input stringlengths 427 2.84k | output stringlengths 178 432 | emotional bool 2
classes |
|---|---|---|
Predicted label: neutral
[admiration] he:-0.000, suppose:-0.000, her:-0.000, questioned:0.000, badly:0.000, had:0.000
[amusement] about:-0.001, months:0.001, he:-0.001, it:0.001, of:-0.001, last:-0.001
[disapproval] suppose:-0.010, feels:-0.009, badly:0.007, had:0.006, a:0.005, questioned:0.005
[disgust] suppose:-0.001... | The words "suppose" 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 |
Predicted label: sadness
[admiration] myself:-0.004, feel:-0.004, lost:-0.003, or:-0.003, person:0.003, a:0.003
[amusement] or:0.000, bit:-0.000, loss:-0.000, lost:-0.000, well:0.000, love:0.000
[disapproval] feel:-0.003, that:-0.002, no:0.002, myself:-0.002, a:0.002, of:-0.001
[disgust] sadness:-0.000, bit:-0.000, is:... | The words "sadness", "loss" 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 |
Predicted label: realization
[admiration] indecisive:0.000, feeling:0.000, im:-0.000, terms:0.000, of:0.000, somewhat:0.000
[amusement] indecisive:0.000, terms:0.000, do:-0.000, in:0.000, im:0.000, feeling:0.000
[disapproval] about:-0.002, indecisive:0.002, what:-0.002, feeling:0.002, of:0.001, in:0.001
[disgust] indec... | The words "indecisive" contributed significantly to the feeling of realization.
The model also considered embarrassment due to words like "indecisive", "feeling".
The model also considered confusion due to words like "somewhat", "what".
The model chose realization because the positively contributing words were more rel... | true |
Predicted label: business
[business] International:0.052, Inc:0.049, baron:0.045, billion:0.045, sue:0.037, Conrad:-0.031
[entertainment] him:0.035, Sue:-0.034, Conrad:0.033, Inc:-0.029, to:-0.027, and:-0.025
[politics] Hollinger:0.010, million:-0.010, International:-0.010, billion:-0.009, over:0.007, Inc:-0.006
[sport... | The model classified this input as business because of the words "International".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: disapproval
[admiration] inhibited:0.000, feel:0.000, want:0.000, bodies:-0.000, t:0.000, don:0.000
[amusement] i:0.000, want:0.000, anyone:-0.000, feel:0.000, bodies:0.000, don:-0.000
[disapproval] want:0.373, anyone:0.218, don:0.215, i:0.156, feel:-0.127, t:0.120
[disgust] anyone:-0.000, feel:0.000, ... | The words "want", "anyone", "don" contributed significantly to the feeling of disapproval.
The model chose disapproval because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: tech
[business] button:-0.005, use:-0.005, new:-0.004, has:-0.004, cursor:-0.004, computer:-0.003
[entertainment] button:-0.004, new:-0.003, use:-0.003, has:-0.003, cursor:-0.003, computer:-0.003
[politics] button:-0.002, cursor:-0.002, commands:-0.002, has:-0.002, computer:-0.001, new:-0.001
[sport] b... | The model classified this input as tech because of the words "button".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: approval
[admiration] bit:0.000, feeling:0.000, turned:0.000, ive:0.000, casual:0.000, very:-0.000
[amusement] ive:0.000, very:-0.000, casual:-0.000, its:0.000, turned:0.000, feeling:-0.000
[disapproval] bit:0.000, its:0.000, very:-0.000, feeling:0.000, but:0.000, ive:-0.000
[disgust] feeling:0.000, bi... | The words "casual" 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 |
Predicted label: business
[business] Sudan:0.061, factions:0.043, Nigeria:0.033, negotiators:0.032, in:0.031, After:-0.029
[entertainment] Sudan:-0.001, negotiators:-0.001, key:-0.001, factions:-0.001, in:-0.001, Nigeria:-0.001
[politics] Sudan:-0.008, negotiators:-0.005, Nigeria:-0.005, rebels:0.004, far:-0.004, Said:... | The model classified this input as business because of the words "Sudan".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: sport
[business] medal:-0.021, champion:-0.018, Cyclist:-0.018, Tyler:-0.016, his:-0.014, on:-0.012
[entertainment] possible:-0.046, the:-0.046, to:-0.045, medal:-0.039, test:-0.036, outcome:0.032
[politics] Cyclist:-0.016, medal:-0.016, champion:-0.015, gold:-0.012, time:-0.012, transfusions:0.012
[sp... | The model classified this input as sport because of the words "medal".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: neutral
[admiration] sheet:-0.000, like:0.000, i:0.000, blank:-0.000, feel:0.000, a:0.000
[amusement] sheet:-0.000, like:0.000, feel:0.000, i:0.000, blank:-0.000, a:0.000
[disapproval] sheet:-0.001, like:0.000, blank:0.000, a:-0.000, feel:0.000, i:0.000
[disgust] sheet:-0.001, like:0.001, feel:0.001, b... | The words "sheet" contributed significantly to the feeling of neutral.
The model also considered love due to words like "i", "like", "blank".
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: anger
[admiration] angry:-0.000, so:0.000, feeling:0.000, found:0.000, myself:-0.000, i:0.000
[amusement] angry:-0.000, myself:0.000, feeling:0.000, found:-0.000, so:0.000, i:-0.000
[disapproval] feeling:0.000, found:-0.000, i:-0.000, angry:-0.000, myself:0.000, so:0.000
[disgust] angry:-0.000, feeling... | 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 |
Predicted label: sport
[business] Roddick:-0.010, 39:-0.010, Open:-0.009, US:-0.008, begins:-0.008, against:-0.007
[entertainment] advantage:-0.042, can:-0.038, against:-0.037, of:-0.031, US:-0.030, home:-0.027
[politics] Roddick:-0.002, Open:-0.002, US:-0.001, home:-0.001, 39:-0.001, year:-0.001
[sport] Roddick:0.070,... | The model classified this input as sport because of the words "Roddick".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: tech
[business] the:-0.001, system:-0.000, three:-0.000, antennas:-0.000, Multiplexing:-0.000, Sets:-0.000
[entertainment] the:-0.001, system:-0.001, three:-0.000, Multiplexing:-0.000, antennas:-0.000, consisting:-0.000
[politics] the:-0.000, Internet:-0.000, system:-0.000, antennas:-0.000, antenna:-0.... | The model classified this input as tech because of the words "the".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: business
[business] Dies:0.284, died:0.209, 39:-0.202, one:0.175, who:-0.152, federation:0.145
[entertainment] Dies:-0.281, 39:0.219, died:-0.201, Statesman:0.160, bin:-0.137, world:0.112
[politics] world:-0.031, 39:-0.026, Gulf:-0.024, Sheik:-0.023, Sultan:-0.019, statesmen:0.012
[sport] became:-0.036... | The model classified this input as business because of the words "Dies", "died".
It also considered entertainment due to "39", "Statesman".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: neutral
[admiration] ill:-0.267, generous:0.225, kind:0.210, feeling:-0.178, a:-0.141, album:0.137
[amusement] album:-0.000, krem:-0.000, like:0.000, ye:-0.000, and:0.000, i:0.000
[disapproval] krem:-0.000, album:-0.000, like:0.000, feeling:0.000, am:-0.000, and:0.000
[disgust] krem:-0.000, ye:-0.000, ... | The words "ill", "show" contributed significantly to the feeling of neutral.
The model also considered admiration due to words like "generous", "kind".
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: annoyance
[admiration] bored:0.000, dull:-0.000, feeling:0.000, im:0.000, and:-0.000
[amusement] bored:0.000, dull:-0.000, and:-0.000, im:0.000, feeling:0.000
[disapproval] im:-0.008, feeling:-0.005, dull:0.003, and:0.001, bored:-0.001
[disgust] feeling:-0.068, bored:0.050, and:0.045, im:0.043, dull:0.... | The words "bored" contributed significantly to the feeling of annoyance.
The model also considered disappointment due to words like "dull", "feeling".
The model chose annoyance because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: sport
[business] BALI:-0.006, 6:-0.005, fourth:-0.004, Indonesia:-0.004, to:-0.003, into:0.002
[entertainment] fourth:-0.003, matches:-0.003, 6:-0.003, 10:-0.002, straight:-0.002, Kuznetsova:-0.002
[politics] matches:-0.001, fourth:-0.001, 6:-0.001, straight:-0.001, Ticker:-0.001, 2:0.000
[sport] 6:0.0... | The model classified this input as sport because of the words "6".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: nervousness
[admiration] overworked:0.000, fumes:-0.000, running:0.000, feeling:-0.000, im:0.000, stressed:0.000
[amusement] stressed:0.000, overworked:0.000, fumes:0.000, and:-0.000, running:-0.000, im:0.000
[disapproval] im:-0.001, feeling:-0.001, stressed:0.001, overworked:0.001, fumes:0.000, runnin... | The words "stressed" 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 |
Predicted label: gratitude
[admiration] honoured:0.126, feel:-0.105, to:-0.102, one:-0.075, i:-0.060, this:0.045
[amusement] honoured:0.000, this:-0.000, i:0.000, to:-0.000, wear:0.000, one:-0.000
[disapproval] honoured:0.001, to:0.000, i:0.000, wear:0.000, this:-0.000, one:-0.000
[disgust] honoured:0.000, i:0.000, thi... | The words "honoured" contributed significantly to the feeling of gratitude.
The model chose gratitude because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: business
[business] Detroit:0.165, undermined:0.163, Terror:-0.124, Case:0.096, about:-0.091, operational:0.090
[entertainment] undermined:-0.175, cell:-0.151, Case:-0.110, operational:-0.107, Threads:0.091, sleeper:0.089
[politics] Terror:0.114, cell:0.110, undermined:0.093, Detroit:-0.080, Case:0.077... | The model classified this input as business because of the words "Detroit".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: sport
[business] Invitational:-0.005, and:-0.004, semifinals:-0.004, first:-0.004, scores:-0.003, McCants:-0.003
[entertainment] Invitational:-0.009, and:-0.009, semifinals:-0.008, McCants:-0.008, 81:-0.006, to:-0.006
[politics] Invitational:-0.003, and:-0.003, semifinals:-0.003, McCants:-0.002, first:... | 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 |
Predicted label: neutral
[admiration] shaken:0.000, creative:0.000, side:-0.000, down:-0.000, pulled:-0.000, to:-0.000
[amusement] croissants:0.002, pulled:-0.002, didn:-0.002, lunch:0.002, down:-0.001, shaken:-0.001
[disapproval] feel:-0.302, shaken:-0.141, didn:0.122, croissants:0.113, like:0.085, a:0.081
[disgust] p... | The words "pulled", "the" contributed significantly to the feeling of neutral.
The model also considered disappointment due to words like "shaken", "creative", "i".
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: sport
[business] challengers:-0.005, face:-0.004, goal:-0.004, defeats:-0.004, medal:-0.004, would:-0.004
[entertainment] defeats:-0.041, goal:-0.035, would:-0.029, team:-0.028, stars:0.026, formidable:0.022
[politics] face:-0.003, they:-0.003, United:-0.003, challengers:-0.002, National:-0.002, gold:-... | The model classified this input as sport because of the words "would".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: business
[business] financial:0.116, for:0.104, security:0.084, 39:-0.077, was:0.076, accidentally:0.053
[entertainment] 39:0.102, security:-0.093, for:-0.086, financial:-0.066, of:-0.045, s:-0.043
[politics] reputation:-0.002, financial:-0.002, of:-0.001, with:-0.001, the:-0.001, Kong:0.001
[sport] se... | The model classified this input as business because of the words "financial".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: neutral
[admiration] longer:-0.000, feeling:0.000, watch:0.000, morning:0.000, dad:0.000, this:-0.000
[amusement] a:-0.000, feeling:0.000, additional:-0.000, then:-0.000, fine:0.000, dad:0.000
[disapproval] feeling:0.000, fine:0.000, longer:-0.000, rest:0.000, dad:0.000, am:0.000
[disgust] feeling:0.00... | The words "longer" 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 |
Predicted label: tech
[business] handset:-0.445, market:0.331, firm:0.246, handsets:-0.208, Nokia:-0.206, 39:0.174
[entertainment] growth:-0.005, rise:0.004, expects:-0.004, Nokia:-0.004, in:-0.004, launch:-0.004
[politics] growth:-0.001, Nokia:-0.001, expects:-0.001, in:-0.001, launch:-0.001, rise:0.001
[sport] growth... | The model classified this input as tech because of the words "handset", "Nokia", "handsets".
It also considered business due to "market", "firm", "39".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: disgust
[admiration] feel:-0.000, so:-0.000, dirty:0.000, french:0.000, i:0.000, unamerican:-0.000
[amusement] unamerican:-0.000, so:-0.000, feel:-0.000, i:0.000, french:0.000, dirty:0.000
[disapproval] i:-0.022, so:-0.019, unamerican:0.015, feel:-0.014, dirty:0.012, french:-0.009
[disgust] dirty:0.697... | The words "dirty" contributed significantly to the feeling of disgust.
The model also considered disappointment due to words like "unamerican", "feel", "french".
The model chose disgust because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: sport
[business] Jenson:-0.405, Williams:-0.183, Button:-0.177, Recognition:0.171, from:-0.133, dispute:0.104
[entertainment] arbitration:-0.051, house:0.051, Formula:-0.046, advanced:-0.043, strategy:-0.043, Board:0.039
[politics] Contracts:-0.007, arbitration:-0.007, of:-0.004, Formula:-0.004, Jenson... | The model classified this input as sport because of the words "Jenson", "Williams", "arbitration".
It also considered business due to "Recognition".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: anger
[admiration] frustrated:-0.001, yelling:-0.000, i:0.000, concerned:0.000, from:0.000, and:0.000
[amusement] concerned:0.000, yelling:-0.000, behind:-0.000, i:0.000, up:-0.000, regarding:-0.000
[disapproval] yelling:-0.004, and:-0.003, despite:-0.003, with:-0.002, not:0.002, behind:0.002
[disgust]... | The words "frustrated", "yelling" 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 |
Predicted label: annoyance
[admiration] passionately:0.009, feel:0.009, anyone:-0.009, please:-0.008, feels:-0.008, hyperventilating:-0.008
[amusement] not:-0.002, grew:-0.002, discussing:0.002, feels:-0.002, feel:0.002, stop:0.002
[disapproval] dangerous:0.016, so:-0.015, stop:0.013, mom:0.013, was:0.010, worth:0.010
... | The words "stop", "hyperventilating" contributed significantly to the feeling of annoyance.
The model also considered fear due to words like "dangerous".
The model chose annoyance because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: business
[business] de:0.025, automakers:0.021, O:0.020, newspaper:-0.018, metalworkers:0.016, Raise:0.014
[entertainment] de:-0.004, automakers:-0.003, O:-0.003, country:0.002, Estado:-0.002, increase:-0.002
[politics] Brazil:-0.001, automakers:-0.001, Automakers:-0.001, percent:-0.001, 39:-0.001, inc... | The model classified this input as business because of the words "de".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: politics
[business] Howard:-0.483, Reuters:0.206, execution:-0.148, Australians:-0.141, an:0.141, John:-0.101
[entertainment] Iraq:-0.002, Hostage:-0.002, remained:-0.002, Minister:-0.001, threatened:-0.001, Reuters:-0.001
[politics] Howard:0.494, Reuters:-0.202, Australians:0.156, execution:0.148, an:... | The model classified this input as politics because of the words "Howard".
It also considered business due to "Reuters".
These weaker contributions were outweighed by the stronger relevance of terms linked to politics, leading to the final prediction. | false |
Predicted label: disgust
[admiration] butt:-0.019, awful:-0.014, feeling:-0.013, cant:0.012, it:0.011, sitting:0.010
[amusement] im:0.010, in:-0.010, feel:-0.008, couch:-0.007, still:-0.007, perfectly:0.007
[disapproval] awful:-0.004, cant:0.003, sitting:0.003, all:0.002, than:-0.002, me:-0.002
[disgust] awful:0.456, b... | The words "awful", "feeling" contributed significantly to the feeling of disgust.
The model also considered annoyance due to words like "before", "butt", "awful".
The model also considered approval due to words like "when", "fine".
The model chose disgust because the positively contributing words were more relevant tha... | true |
Predicted label: neutral
[admiration] less:-0.006, her:-0.006, generous:0.005, psychotic:-0.005, feeling:-0.003, im:-0.002
[amusement] her:-0.003, less:-0.003, i:0.002, psychotic:0.002, call:0.001, generous:0.001
[disapproval] her:-0.001, i:0.001, psychotic:0.001, feeling:0.001, call:0.001, generous:0.001
[disgust] her... | 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 |
Predicted label: business
[business] bankruptcy:0.316, contracts:0.137, is:0.096, Redback:0.092, it:0.081, emerged:0.079
[entertainment] bankruptcy:-0.060, Networks:-0.033, is:-0.033, contracts:-0.027, Redback:-0.022, on:0.016
[politics] Networks:-0.021, contracts:-0.019, bankruptcy:-0.013, is:-0.012, racking:-0.011, E... | The model classified this input as business because of the words "bankruptcy".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: disgust
[admiration] feel:0.000, pressured:-0.000, and:0.000, to:0.000, any:-0.000, like:0.000
[amusement] pressured:-0.000, any:-0.000, don:-0.000, assaults:0.000, i:0.000, making:0.000
[disapproval] feel:-0.341, don:0.258, anything:0.135, rapes:-0.095, like:-0.090, has:-0.086
[disgust] rapes:0.300, f... | The words "rapes", "feel" contributed significantly to the feeling of disgust.
The model also considered disapproval due to words like "don".
The model also considered anger due to words like "rapes".
The model chose disgust because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: sport
[business] stroke:-0.014, by:-0.012, one:-0.011, Drugs:0.010, round:-0.009, Kim:-0.006
[entertainment] Teske:-0.042, Kim:-0.039, Thursday:0.034, Longs:-0.033, held:-0.030, LPGA:-0.029
[politics] stroke:-0.004, by:-0.003, one:-0.002, Challenge:-0.002, Kim:-0.002, One:0.001
[sport] Teske:0.055, Kim... | The model classified this input as sport because of the words "Teske".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: approval
[admiration] lethargic:0.000, less:-0.000, my:-0.000, feeling:0.000, soon:-0.000, and:-0.000
[amusement] less:-0.000, lot:-0.000, soon:-0.000, the:0.000, pillow:0.000, usual:-0.000
[disapproval] lethargic:0.000, and:-0.000, up:-0.000, less:-0.000, hits:-0.000, night:-0.000
[disgust] lethargic:... | The words "lethargic" contributed significantly to the feeling of approval.
The model also considered caring due to words like "lethargic", "sleep".
The model chose approval because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: tech
[business] Sybase:-0.013, Express:-0.012, source:-0.010, free:-0.009, Server:-0.009, ASE:0.008
[entertainment] Sybase:-0.001, offering:-0.001, Server:-0.001, trying:-0.001, Linux:-0.001, free:-0.000
[politics] Sybase:-0.000, trying:-0.000, offering:-0.000, Server:-0.000, Linux:-0.000, open:-0.000
... | The model classified this input as tech because of the words "Sybase".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: business
[business] Oil:0.031, southern:0.030, Prices:0.026, dropped:0.024, risen:0.023, this:-0.020
[entertainment] Sabotage:-0.000, dropped:-0.000, southern:-0.000, pipeline:-0.000, Oil:-0.000, Update:-0.000
[politics] dropped:-0.001, Prices:-0.001, Oil:-0.000, prices:-0.000, by:0.000, futures:-0.000... | The model classified this input as business because of the words "Oil".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: neutral
[admiration] the:-0.000, senior:0.000, i:0.000, that:0.000, get:-0.000, kids:-0.000
[amusement] get:-0.001, gonna:0.000, that:0.000, feeling:0.000, i:0.000, year:0.000
[disapproval] the:-0.001, get:-0.001, hated:0.001, i:0.000, feeling:0.000, kids:0.000
[disgust] few:-0.041, get:-0.039, i:0.037... | The words "the", "few" contributed significantly to the feeling of neutral.
The model also considered annoyance due to words like "hated", "feeling".
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: business
[business] sales:0.201, increases:0.127, slipping:0.108, Online:-0.099, billion:0.096, Study:0.081
[entertainment] shoppers:-0.005, sales:-0.004, fork:-0.003, year:-0.003, over:-0.003, merry:0.002
[politics] shoppers:-0.001, sales:-0.001, season:-0.001, 2:-0.001, billion:-0.001, increases:-0.0... | The model classified this input as business because of the words "sales".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: curiosity
[admiration] objects:-0.000, be:0.000, play:-0.000, or:-0.000, to:0.000, looking:0.000
[amusement] suspicious:-0.000, why:-0.000, or:-0.000, looking:0.000, at:0.000, did:0.000
[disapproval] suspicious:-0.000, did:0.000, looking:0.000, though:-0.000, play:-0.000, itself:0.000
[disgust] why:-0.... | The words "suspicious" 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 |
Predicted label: anger
[admiration] such:0.000, feel:-0.000, stupid:-0.000, even:-0.000, cancer:-0.000, out:0.000
[amusement] angry:-0.000, am:-0.000, i:0.000, such:0.000, stupid:-0.000, only:-0.000
[disapproval] angry:-0.003, unfairness:0.002, feel:-0.002, as:-0.002, cancer:0.002, about:-0.001
[disgust] angry:-0.000, ... | 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 |
Predicted label: sport
[business] organizers:-0.173, marathon:-0.113, Ticker:0.096, is:0.072, s:0.068, route:-0.067
[entertainment] route:-0.388, marathon:-0.252, organizers:0.225, details:-0.175, Olympics:0.149, unveiled:0.128
[politics] marathon:-0.025, bid:0.018, Olympics:-0.011, later:0.011, organizers:-0.010, week... | The model classified this input as sport because of the words "marathon", "route".
It also considered entertainment due to "organizers".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: sport
[business] yards:-0.012, Wrap:-0.010, for:-0.009, Jets:-0.008, Foxboro:0.007, 115:-0.007
[entertainment] yards:-0.014, 115:-0.014, for:-0.013, NFL:-0.013, Dillon:0.013, winning:-0.012
[politics] yards:-0.001, NFL:-0.001, Jets:-0.001, winning:-0.001, Dillon:-0.001, Wrap:-0.001
[sport] yards:0.028,... | The model classified this input as sport because of the words "yards".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: politics
[business] Connor:-0.181, Extols:-0.084, O:-0.083, extolled:-0.076, 11:-0.057, AP:0.057
[entertainment] law:-0.066, tensions:-0.066, importance:-0.053, Sept:0.047, saying:-0.040, Wednesday:0.037
[politics] Connor:0.149, extolled:0.129, tensions:0.116, saying:0.094, Extols:0.084, law:0.070
[spo... | The model classified this input as politics because of the words "Connor".
These weaker contributions were outweighed by the stronger relevance of terms linked to politics, leading to the final prediction. | false |
Predicted label: tech
[business] Robots:-0.005, devices:-0.003, Humanoid:-0.003, technology:-0.003, in:-0.002, Sends:-0.002
[entertainment] Robots:-0.006, Humanoid:-0.004, devices:-0.004, technology:-0.004, in:-0.004, Sends:-0.003
[politics] Robots:-0.005, Humanoid:-0.003, devices:-0.003, Sony:-0.003, in:-0.002, scienc... | The model classified this input as tech because of the words "Robots".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: sport
[business] marathon:-0.054, runners:-0.050, primary:0.048, a:-0.048, meet:-0.046, Leukemia:-0.042
[entertainment] found:-0.045, training:-0.035, in:-0.030, 38:0.028, runners:-0.025, people:0.024
[politics] runners:-0.016, marathon:-0.016, Leukemia:-0.014, Competing:-0.012, meet:-0.011, other:0.00... | The model classified this input as sport because of the words "runners", "Lymphoma".
It also considered tech due to "people".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: annoyance
[admiration] feelin:-0.000, i:0.000, so:-0.000, morning:-0.000, shot:0.000, this:0.000
[amusement] gun:-0.001, ain:-0.001, since:-0.001, horny:0.001, bitch:0.001, a:0.001
[disapproval] horny:-0.005, m:-0.004, so:0.004, gun:-0.004, shot:0.004, since:-0.004
[disgust] ain:-0.003, feelin:0.002, b... | The words "bitch", "horny", "t" 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 |
Predicted label: tech
[business] Nanotech:-0.518, Nanosys:-0.165, Venture:-0.127, investor:0.124, IPO:0.088, says:0.081
[entertainment] Nanosys:-0.008, Nanotech:-0.007, IPO:-0.006, investor:-0.005, capitalist:-0.005, setback:-0.005
[politics] Nanotech:-0.003, Nanosys:-0.003, IPO:-0.002, canceled:-0.002, investor:-0.002... | 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 |
Predicted label: business
[business] Bin:0.104, US:0.100, terrorism:0.086, is:0.079, Attacks:0.077, Laden:-0.074
[entertainment] US:-0.022, still:-0.021, coalition:-0.019, the:-0.015, After:0.010, Laden:0.010
[politics] terrorism:-0.096, Bin:-0.072, Laden:0.061, is:-0.056, 11:0.053, Attacks:-0.043
[sport] US:-0.003, co... | The model classified this input as business because of the words "Bin".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: approval
[admiration] i:-0.000, struggled:0.000, inadequate:0.000, ive:-0.000, with:-0.000, various:0.000
[amusement] life:0.000, feeling:0.000, subpar:-0.000, always:-0.000, in:-0.000, with:-0.000
[disapproval] inadequate:0.004, know:-0.004, with:-0.003, will:0.003, struggled:-0.002, subpar:-0.002
[di... | The words "always", "ive" contributed significantly to the feeling of approval.
The model also considered optimism due to words like "will".
The model also considered disappointment due to words like "inadequate", "struggled".
The model chose approval because the positively contributing words were more relevant than th... | true |
Predicted label: politics
[business] 39:-0.116, Mbeki:-0.098, President:0.076, Haiti:0.074, Party:-0.069, Bertrand:-0.056
[entertainment] allowed:-0.013, incite:0.012, Party:-0.011, Mbeki:-0.011, deposed:-0.009, says:-0.009
[politics] Mbeki:0.116, 39:0.112, Party:0.100, President:-0.095, Bertrand:0.062, that:-0.044
[sp... | The model classified this input as politics because of the words "Mbeki".
These weaker contributions were outweighed by the stronger relevance of terms linked to politics, leading to the final prediction. | false |
Predicted label: annoyance
[admiration] feel:-0.000, have:-0.000, bothered:0.000, like:0.000, i:0.000, shouldnt:0.000
[amusement] i:0.000, have:-0.000, like:0.000, even:-0.000, shouldnt:-0.000, bothered:-0.000
[disapproval] bothered:-0.145, shouldnt:0.065, feel:-0.059, have:-0.053, i:0.047, even:-0.028
[disgust] even:-... | The words "bothered", "i" 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 |
Predicted label: nervousness
[admiration] automatically:-0.000, insecure:0.000, me:0.000, makes:0.000, people:-0.000, pictures:0.000
[amusement] automatically:-0.001, myself:0.001, me:0.001, people:-0.001, pictures:0.000, i:-0.000
[disapproval] insecure:0.000, automatically:-0.000, makes:0.000, people:-0.000, me:0.000,... | The words "insecure" contributed significantly to the feeling of nervousness.
The model also considered neutral due to words like "automatically", "people".
The model chose nervousness because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: business
[business] merger:0.075, 200:0.054, an:0.053, channel:-0.048, State:0.045, Holdings:0.044
[entertainment] newspaper:-0.041, merger:-0.034, Holdings:-0.034, channel:0.031, Press:-0.027, broadcaster:0.023
[politics] merger:-0.005, Singapore:-0.005, more:-0.004, said:0.004, employees:-0.004, Ltd:... | The model classified this input as business because of the words "merger".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: business
[business] software:-0.410, Cuts:0.303, million:0.205, restructuring:0.201, plan:0.181, Jobs:0.161
[entertainment] Associates:-0.006, Computer:-0.006, 70:-0.005, 36:0.005, Inc:-0.004, saving:-0.004
[politics] Cuts:-0.001, Associates:-0.001, a:-0.001, 70:-0.001, Inc:-0.001, announced:-0.001
[sp... | The model classified this input as business because of the words "Cuts", "million", "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 |
Predicted label: tech
[business] Packs:-0.007, Punch:0.006, 3:-0.006, the:-0.005, ensure:-0.005, solutions:-0.005
[entertainment] Packs:-0.001, 3:-0.001, latest:-0.001, Linux:-0.001, solutions:-0.001, ensure:-0.001
[politics] Packs:-0.001, Linux:-0.001, 3:-0.001, ensure:-0.000, power:-0.000, latest:-0.000
[sport] Packs... | The model classified this input as tech because of the words "Packs".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: tech
[business] porn:-0.065, internet:-0.062, pornster:-0.050, only:-0.028, CHINESE:0.024, a:-0.023
[entertainment] internet:-0.205, porn:-0.162, his:0.089, sentenced:0.071, Xinhua:-0.055, pornster:0.048
[politics] porn:-0.027, internet:-0.025, only:-0.013, jail:-0.013, agency:-0.011, down:0.008
[sport... | The model classified this input as tech because of the words "internet", "porn".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: tech
[business] wireless:-0.106, Digital:-0.079, School:0.065, Dentistry:0.062, inch:-0.061, PowerBooks:-0.061
[entertainment] wireless:-0.029, simulations:-0.023, PowerBooks:-0.010, struggle:-0.009, University:-0.009, home:0.007
[politics] wireless:-0.012, PowerBooks:-0.009, simulations:-0.009, Oct:-0... | The model classified this input as tech because of the words "wireless".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: sport
[business] series:-0.003, ROMP:-0.002, runs:-0.002, up:-0.002, AUSTRALIA:-0.002, Australia:-0.002
[entertainment] runs:-0.011, by:-0.009, Test:-0.008, WIN:-0.008, Australia:-0.008, 342:-0.007
[politics] runs:-0.003, series:-0.003, their:-0.003, win:-0.003, third:0.002, WIN:-0.002
[sport] runs:0.0... | The model classified this input as sport because of the words "runs".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: entertainment
[business] Beckham:-0.033, ENGLAND:-0.023, scandal:-0.020, soccer:-0.019, is:0.016, the:-0.014
[entertainment] ENGLAND:-0.265, soccer:0.192, prostitute:0.190, e:0.170, a:-0.147, David:0.124
[politics] soccer:-0.059, captain:-0.055, ENGLAND:-0.050, involving:0.030, prostitute:0.027, victim... | The model classified this input as entertainment because of the words "soccer".
It also considered sport due to "ENGLAND", "captain".
These weaker contributions were outweighed by the stronger relevance of terms linked to entertainment, leading to the final prediction. | false |
Predicted label: business
[business] to:0.010, Dutch:0.009, it:0.009, said:0.008, CSM:0.007, Thursday:-0.006
[entertainment] business:-0.004, Capital:-0.004, candy:-0.004, CVC:-0.004, Thursday:0.004, CSM:-0.003
[politics] Dutch:-0.001, it:-0.001, to:-0.001, investment:-0.000, said:-0.000, CSM:-0.000
[sport] to:-0.001, ... | 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 |
Predicted label: sport
[business] tolerance:-0.039, for:-0.037, test:-0.032, on:-0.030, Adrian:-0.026, CHELSEA:-0.020
[entertainment] for:-0.031, on:-0.026, tolerance:-0.026, dismissed:-0.023, Romania:-0.019, door:0.018
[politics] Adrian:-0.055, a:-0.050, down:0.049, Chelsea:-0.046, enforced:0.044, when:-0.043
[sport] ... | The model classified this input as sport because of the words "Chelsea".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: approval
[admiration] bring:-0.000, satisfied:0.000, selection:-0.000, feel:0.000, very:0.000, the:-0.000
[amusement] satisfied:0.000, bring:-0.000, i:0.000, song:0.000, up:-0.000, the:-0.000
[disapproval] feel:0.000, bring:-0.000, satisfied:0.000, very:0.000, i:-0.000, selection:-0.000
[disgust] bring... | The words "satisfied", "very" 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 |
Predicted label: tech
[business] produced:-0.169, sky:-0.080, eyes:0.077, s:0.063, Iran:0.058, plummets:0.056
[entertainment] 39:0.400, produced:0.284, resolution:-0.278, eyes:-0.186, vantage:-0.163, high:-0.160
[politics] 39:-0.014, produced:-0.010, watching:0.010, 260:-0.009, into:0.006, sky:-0.006
[sport] sky:-0.005... | The model classified this input as tech because of the words "resolution".
It also considered entertainment due to "39", "produced".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: caring
[admiration] love:0.000, about:0.000, me:0.000, i:0.000, important:0.000, and:0.000
[amusement] love:0.000, spiritual:0.000, i:0.000, someones:-0.000, about:0.000, me:0.000
[disapproval] love:0.000, spiritual:0.000, progress:-0.000, about:0.000, i:0.000, me:0.000
[disgust] progress:-0.000, spiri... | The words "care", "about" contributed significantly to the feeling of caring.
The model also considered love due to words like "love".
The model chose caring because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: realization
[admiration] feeling:0.000, nostalgic:0.000, went:0.000, box:0.000, of:0.000, ive:-0.000
[amusement] ive:0.004, bit:-0.003, for:-0.003, been:0.003, ever:0.002, nostalgic:0.002
[disapproval] nostalgic:0.000, feeling:0.000, bit:-0.000, http:0.000, my:-0.000, went:0.000
[disgust] feeling:0.001... | The words "feeling" contributed significantly to the feeling of realization.
The model chose realization because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: annoyance
[admiration] irritated:-0.192, when:-0.138, great:0.127, music:0.062, if:-0.035, i:0.029
[amusement] irritated:-0.000, ignored:0.000, even:-0.000, great:0.000, when:0.000, is:0.000
[disapproval] necessary:0.003, even:-0.003, irritated:-0.002, if:-0.002, guess:-0.002, gets:-0.002
[disgust] irr... | The words "irritated" 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 |
Predicted label: business
[business] Inc:0.169, Research:0.108, Quote:0.095, Stelco:0.085, Severstal:0.071, on:-0.070
[entertainment] Inc:-0.024, offer:-0.023, bid:-0.022, for:-0.020, 39:0.018, the:0.016
[politics] Inc:-0.003, on:0.002, bid:-0.002, offer:-0.002, 39:-0.002, debts:-0.002
[sport] Inc:-0.126, Russia:0.085,... | The model classified this input as business because of the words "Inc".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: neutral
[admiration] not:-0.000, frantic:0.000, instead:-0.000, make:0.000, to:0.000, am:-0.000
[amusement] not:-0.000, instead:-0.000, am:-0.000, frantic:0.000, sleep:0.000, im:0.000
[disapproval] feeling:-0.022, to:0.021, this:0.021, yet:-0.019, instead:-0.018, teddy:-0.016
[disgust] not:-0.000, inst... | The words "not", "instead" contributed significantly to the feeling of neutral.
The model also considered nervousness due to words like "frantic".
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: politics
[business] Milosevic:-0.439, suspended:-0.096, lawyers:0.071, more:0.069, Yugoslav:0.066, time:0.056
[entertainment] Milosevic:-0.023, allow:-0.013, in:-0.010, suspended:-0.010, trial:0.008, time:-0.007
[politics] Milosevic:0.574, Yugoslav:-0.080, more:-0.058, suspended:0.056, defense:0.053, o... | The model classified this input as politics because of the words "Milosevic".
These weaker contributions were outweighed by the stronger relevance of terms linked to politics, leading to the final prediction. | false |
Predicted label: business
[business] report:0.268, Report:0.127, Norway:0.101, trawler:0.097, how:-0.094, published:-0.093
[entertainment] report:-0.225, Report:-0.207, mystery:0.154, published:0.149, of:-0.120, how:0.085
[politics] Norway:-0.076, Gaul:0.065, Report:0.055, report:0.047, of:0.046, a:-0.042
[sport] trawl... | The model classified this input as business because of the words "report".
It also considered entertainment due to "mystery".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: neutral
[admiration] great:0.375, how:0.201, commissions:-0.132, some:-0.119, you:-0.119, that:0.111
[amusement] bank:-0.000, that:0.000, great:0.000, i:0.000, not:-0.000, marketing:0.000
[disapproval] my:-0.020, not:0.018, about:-0.018, can:-0.017, reader:-0.017, for:0.017
[disgust] great:0.000, feel:... | The words "informed" contributed significantly to the feeling of neutral.
The model also considered admiration due to words like "great", "how".
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: sport
[business] players:-0.049, an:-0.044, scouting:-0.041, the:-0.029, draft:-0.028, t:0.021
[entertainment] said:-0.016, Contract:-0.014, NHL:-0.013, an:0.011, Wait:-0.011, league:-0.009
[politics] said:-0.004, NHL:-0.004, Wait:-0.004, with:-0.003, scouting:-0.003, Contract:-0.003
[sport] players:0.... | 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 |
Predicted label: love
[admiration] i:0.000, like:0.000, not:-0.000, moving:-0.000, with:0.000, time:-0.000
[amusement] i:0.000, like:0.000, with:-0.000, time:0.000, moving:-0.000, feeling:0.000
[disapproval] i:0.000, and:-0.000, like:0.000, feeling:0.000, not:0.000, time:0.000
[disgust] i:0.000, like:0.000, and:-0.000,... | The words "i", "like", "not" contributed significantly to the feeling of love.
The model also considered neutral due to words like "rushed".
The model chose love because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: sport
[business] AL:-0.005, Fernandez:-0.004, Valuable:-0.003, for:-0.003, a:-0.003, Dominican:-0.003
[entertainment] country:0.133, League:-0.130, Valuable:-0.110, was:-0.085, president:-0.078, safe:-0.072
[politics] AL:-0.002, for:-0.002, Valuable:-0.001, Fernandez:-0.001, Dominican:-0.001, to:-0.001... | The model classified this input as sport because of the words "League".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: sport
[business] Clemens:-0.013, said:-0.010, d:-0.009, with:-0.008, win:-0.008, TOP:-0.007
[entertainment] his:-0.017, TOP:-0.014, Astros:-0.012, said:-0.012, win:-0.011, 39:0.011
[politics] sign:-0.003, Clemens:-0.003, said:-0.002, BRAVES:-0.002, TOP:-0.002, STAGE:-0.002
[sport] Clemens:0.037, said:0... | The model classified this input as sport because of the words "Clemens".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: sport
[business] Czech:-0.017, announced:-0.016, playmaker:-0.016, for:-0.014, his:-0.013, Nedved:-0.012
[entertainment] international:-0.014, been:-0.011, football:-0.011, from:-0.011, Juventus:-0.011, decision:-0.009
[politics] Czech:-0.005, for:-0.004, decision:-0.004, playmaker:-0.004, announced:-0... | The model classified this input as sport because of the words "Czech".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: entertainment
[business] Americana:-0.032, GoldenPalace:-0.031, Antigua:0.027, a:-0.024, culture:-0.023, Grilled:-0.022
[entertainment] Americana:0.307, eBay:-0.149, Mary:0.115, b:0.099, phenomenon:0.098, grilled:0.083
[politics] Americana:-0.003, grilled:-0.003, Mary:0.003, pop:-0.003, 22:-0.002, eBay... | The model classified this input as entertainment because of the words "Americana".
It also considered tech due to "eBay".
These weaker contributions were outweighed by the stronger relevance of terms linked to entertainment, leading to the final prediction. | false |
Predicted label: tech
[business] Search:-0.001, com:-0.001, it:-0.001, be:-0.001, in:-0.001, with:-0.001
[entertainment] com:-0.001, Search:-0.001, it:-0.001, in:-0.001, and:-0.001, Forget:0.001
[politics] Search:-0.000, in:-0.000, com:-0.000, Yahoo:-0.000, Google:-0.000, it:-0.000
[sport] com:-0.000, Yahoo:-0.000, Goo... | The model classified this input as tech because of the words "com".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: business
[business] Cartel:0.091, company:0.072, of:0.057, price:0.053, over:0.046, a:0.039
[entertainment] of:-0.004, post:-0.004, price:-0.004, Cartel:-0.004, fixing:-0.004, allegations:-0.003
[politics] Cartel:-0.041, company:-0.040, of:-0.040, over:-0.024, packaging:-0.022, post:0.022
[sport] Carte... | The model classified this input as business because of the words "Cartel".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: realization
[admiration] realized:-0.000, shaky:0.000, of:-0.000, im:0.000, mention:0.000, the:0.000
[amusement] opposite:-0.000, feeling:0.000, realized:-0.000, hot:-0.000, intolerably:0.000, what:-0.000
[disapproval] realized:-0.001, what:0.000, that:-0.000, opposite:0.000, hot:-0.000, im:-0.000
[dis... | 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 |
Predicted label: caring
[admiration] feel:-0.048, life:-0.044, successful:0.039, very:0.026, work:-0.019, in:0.018
[amusement] successful:0.000, my:-0.000, life:0.000, feel:0.000, i:0.000, and:-0.000
[disapproval] successful:0.000, very:0.000, and:-0.000, in:-0.000, work:0.000, i:0.000
[disgust] successful:0.001, both:... | The words "successful", "feel" contributed significantly to the feeling of caring.
The model chose caring because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: business
[business] Security:0.297, Time:-0.292, warned:0.250, Mohamed:0.232, Atomic:-0.166, Energy:0.151
[entertainment] Security:-0.136, 39:0.110, Time:0.071, Energy:-0.047, The:0.044, that:0.041
[politics] Energy:-0.018, ElBaradei:0.017, of:0.017, the:0.015, International:-0.011, faces:-0.010
[sport... | The model classified this input as business because of the words "Security", "warned", "Mohamed".
It also considered tech due to "Time", "Against".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: sport
[business] Boxing:-0.130, Ali:-0.106, boxers:-0.075, Muhammad:-0.064, Commission:-0.049, oversight:0.046
[entertainment] U:-0.057, by:0.040, Commission:-0.036, a:-0.036, Calls:-0.036, oversight:-0.032
[politics] Boxing:-0.052, great:-0.049, Ali:-0.047, boxers:-0.035, U:-0.035, a:0.025
[sport] Box... | The model classified this input as sport because of the words "Boxing".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: neutral
[admiration] feel:-0.000, i:0.000, needy:-0.000, up:0.000, taking:0.000, persons:-0.000
[amusement] persons:-0.000, i:0.000, more:-0.000, some:0.000, like:0.000, place:0.000
[disapproval] persons:-0.000, needy:0.000, im:-0.000, in:-0.000, more:-0.000, place:0.000
[disgust] persons:-0.000, needy... | The words "persons", "feel" contributed significantly to the feeling of neutral.
The model also considered love due to words like "i", "like".
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: tech
[business] Cisco:-0.075, servers:-0.058, in:-0.054, data:-0.052, communications:-0.046, executives:0.042
[entertainment] data:-0.010, communications:-0.010, servers:-0.008, CLARA:-0.007, off:-0.007, centers:-0.006
[politics] data:-0.002, Cisco:-0.001, network:-0.001, servers:-0.001, better:-0.001,... | 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 |
Predicted label: neutral
[admiration] cried:0.000, who:-0.000, as:0.000, homosexual:-0.000, find:-0.000, feel:-0.000
[amusement] cried:0.000, who:-0.000, feel:-0.000, as:0.000, in:0.000, a:-0.000
[disapproval] cried:0.000, who:-0.000, as:0.000, think:-0.000, about:-0.000, feel:-0.000
[disgust] cried:0.000, who:-0.000, ... | The words "who" contributed significantly to the feeling of neutral.
The model also considered sadness due to words like "cried".
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: business
[business] Monday:0.029, would:0.019, Street:0.019, year:0.018, Reuters:0.016, on:-0.015
[entertainment] Monday:-0.001, forecasts:-0.001, price:-0.001, organic:-0.000, Reuters:-0.000, retailer:-0.000
[politics] Monday:-0.000, price:-0.000, forecasts:-0.000, organic:-0.000, Reuters:-0.000, reta... | The model classified this input as business because of the words "Monday".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: disappointment
[admiration] friend:-0.000, left:-0.000, friendship:0.000, distance:0.000, to:0.000, i:0.000
[amusement] sour:-0.000, friend:-0.000, i:0.000, problems:0.000, of:-0.000, what:-0.000
[disapproval] left:-0.003, i:0.001, talked:0.001, friendship:0.001, about:-0.001, being:-0.001
[disgust] ne... | The words "sour", "ignored", "am", "a" 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 |
Predicted label: business
[business] company:0.099, Mining:0.098, Co:0.077, they:0.052, DENVER:0.048, against:-0.043
[entertainment] Mining:-0.019, company:-0.018, Co:-0.015, executives:-0.010, are:-0.009, five:0.008
[politics] company:-0.037, Co:-0.029, Mining:-0.024, lawsuit:-0.024, DENVER:-0.024, investigation:0.015... | The model classified this input as business because of the words "company".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: disapproval
[admiration] artist:-0.155, thats:0.143, cool:0.129, i:-0.117, so:0.113, book:0.091
[amusement] cool:0.000, creativity:0.000, than:-0.000, am:0.000, so:0.000, ever:-0.000
[disapproval] hopelessly:0.248, creativity:0.180, of:-0.168, cool:-0.127, are:0.121, code:0.100
[disgust] of:-0.002, ext... | The words "hopelessly" contributed significantly to the feeling of disapproval.
The model also considered annoyance due to words like "bullshit", "goddamned", "extensions".
The model chose disapproval because the positively contributing words were more relevant than those for other emotions. | true |
Predicted label: tech
[business] Phishing:-0.032, and:-0.030, phishing:-0.023, A:-0.023, personal:-0.021, technology:-0.021
[entertainment] soon:-0.001, House:-0.001, A:-0.001, Cyber:-0.000, Sees:-0.000, scams:-0.000
[politics] soon:-0.003, Web:-0.003, most:-0.002, predicted:-0.002, A:-0.002, Chief:0.002
[sport] scams:... | The model classified this input as tech because of the words "Phishing".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: sport
[business] Tigers:-0.004, Mark:-0.003, and:-0.003, set:-0.003, Matt:-0.002, Halama:-0.002
[entertainment] Tigers:-0.025, franchise:-0.025, Wins:-0.020, set:-0.020, Matt:-0.017, Devil:0.015
[politics] Tigers:-0.001, set:-0.001, and:-0.001, Mark:-0.001, Matt:-0.001, John:-0.001
[sport] franchise:0.... | The model classified this input as sport because of the words "franchise".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: tech
[business] Mainframes:-0.151, software:-0.123, mainframe:-0.120, pricing:0.090, Pricing:0.086, to:-0.064
[entertainment] Mainframes:-0.002, has:-0.001, migrate:-0.001, software:-0.001, management:-0.001, Chart:-0.001
[politics] Mainframes:-0.001, migrate:-0.001, software:-0.001, mainframe:-0.001, ... | The model classified this input as tech because of the words "Mainframes".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
Predicted label: sport
[business] Jazz:-0.048, NBA:0.031, Sports:0.030, decided:-0.025, has:0.022, scheduled:-0.020
[entertainment] NBA:-0.169, Utah:-0.165, the:-0.132, Jazz:0.131, concerns:-0.125, postpone:-0.104
[politics] Jazz:-0.007, Russia:-0.006, has:0.005, scheduled:-0.004, security:-0.003, because:0.003
[sport]... | The model classified this input as sport because of the words "Utah".
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
Predicted label: business
[business] growth:0.212, UP:0.185, buy:0.178, 39:-0.141, TOMMY:-0.139, upscale:0.137
[entertainment] UP:-0.177, Lagerfeld:0.172, TUNES:0.166, business:-0.143, growth:-0.141, engine:-0.141
[politics] Corp:-0.003, Lagerfeld:-0.003, growth:-0.002, buy:-0.002, desperate:-0.002, UP:0.001
[sport] La... | The model classified this input as business because of the words "growth".
It also considered entertainment due to "Lagerfeld", "TUNES".
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
Predicted label: tech
[business] Spam:-0.287, browsers:-0.243, Feds:0.097, Federal:0.077, of:-0.073, computers:-0.058
[entertainment] a:0.046, Spam:-0.043, hijacking:-0.035, Feds:-0.033, that:-0.033, business:-0.029
[politics] Spam:-0.004, browsers:-0.003, illicit:-0.002, dance:0.002, after:-0.002, Feds:-0.002
[sport] ... | The model classified this input as tech because of the words "Spam", "browsers".
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
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