0.000, with:0.000, i:0.000, god:0.000, something:-0.000
[disapproval] i:-0.000, know:-0.000, slightly:0.000, satan:-0.000, its:0.000, not:0.000
[disgust] sorrow:0.000, i:-0.000, slightly:0.00... | The words sorrow, i contributed significantly to the feeling of sadness.
The model also considered surprise due to words like strange.
The model chose sadness because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] peaceful:0.000, and:0.000, work:-0.000, supposed:-0.000, i:-0.000, sad:-0.000
[amusement] forget:-0.001, smile:0.001, supposed:0.001, and:0.001, sad:0.001, about:-0.000
[disapproval] i:-0.000, peaceful:0.000, that:0.000, feel:0.000, or:-0.000, work:-0.000
[disgust] peaceful:0.000, that:0.000, feel:0.000, i... | The words smile, and, feel contributed significantly to the feeling of joy.
The model also considered sadness due to words like sad, i.
The model chose joy because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] nor:-0.257, beautiful:0.147, important:-0.111, smart:0.108, funny:-0.059, useless:-0.058
[amusement] nor:-0.423, funny:0.231, smart:-0.091, important:0.089, uninteresting:-0.065, i:0.043
[disapproval] tired:-0.129, nor:0.123, funny:0.077, important:-0.074, feeling:-0.060, i:-0.023
[disgust] useless:0.033, ... | The words uninteresting, nor contributed significantly to the feeling of disappointment.
The model also considered amusement due to words like funny.
The model also considered annoyance due to words like nor, tired, useless.
The model chose disappointment because the positively contributing words were more relevant tha... | true |
[business] Ethiopia:0.155, Strained:0.125, Eritrea:0.125, Axum:0.114, obelisk:-0.111, could:0.097
[entertainment] obelisk:0.106, could:-0.104, relations:-0.084, Axum:-0.069, Deadlock:-0.049, to:-0.046
[politics] Eritrea:-0.026, Axum:-0.022, Ethiopia:-0.015, and:-0.012, could:0.009, prevent:0.007
[sport] Eritrea:-0.011,... | The model classified this input as business because of the words Ethiopia.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[admiration] abused:-0.002, raped:-0.002, i:-0.002, like:0.001, was:-0.001, feel:0.001
[amusement] defiled:-0.000, i:0.000, raped:0.000, like:-0.000, feel:0.000, was:-0.000
[disapproval] i:-0.002, abused:0.001, feel:-0.001, like:-0.001, defiled:0.001, raped:-0.001
[disgust] abused:-0.095, defiled:-0.072, raped:0.048, w... | The words feel, raped, defiled contributed significantly to the feeling of sadness.
The model also considered embarrassment due to words like feel, abused, i.
The model also considered love due to words like i, like.
The model chose sadness because the positively contributing words were more relevant than those for oth... | true |
[admiration] stimulation:-0.000, thing:-0.000, this:0.000, have:0.000, just:0.000, poking:-0.000
[amusement] stimulation:-0.001, i:-0.001, this:0.001, spot:-0.001, at:0.001, already:0.000
[disapproval] i:-0.000, stimulation:-0.000, find:0.000, at:0.000, determined:0.000, method:-0.000
[disgust] stimulation:-0.015, enjo... | The words enjoy, i contributed significantly to the feeling of joy.
The model also considered approval due to words like determined.
The model chose joy because the positively contributing words were more relevant than those for other emotions. | true |
[business] UK:-0.134, level:0.075, fraud:0.072, student:-0.063, presented:-0.057, because:-0.034
[entertainment] fraud:-0.358, record:0.265, level:-0.262, hits:0.246, UK:0.137, qualifications:-0.124
[politics] fraud:0.355, cancelled:0.214, level:0.209, hits:-0.157, student:0.147, record:-0.133
[sport] UK:-0.019, applic... | The model classified this input as politics because of the words fraud, cancelled, level.
It also considered entertainment due to record, hits.
These weaker contributions were outweighed by the stronger relevance of terms linked to politics, leading to the final prediction. | false |
[business] to:0.017, third:0.017, growing:0.016, market:0.015, Sales:0.014, Profit:0.013
[entertainment] growing:-0.001, to:-0.001, spending:-0.001, Sales:-0.001, third:-0.001, market:-0.001
[politics] approved:-0.002, medicines:-0.002, growing:-0.002, demand:-0.001, Profit:-0.001, to:-0.001
[sport] to:-0.008, growing:... | The model classified this input as business because of the words to.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[admiration] less:-0.001, her:-0.001, generous:0.001, psychotic:-0.001, feeling:-0.001, im:-0.001
[amusement] less:-0.002, her:-0.001, generous:0.001, i:0.001, feeling:-0.001, call:0.001
[disapproval] her:-0.004, feeling:0.002, psychotic:0.002, i:0.002, call:0.002, less:-0.000
[disgust] her:-0.001, less:-0.000, generou... | The words her contributed significantly to the feeling of neutral.
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] nasty:0.000, i:0.000, to:0.000, with:0.000, rather:-0.000, like:0.000
[amusement] vent:-0.000, was:0.000, nasty:0.000, vicious:-0.000, rather:-0.000, adult:0.000
[disapproval] ugly:0.007, like:0.006, rather:-0.005, out:-0.005, them:-0.005, and:-0.004
[disgust] rather:-0.203, nasty:0.191, vent:-0.171, was:0... | The words deal contributed significantly to the feeling of approval.
The model also considered disgust due to words like nasty.
The model also considered neutral due to words like rather.
The model chose approval because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] beautiful:0.731, nice:0.205, neglectful:-0.146, imporant:-0.127, could:0.084, was:0.076
[amusement] feel:0.000, neglectful:0.000, reception:-0.000, you:-0.000, tell:0.000, way:-0.000
[disapproval] pulled:0.011, people:-0.010, those:-0.009, neglectful:0.009, walked:0.009, only:-0.008
[disgust] champagne:-0.... | The words beautiful, nice contributed significantly to the feeling of admiration.
The model chose admiration because the positively contributing words were more relevant than those for other emotions. | true |
[business] profit:0.011, and:0.011, Russia:0.010, 3Q:0.009, Profit:0.008, reported:0.008
[entertainment] profit:-0.008, and:-0.007, Russia:-0.007, 3Q:-0.006, reported:-0.006, volume:0.005
[politics] profit:-0.001, 3Q:-0.001, and:-0.001, Russia:-0.001, Profit:-0.001, reported:-0.001
[sport] profit:-0.001, 3Q:-0.001, and... | The model classified this input as business because of the words profit.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[admiration] is:-0.000, irritable:0.000, who:-0.000, head:-0.000, making:0.000, bit:-0.000
[amusement] irritable:0.000, pressured:-0.000, because:-0.000, bit:-0.000, than:0.000, momentarily:0.000
[disapproval] me:0.000, is:-0.000, relates:-0.000, been:0.000, who:-0.000, head:-0.000
[disgust] irritable:0.000, because:-0... | The words irritable contributed significantly to the feeling of annoyance.
The model chose annoyance because the positively contributing words were more relevant than those for other emotions. | true |
[business] dole:0.051, Corporate:0.048, perks:0.041, billion:0.041, final:0.039, nearly:-0.035
[entertainment] Corporate:-0.008, out:-0.006, for:-0.006, dole:-0.006, tax:-0.004, OK:-0.004
[politics] Senate:0.028, await:-0.020, perks:-0.019, active:-0.019, the:-0.017, who:-0.016
[sport] Corporate:-0.038, dole:-0.027, it... | The model classified this input as business because of the words dole.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[business] Tampa:-0.004, Wins:-0.004, and:-0.004, Tigers:-0.003, Diaz:-0.003, with:-0.003
[entertainment] and:-0.016, 7:-0.013, set:-0.012, Tigers:-0.011, Mark:-0.009, Rays:-0.009
[politics] and:-0.002, Tampa:-0.002, Wins:-0.001, Beating:-0.001, 70th:-0.001, Top:-0.001
[sport] and:0.026, Tampa:0.025, Wins:0.019, Beatin... | The model classified this input as sport because of the words and.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[business] Iran:0.146, countries:0.128, deadline:0.119, scene:-0.094, West:0.079, uranium:0.072
[entertainment] countries:-0.107, deadline:-0.104, scene:0.104, Iran:-0.095, activities:-0.068, West:-0.041
[politics] uranium:-0.007, Iran:-0.007, freeze:-0.004, showdown:-0.003, countries:-0.003, West:-0.002
[sport] uraniu... | The model classified this input as business because of the words Iran.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[admiration] and:-0.000, odonnell:-0.000, is:0.000, two:0.000, very:-0.000, moments:0.000
[amusement] i:0.000, betrayed:-0.000, interceptions:-0.000, dont:-0.000, odonnell:0.000, two:0.000
[disapproval] look:-0.029, his:0.029, dont:0.028, realize:-0.027, interceptions:-0.025, by:-0.025
[disgust] interceptions:-0.000, b... | The words realize contributed significantly to the feeling of realization.
The model also considered sadness due to words like heartbreaking, unfortunate.
The model chose realization because the positively contributing words were more relevant than those for other emotions. | true |
[business] alumni:-0.017, Palmer:-0.015, Singh:-0.013, several:-0.011, for:-0.010, is:0.009
[entertainment] Vijay:-0.053, PGA:-0.053, Against:-0.050, off:-0.043, Goes:0.039, Grain:0.034
[politics] PGA:-0.002, victory:-0.001, Tour:-0.001, off:-0.001, Ryan:-0.001, for:-0.001
[sport] PGA:0.085, Singh:0.073, Goes:-0.070, w... | The model classified this input as sport because of the words PGA.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[business] are:0.008, spam:-0.008, commercial:-0.007, messaging:-0.006, senders:-0.005, ISPs:-0.005
[entertainment] of:-0.002, providers:-0.002, spam:-0.002, major:-0.002, instant:-0.002, lawsuits:-0.001
[politics] spam:-0.001, of:-0.001, ISPs:-0.001, commercial:-0.001, messaging:-0.001, second:-0.001
[sport] spam:-0.0... | The model classified this input as tech because of the words spam.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
[business] game:-0.141, LSU:-0.132, team:-0.082, occur:-0.080, call:-0.071, for:0.052
[entertainment] losses:-0.010, past:-0.010, lost:-0.009, be:-0.009, weekend:-0.009, occur:-0.008
[politics] LSU:-0.006, season:-0.003, weekend:-0.003, game:-0.002, occur:-0.002, past:-0.002
[sport] LSU:0.155, game:0.143, occur:0.083, ... | The model classified this input as sport because of the words LSU.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[business] the:-0.002, worlds:-0.002, ending:-0.002, to:-0.002, on:-0.001, 2:-0.001
[entertainment] Russian:-0.001, the:-0.001, ending:-0.001, on:-0.001, to:-0.001, 4:0.001
[politics] the:-0.000, ending:-0.000, to:-0.000, Teenager:-0.000, worlds:-0.000, Serena:-0.000
[sport] the:0.006, ending:0.004, to:0.004, Russian:0... | The model classified this input as sport because of the words the.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[business] the:-0.001, quarter:-0.001, Lamar:-0.001, Miami:-0.001, in:-0.001, first:-0.001
[entertainment] quarter:-0.003, left:-0.002, tackle:-0.002, Gordon:-0.002, AP:-0.002, against:-0.002
[politics] quarter:-0.000, RB:-0.000, Lamar:-0.000, Sunday:-0.000, Gordon:-0.000, Hope:-0.000
[sport] quarter:0.005, Gordon:0.00... | The model classified this input as sport because of the words quarter.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[business] Multiplexing:-0.004, antennas:-0.003, one:-0.003, transmitting:-0.003, Frequency:-0.003, Siemens:0.002
[entertainment] Multiplexing:-0.002, one:-0.002, antennas:-0.002, to:-0.001, transmitting:-0.001, achieve:0.001
[politics] Multiplexing:-0.001, one:-0.001, antennas:-0.001, transmitting:-0.001, to:-0.001, D... | The model classified this input as tech because of the words Multiplexing.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
[admiration] californians:-0.000, feel:0.000, sorry:0.000, i:-0.000, so:0.000, for:0.000
[amusement] for:0.000, sorry:0.000, feel:0.000, californians:-0.000, i:0.000, so:0.000
[disapproval] for:0.000, feel:0.000, sorry:-0.000, so:0.000, i:0.000, californians:0.000
[disgust] californians:-0.000, feel:0.000, so:0.000, fo... | The words sorry contributed significantly to the feeling of remorse.
The model chose remorse because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] wasnt:-0.008, a:-0.006, semi:0.006, situation:-0.005, all:0.005, clever:0.004
[amusement] wasnt:-0.001, clever:0.000, a:-0.000, semi:0.000, i:0.000, song:0.000
[disapproval] clever:0.208, i:-0.183, wasnt:0.144, try:-0.125, or:0.088, lyric:0.086
[disgust] clever:0.000, wasnt:-0.000, but:-0.000, i:0.000, all... | The words title contributed significantly to the feeling of neutral.
The model also considered disapproval due to words like clever.
The model also considered disappointment due to words like wasnt, clever, i.
The model chose neutral because the positively contributing words were more relevant than those for other emot... | true |
[admiration] hope:-0.000, to:0.000, hesitant:0.000, feel:0.000, that:0.000, i:-0.000
[amusement] hope:-0.000, hesitant:0.000, to:0.000, something:-0.000, have:-0.000, eternal:-0.000
[disapproval] hope:-0.001, hesitant:0.000, to:0.000, that:0.000, can:-0.000, experienced:0.000
[disgust] hope:-0.000, hesitant:0.000, to:0... | The words hope contributed significantly to the feeling of optimism.
The model chose optimism because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] means:-0.282, good:0.213, writing:0.148, also:0.112, it:0.112, my:-0.068
[amusement] is:-0.000, good:0.000, feeling:0.000, means:0.000, am:-0.000, also:-0.000
[disapproval] unsure:0.000, good:0.000, which:-0.000, am:-0.000, is:-0.000, my:-0.000
[disgust] good:0.000, feeling:0.000, writing:-0.000, my:-0.000... | The words good contributed significantly to the feeling of optimism.
The model also considered admiration due to words like good.
The model also considered approval due to words like good.
The model also considered confusion due to words like unsure.
The model chose optimism because the positively contributing words we... | true |
[business] SAP:-0.109, software:-0.098, support:-0.078, better:0.044, center:-0.040, The:0.037
[entertainment] SAP:-0.007, software:-0.007, well:-0.006, to:-0.006, support:-0.006, as:0.004
[politics] software:-0.004, SAP:-0.003, enterprise:-0.002, 4:-0.001, well:-0.001, in:-0.001
[sport] software:-0.002, SAP:-0.001, in... | The model classified this input as tech because of the words SAP.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
[admiration] feel:0.000, can:-0.000, the:0.000, get:0.000, excited:-0.000, better:0.000
[amusement] excited:-0.002, craving:0.001, and:-0.001, only:-0.001, feels:0.001, sometimes:-0.001
[disapproval] excited:-0.000, craving:0.000, feels:0.000, better:0.000, like:0.000, me:-0.000
[disgust] excited:-0.000, feels:0.000, a... | The words excited contributed significantly to the feeling of excitement.
The model also considered joy due to words like feel, me.
The model chose excitement because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] feel:-0.000, left:-0.000, extra:0.000, hated:0.000, i:0.000, out:0.000
[amusement] extra:-0.000, i:0.000, left:-0.000, just:-0.000, feel:0.000, hated:-0.000
[disapproval] feel:-0.000, left:-0.000, hated:0.000, i:0.000, extra:0.000, just:0.000
[disgust] left:-0.001, hated:0.001, feel:0.000, out:-0.000, extr... | The words feel, left contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] songs:-0.000, tortured:0.000, writes:-0.000, taylor:-0.000, basks:-0.000, swift:-0.000
[amusement] tortured:0.000, knowledge:-0.000, basks:-0.000, bet:-0.000, songs:-0.000, about:-0.000
[disapproval] tortured:0.000, songs:-0.000, about:-0.000, bet:-0.000, knowledge:-0.000, probably:-0.000
[disgust] songs:-... | The words that contributed significantly to the feeling of neutral.
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] realized:-0.000, shaky:0.000, hot:0.000, im:0.000, feel:-0.000, i:-0.000
[amusement] i:0.000, realized:-0.000, feeling:0.000, hot:-0.000, opposite:-0.000, still:-0.000
[disapproval] realized:-0.001, the:-0.001, polar:0.000, still:-0.000, which:-0.000, of:0.000
[disgust] realized:-0.000, intolerably:0.000, ... | The words realized contributed significantly to the feeling of realization.
The model also considered disappointment due to words like shaky.
The model chose realization because the positively contributing words were more relevant than those for other emotions. | true |
[business] Rooney:-0.037, fantasy:-0.029, debut:-0.024, stuff:0.019, Manchester:-0.019, down:0.017
[entertainment] Rooney:-0.368, United:-0.285, have:-0.128, of:-0.099, the:0.078, stuff:0.078
[politics] Rooney:-0.012, fantasy:-0.008, Wayne:-0.006, debut:-0.006, Manchester:-0.004, stuff:0.003
[sport] Rooney:0.476, Unite... | The model classified this input as sport because of the words Rooney, United.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[business] Beagle:-0.009, by:-0.007, the:-0.007, Colin:-0.007, robotic:-0.006, been:-0.006
[entertainment] Beagle:0.432, robotic:-0.322, mission:-0.258, revealed:0.185, new:-0.125, laboratory:-0.117
[politics] Beagle:-0.007, Colin:-0.007, team:-0.006, outlined:-0.006, have:0.005, Mars:-0.005
[sport] details:-0.010, Col... | The model classified this input as tech because of the words robotic, mission.
It also considered entertainment due to Beagle, revealed.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
[admiration] i:-0.000, appalled:-0.000, im:-0.000, life:0.000, full:0.000, of:0.000
[amusement] appalled:-0.002, so:0.001, im:-0.001, life:0.001, feel:-0.001, of:-0.001
[disapproval] life:0.000, im:-0.000, feel:-0.000, so:0.000, full:0.000, appalled:0.000
[disgust] life:0.000, i:-0.000, feel:-0.000, im:-0.000, so:0.000... | The words appalled contributed significantly to the feeling of fear.
The model also considered joy due to words like full.
The model chose fear because the positively contributing words were more relevant than those for other emotions. | true |
[business] Bangladesh:0.521, paralysed:-0.190, strikes:0.140, day:0.110, Opposition:0.082, political:0.074
[entertainment] strikes:-0.033, Opposition:-0.027, after:-0.017, activists:-0.016, halt:-0.015, at:-0.015
[politics] Bangladesh:-0.506, paralysed:0.160, cities:-0.097, activists:0.081, day:-0.077, rally:0.063
[spo... | The model classified this input as business because of the words Bangladesh.
It also considered politics due to paralysed.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[business] Congo:0.107, Security:0.079, visiting:0.077, troops:0.067, are:0.062, peace:0.055
[entertainment] Council:-0.038, Security:-0.031, eastern:-0.027, troops:-0.026, visiting:-0.024, a:0.017
[politics] Congo:-0.054, DR:-0.045, Security:-0.036, visiting:-0.027, peace:-0.017, UN:0.016
[sport] troops:-0.019, visits... | The model classified this input as business because of the words Congo.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[business] Premier:-0.002, match:-0.002, 0:-0.002, with:-0.002, League:-0.002, on:-0.001
[entertainment] Premier:-0.012, 39:0.011, match:-0.011, 0:-0.011, League:-0.010, with:-0.009
[politics] Premier:-0.001, match:-0.001, 0:-0.001, League:-0.001, with:-0.001, the:-0.001
[sport] Premier:0.024, match:0.023, League:0.022... | The model classified this input as sport because of the words Premier.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[admiration] though:-0.009, feel:-0.007, successful:0.007, more:0.005, as:-0.004, i:-0.004
[amusement] more:-0.003, i:0.002, as:0.002, we:0.002, feel:-0.001, successful:-0.001
[disapproval] here:0.000, as:0.000, were:0.000, successful:0.000, we:-0.000, more:-0.000
[disgust] successful:0.000, as:0.000, i:-0.000, were:0.... | The words we, successful, i contributed significantly to the feeling of optimism.
The model chose optimism because the positively contributing words were more relevant than those for other emotions. | true |
[business] Democrats:-0.107, party:-0.087, SPD:-0.085, painful:-0.055, economic:0.049, years:0.049
[entertainment] reforms:-0.011, its:-0.009, Democrats:-0.009, Routed:-0.007, SPD:-0.007, s:-0.007
[politics] Democrats:0.119, party:0.097, SPD:0.094, years:-0.063, its:0.054, painful:0.043
[sport] its:-0.010, SPD:-0.008, ... | The model classified this input as politics because of the words Democrats.
These weaker contributions were outweighed by the stronger relevance of terms linked to politics, leading to the final prediction. | false |
[business] software:-0.298, Cuts:0.286, plan:0.234, restructuring:0.233, a:0.161, 70:0.159
[entertainment] Jobs:-0.003, Cuts:-0.003, 36:0.003, by:-0.002, announced:-0.002, implemented:-0.002
[politics] Cuts:-0.001, people:0.000, Jobs:-0.000, restructuring:-0.000, announced:-0.000, business:-0.000
[sport] Cuts:-0.000, J... | The model classified this input as business because of the words Cuts, plan, restructuring.
It also considered tech due to software.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[admiration] study:-0.070, needs:-0.060, i:-0.052, for:0.051, the:0.044, enjoyable:-0.039
[amusement] glad:-0.000, enjoyable:0.000, needs:-0.000, teaching:0.000, curriculum:0.000, know:0.000
[disapproval] study:-0.000, glad:0.000, interesting:-0.000, make:-0.000, enjoyable:-0.000, special:0.000
[disgust] helped:0.000, ... | The words glad, enjoyable contributed significantly to the feeling of joy.
The model chose joy because the positively contributing words were more relevant than those for other emotions. | true |
[business] manufacturing:0.045, spending:0.042, sturdiness:-0.039, September:0.039, that:0.037, construction:0.034
[entertainment] sturdiness:0.049, spending:-0.045, activity:-0.042, manufacturing:-0.039, September:-0.032, picks:0.030
[politics] manufacturing:-0.002, gt:-0.002, spending:-0.002, economy:-0.002, provided... | The model classified this input as business because of the words manufacturing.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
[admiration] i:-0.000, do:-0.000, really:0.000, feel:-0.000, giggly:-0.000
[amusement] really:-0.009, do:0.006, feel:-0.006, giggly:0.005, i:0.005
[disapproval] giggly:0.001, i:-0.000, feel:-0.000, do:0.000, really:0.000
[disgust] feel:-0.012, giggly:0.006, really:-0.005, i:-0.004, do:0.002
[embarrassment] giggly:0.375... | The words giggly contributed significantly to the feeling of nervousness.
The model also considered embarrassment due to words like giggly, i.
The model also considered annoyance due to words like giggly.
The model chose nervousness because the positively contributing words were more relevant than those for other emoti... | true |
[admiration] affectionate:-0.000, i:0.000, want:0.000, to:-0.000, feel:0.000
[amusement] to:0.000, i:0.000, feel:0.000, want:0.000, affectionate:0.000
[disapproval] i:-0.000, want:0.000, feel:-0.000, affectionate:-0.000, to:-0.000
[disgust] want:-0.000, feel:0.000, to:-0.000, affectionate:0.000, i:0.000
[embarrassment]... | The words want, i contributed significantly to the feeling of desire.
The model also considered neutral due to words like affectionate.
The model chose desire because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] amazed:0.244, feefyefo:0.235, blabber:-0.156, and:0.129, space:0.102, read:0.100
[amusement] at:-0.001, i:0.001, space:-0.001, how:0.000, through:-0.000, was:-0.000
[disapproval] amazed:0.000, at:-0.000, life:0.000, i:0.000, was:-0.000, blabber:-0.000
[disgust] feel:0.000, at:-0.000, the:-0.000, transparen... | The words amazed contributed significantly to the feeling of surprise.
The model also considered admiration due to words like amazed, feefyefo.
The model chose surprise because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] desire:-0.000, insatiable:0.000, and:0.000, amorous:0.000, i:-0.000, clean:-0.000
[amusement] bit:-0.001, plant:0.001, feeling:0.001, clean:-0.000, insatiable:0.000, m:0.000
[disapproval] clean:-0.003, amorous:0.002, plant:0.002, little:-0.002, feeling:-0.002, a:0.002
[disgust] clean:-0.001, desire:-0.001,... | The words desire, clean contributed significantly to the feeling of desire.
The model chose desire because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] overworked:0.000, stressed:0.000, running:0.000, im:0.000, fumes:-0.000, feeling:0.000
[amusement] stressed:0.000, overworked:0.000, fumes:0.000, im:0.000, and:-0.000, running:-0.000
[disapproval] im:-0.001, stressed:0.001, overworked:0.001, feeling:-0.001, on:-0.000, running:0.000
[disgust] on:-0.033, str... | The words stressed, feeling contributed significantly to the feeling of nervousness.
The model also considered disappointment due to words like overworked.
The model chose nervousness because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] wicked:-0.048, try:-0.045, deserve:0.038, lost:-0.035, i:0.029, ive:0.026
[amusement] me:0.001, wicked:0.001, keen:-0.001, clothing:-0.001, this:0.000, feels:-0.000
[disapproval] deserve:0.000, tell:-0.000, lbs:-0.000, this:0.000, year:-0.000, between:-0.000
[disgust] wicked:0.000, deserve:0.000, new:-0.00... | The words deserve contributed significantly to the feeling of approval.
The model also considered desire due to words like deserve, wicked.
The model chose approval because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] feel:-0.004, should:-0.003, uncertain:-0.003, a:0.003, better:0.002, be:-0.002
[amusement] feel:-0.002, uncertain:-0.002, should:-0.002, a:0.002, be:-0.001, better:0.001
[disapproval] a:-0.005, feel:-0.005, better:0.004, uncertain:-0.004, should:0.003, communicator:0.003
[disgust] feel:-0.000, better:0.000... | The words should, communicator contributed significantly to the feeling of neutral.
The model also considered approval due to words like better, i.
The model also considered confusion due to words like uncertain.
The model chose neutral because the positively contributing words were more relevant than those for other e... | true |
[admiration] gorgeous:0.828, my:-0.085, following:-0.077, style:0.072, i:-0.065, mother:0.052
[amusement] more:0.000, style:-0.000, become:0.000, i:0.000, changed:0.000, my:0.000
[disapproval] gorgeous:-0.000, more:0.000, confident:0.000, become:0.000, than:-0.000, and:-0.000
[disgust] confident:0.000, gorgeous:-0.000,... | The words gorgeous contributed significantly to the feeling of admiration.
The model chose admiration because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] i:-0.048, proud:0.039, to:-0.034, feel:-0.026, my:-0.022, how:0.011
[amusement] proud:-0.000, to:0.000, i:0.000, extremely:0.000, want:0.000, can:-0.000
[disapproval] to:0.000, country:-0.000, can:-0.000, proud:-0.000, extremely:0.000, feel:0.000
[disgust] proud:0.000, extremely:0.000, feel:0.000, run:-0.0... | The words proud contributed significantly to the feeling of pride.
The model chose pride because the positively contributing words were more relevant than those for other emotions. | true |
[admiration] before:-0.000, staying:-0.000, which:-0.000, broken:0.000, relatives:-0.000, in:-0.000
[amusement] before:-0.000, staying:-0.000, which:-0.000, broken:0.000, was:0.000, in:-0.000
[disapproval] before:-0.000, which:-0.000, broken:0.000, relatives:-0.000, house:0.000, was:0.000
[disgust] before:-0.000, stayi... | The words before contributed significantly to the feeling of neutral.
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
[business] eastern:0.011, Marlins:-0.011, Third:-0.011, postponed:-0.010, Sunday:-0.008, crept:-0.008
[entertainment] postponed:-0.031, Florida:-0.028, Marlins:-0.028, Third:-0.025, of:-0.024, ashore:0.018
[politics] Marlins:-0.003, Third:-0.003, Sunday:-0.003, postponed:-0.003, crept:-0.002, eastern:0.002
[sport] post... | The model classified this input as sport because of the words postponed.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[business] Nanotech:-0.455, Nanosys:-0.162, investor:0.128, nature:-0.116, IPO:0.102, capitalist:0.082
[entertainment] Nanosys:-0.014, IPO:-0.011, nature:-0.010, Venture:-0.010, Nanotech:-0.010, capitalist:-0.010
[politics] Nanosys:-0.004, IPO:-0.003, setback:-0.003, Venture:-0.003, Nanotech:-0.003, investor:-0.002
[sp... | The model classified this input as tech because of the words Nanotech.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
[business] Detroit:-0.013, night:-0.012, won:-0.009, Square:-0.009, 94:-0.007, It:0.006
[entertainment] will:-0.026, Pistons:-0.026, Knicks:-0.025, hinder:-0.021, Garden:-0.021, 94:-0.018
[politics] Detroit:-0.002, night:-0.002, won:-0.002, Square:-0.001, was:-0.001, 94:-0.001
[sport] Detroit:0.048, Knicks:0.036, 94:0.... | The model classified this input as sport because of the words Detroit.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
[business] loans:0.041, airline:0.036, reported:0.036, plan:0.028, financing:0.025, part:-0.024
[entertainment] restructuring:-0.015, loans:-0.014, lost:-0.014, part:0.012, plan:-0.012, airline:-0.011
[politics] airline:-0.001, loans:-0.001, financing:-0.001, reported:-0.001, restructuring:-0.001, lost:-0.001
[sport] f... | The model classified this input as business because of the words loans.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
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