input stringlengths 380 738 | output stringlengths 174 364 | emotional bool 2
classes |
|---|---|---|
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] welcomed:-0.047, into:-0.018, years:-0.014, i:0.013, life:-0.011, past:-0.011
[joy] welcomed:0.328, feel:-0.079, into:0.045, life:0.041, past:0.037, like:0.033
[love] welcomed:-0.218, feel:0.101, li... | The words "welcomed" contributed significantly to the feeling of joy.
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] feel:-0.334, didnt:0.232, glad:-0.122, like:-0.098, wonderful:-0.095, another:0.066
[joy] glad:0.308, wonderful:0.285, feel:0.266, didnt:-0.250, like:0.091, another:-0.061
[love] feel:0.030, glad:-0... | The words "glad", "wonderful", "feel" contributed significantly to the feeling of joy.
The model also considered sadness due to words like "didnt".
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: tech
[business] Web:-0.155, Internet:-0.145, content:-0.079, March:-0.066, European:-0.057, four:0.047
[entertainment] Telecoms:-0.008, budget:0.007, in:-0.007, Internet:-0.005, to:-0.005, Safer:-0.004
[politics]... | The words "Web", "Internet" contributed significantly to the feeling of tech.
The model chose tech because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: anger
[sadness] insulted:-0.425, unable:0.244, feel:0.214, i:0.105, respectful:-0.103, about:-0.093
[joy] unable:-0.108, sure:0.102, insulted:-0.098, i:-0.074, shouting:-0.067, working:-0.059
[love] unable:-0.064... | The words "insulted", "shouting" contributed significantly to the feeling of anger.
The model also considered sadness due to words like "unable", "feel".
The model chose anger because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: anger
[sadness] greedy:-0.134, steady:-0.133, off:0.083, an:-0.079, with:0.063, pick:0.056
[joy] greedy:-0.405, feel:0.261, steady:0.148, pull:-0.105, pick:-0.085, jaws:-0.080
[love] steady:-0.034, feel:0.032, an... | The words "greedy" contributed significantly to the feeling of anger.
The model also considered joy due to words like "feel".
The model chose anger because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] SEOUL:0.153, Korea:0.116, 39:-0.090, officials:0.075, up:0.069, s:0.066
[entertainment] for:-0.003, s:-0.003, strike:-0.003, up:-0.003, officials:-0.003, Korea:-0.002
[politics] SEOUL:-0.158, ... | The words "SEOUL" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: love
[sadness] tender:-0.103, ready:-0.054, theres:-0.054, morsel:0.034, craving:0.029, appease:-0.019
[joy] tender:-0.544, ready:0.227, theres:0.070, morsel:-0.055, to:-0.040, a:-0.040
[love] tender:0.799, ready... | The words "tender" contributed significantly to the feeling of love.
The model also considered joy due to words like "ready".
The model chose love because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: love
[sadness] feel:-0.263, loyal:-0.132, betrayed:0.114, like:-0.110, need:-0.072, him:-0.044
[joy] loyal:-0.349, feel:0.108, my:-0.048, wait:0.041, i:0.040, like:0.038
[love] loyal:0.637, feel:0.298, like:0.131... | The words "loyal", "feel" contributed significantly to the feeling of love.
The model chose love because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: tech
[business] Linux:-0.191, servers:-0.108, software:-0.081, desktops:-0.075, research:-0.051, revenue:0.043
[entertainment] and:-0.002, Linux:-0.002, claimed:-0.001, overall:-0.001, software:-0.001, has:0.001
... | The words "Linux" contributed significantly to the feeling of tech.
The model chose tech because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: fear
[sadness] frantic:-0.185, teddy:0.052, am:-0.041, i:-0.030, feeling:-0.028, this:-0.026
[joy] frantic:-0.351, feeling:0.125, i:0.042, not:-0.035, instead:0.034, make:0.029
[love] frantic:-0.198, feeling:0.08... | The words "frantic" contributed significantly to the feeling of fear.
The model chose fear because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] talented:-0.096, and:-0.033, idea:0.033, everything:-0.021, feel:0.021, how:0.020
[joy] talented:0.597, feel:0.168, without:-0.050, are:0.046, ones:0.043, most:-0.037
[love] talented:-0.121, feel:0.... | The words "talented", "feel" contributed significantly to the feeling of joy.
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] ungrateful:0.907, wonderful:-0.035, of:0.023, love:0.021, feel:-0.015, such:-0.009
[joy] ungrateful:-0.725, wonderful:0.216, makes:-0.082, so:-0.055, i:0.055, feel:0.051
[love] ungrateful:-0.079... | The words "ungrateful" contributed significantly to the feeling of sadness.
The model also considered joy due to words like "wonderful".
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] president:0.296, General:0.187, Musharraf:-0.180, Pakistan:0.177, Pervez:0.140, s:0.134
[entertainment] said:-0.160, 39:0.131, president:-0.114, addressing:-0.075, Pervez:-0.066, the:0.064
[po... | The words "president", "General", "Pakistan" contributed significantly to the feeling of business.
The model also considered politics due to words like "said", "Musharraf".
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] investment:0.013, Dutch:0.013, Capital:0.012, company:0.010, NV:0.008, Candy:0.008
[entertainment] investment:-0.006, Capital:-0.005, company:-0.005, Dutch:-0.005, Candy:-0.005, Partners:-0.00... | The words "investment" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] feel:-0.410, dont:0.396, innocent:-0.374, too:0.224, character:-0.168, i:-0.090
[joy] innocent:0.661, feel:0.429, dont:-0.307, too:-0.157, character:0.139, almost:-0.090
[love] dont:-0.079, innocent... | The words "innocent", "feel" contributed significantly to the feeling of joy.
The model also considered sadness due to words like "dont", "too".
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: fear
[sadness] uncertain:-0.043, feel:-0.018, better:-0.017, should:0.015, a:-0.011, communicator:-0.010
[joy] uncertain:-0.618, feel:0.206, better:0.143, be:-0.092, should:-0.080, that:0.071
[love] uncertain:-0.... | The words "uncertain" contributed significantly to the feeling of fear.
The model also considered joy due to words like "feel".
The model chose fear because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] rich:-0.021, and:-0.020, like:-0.015, diverse:-0.015, incredibly:-0.012, legacy:0.010
[joy] rich:0.465, feel:-0.152, i:0.076, an:-0.062, makes:0.059, and:0.052
[love] rich:-0.145, feel:0.104, divers... | The words "rich" contributed significantly to the feeling of joy.
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: politics
[business] Sen:-0.117, Senate:-0.112, Byrd:-0.102, about:-0.074, senior:0.064, Mark:-0.060
[entertainment] senior:-0.021, terrorist:-0.018, his:0.018, member:-0.017, attack:-0.015, Dayton:0.013
[politics... | The words "Sen", "Senate" contributed significantly to the feeling of politics.
The model chose politics because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] ugly:0.053, humiliated:0.053, episode:0.045, crappy:0.044, teenage:0.041, a:-0.034
[joy] episode:-0.038, ugly:-0.034, humiliated:-0.031, of:0.027, teenage:-0.025, crappy:-0.025
[love] episode:-0... | The words "ugly" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] feel:-0.218, content:-0.154, not:0.081, i:-0.078, happy:-0.056, if:-0.055
[joy] content:0.296, feel:0.221, happy:0.200, i:0.093, not:-0.063, if:0.041
[love] content:-0.094, happy:-0.086, feel:0.048,... | The words "content", "feel", "happy" contributed significantly to the feeling of joy.
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] Democrats:-0.352, administration:0.323, Bush:0.188, 17:-0.123, strengthened:0.116, Premiums:0.115
[entertainment] strengthened:-0.001, Medicare:-0.001, Premiums:-0.001, Rise:-0.001, Democrats:... | The words "administration", "Bush" contributed significantly to the feeling of business.
The model also considered politics due to words like "Democrats".
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] sorrowful:0.918, this:-0.019, atmosphere:-0.007, feel:-0.002, listen:-0.002, song:-0.002
[joy] sorrowful:-0.608, can:0.069, feel:-0.058, atmosphere:0.048, song:0.045, a:-0.042
[love] sorrowful:-... | The words "sorrowful" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] drained:0.642, feeling:0.414, alone:0.108, with:-0.103, an:-0.102, room:-0.067
[joy] whale:-0.055, dark:-0.049, noises:-0.047, and:0.040, get:0.039, by:-0.031
[love] me:-0.002, dark:-0.002, feel... | The words "drained", "feeling" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: anger
[sadness] frustrated:-0.257, feeling:-0.105, i:-0.034, was:-0.025
[joy] frustrated:-0.275, feeling:0.113, was:0.048, i:0.019
[love] frustrated:-0.200, feeling:0.142, was:0.056, i:0.036
[anger] frustrated:0.... | The words "frustrated" contributed significantly to the feeling of anger.
The model chose anger because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: love
[sadness] feeling:-0.074, liked:-0.064, things:-0.042, no:0.039, collecting:-0.038, re:0.036
[joy] feeling:-0.471, liked:-0.367, no:-0.151, collecting:0.144, bands:0.111, i:0.105
[love] feeling:0.662, liked:... | The words "feeling", "liked" contributed significantly to the feeling of love.
The model chose love because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sport
[business] BLOOMFIELD:-0.033, Cup:-0.028, at:-0.028, United:-0.023, Europe:0.022, fans:-0.021
[entertainment] advantage:-0.026, Offensive:-0.024, Michigan:0.023, at:-0.021, Cup:-0.020, States:-0.015
[politi... | The words "Cup" contributed significantly to the feeling of sport.
The model chose sport because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] retail:0.025, stock:0.024, market:0.022, said:0.021, investors:0.021, safely:-0.019
[entertainment] Hldg:-0.019, retail:-0.017, Ameritrade:-0.015, 39:0.014, related:-0.014, safely:0.012
[polit... | The words "retail" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] hated:0.283, extra:-0.206, left:0.171, just:-0.115, out:-0.098, feel:-0.054
[joy] hated:-0.311, extra:0.159, feel:0.142, out:-0.043, i:-0.026, left:-0.019
[love] hated:-0.149, feel:0.080, extra:... | The words "hated", "left" contributed significantly to the feeling of sadness.
The model also considered joy due to words like "extra".
The model also considered anger due to words like "hated".
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] anguished:0.208, feel:0.192, even:0.125, destroyed:0.106, unworthy:-0.099, know:0.074
[joy] destroyed:-0.015, anguished:-0.014, even:-0.012, instilled:-0.012, promise:0.011, them:-0.010
[love] f... | The words "anguished", "feel" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sport
[business] ORLANDO:-0.210, about:0.103, Questioned:-0.090, inspecting:0.075, AP:0.074, Inspector:0.074
[entertainment] AP:-0.025, Checks:-0.021, Fla:0.020, Questioned:-0.018, ORLANDO:0.018, crucial:-0.013
[... | The words "ORLANDO" contributed significantly to the feeling of sport.
The model chose sport because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] artistic:-0.718, away:0.182, feel:-0.136, side:0.097, melting:0.084, nothing:0.069
[joy] artistic:0.809, feel:0.248, melting:-0.122, side:-0.097, away:-0.059, can:0.047
[love] artistic:-0.038, away:... | The words "artistic", "feel" contributed significantly to the feeling of joy.
The model also considered sadness due to words like "away".
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: anger
[sadness] dangerous:-0.037, feel:-0.032, sure:-0.027, valid:-0.025, cover:0.019, arent:0.017
[joy] dangerous:-0.655, sure:0.216, feel:-0.171, valid:0.155, is:-0.093, will:0.072
[love] dangerous:-0.034, feel... | The words "dangerous", "feel" contributed significantly to the feeling of anger.
The model also considered joy due to words like "sure", "valid".
The model chose anger because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] WTO:0.036, Trade:0.031, biotech:0.025, request:0.024, washingtonpost:0.024, Delays:0.022
[entertainment] Trade:-0.002, Decision:-0.002, washingtonpost:-0.001, Biotech:-0.001, biotech:-0.001, U... | The words "WTO" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] new:0.034, Pharmaceuticals:0.032, acquisition:0.031, shareholder:0.030, Icahn:0.023, 39:-0.020
[entertainment] new:-0.002, acquisition:-0.002, Pharmaceuticals:-0.002, shareholder:-0.002, push:... | The words "new" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: politics
[business] LAURAN:-0.278, Vaccine:0.198, WASHINGTON:-0.194, NEERGAARD:-0.164, Supply:0.151, AP:-0.124
[entertainment] AP:-0.093, the:0.068, Cut:-0.068, most:-0.058, scarce:0.055, Supply:-0.048
[politics]... | The words "LAURAN", "AP", "WASHINGTON", "NEERGAARD" contributed significantly to the feeling of politics.
The model also considered business due to words like "Vaccine", "Supply".
The model chose politics because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] percent:0.014, funds:0.011, Reserve:0.009, economists:0.009, Federal:0.008, The:0.008
[entertainment] economists:-0.003, Expected:-0.003, funds:-0.003, percent:-0.003, traders:-0.002, 0:-0.002... | The words "percent" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] Pfizer:0.371, Pfizers:0.101, twice:0.068, Bextra:0.063, attacks:0.062, painkiller:0.062
[entertainment] Pfizer:-0.033, Pfizers:-0.013, Vioxx:-0.011, strokes:0.010, placebos:-0.009, Bextra:-0.0... | The words "Pfizer" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] Ford:0.018, serious:0.016, s:0.014, Executives:0.013, Motor:0.013, deep:0.010
[entertainment] Ford:-0.006, Company:-0.004, serious:-0.004, Motor:-0.004, executives:-0.004, that:0.003
[politics... | The words "Ford" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: politics
[business] Mbeki:-0.149, 39:-0.129, Party:-0.111, Haiti:0.086, Africa:-0.082, President:0.082
[entertainment] allowed:-0.019, had:-0.014, claims:-0.014, Haiti:0.014, incite:-0.013, should:-0.013
[politic... | The words "Mbeki" contributed significantly to the feeling of politics.
The model chose politics because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sport
[business] Phil:-0.017, straight:-0.016, Cup:-0.015, Ryder:-0.011, hours:0.008, Oakland:-0.008
[entertainment] second:-0.033, Cup:-0.032, straight:-0.027, HILLS:-0.025, Mich:-0.023, BLOOMFIELD:0.019
[politi... | The words "straight" contributed significantly to the feeling of sport.
The model chose sport because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] feel:-0.025, ok:-0.022, to:-0.011, walk:-0.011, for:0.005, and:0.005
[joy] ok:0.509, buy:0.053, world:0.045, i:0.038, feel:-0.035, or:-0.034
[love] ok:-0.383, feel:0.188, to:0.041, walk:0.035, and:-... | The words "ok" contributed significantly to the feeling of joy.
The model also considered love due to words like "feel".
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sport
[business] Weaver:-0.063, Jeff:-0.058, to:-0.037, one:-0.033, the:-0.026, Team:0.021
[entertainment] Dodgers:-0.160, Yankees:-0.155, Team:-0.125, to:-0.114, desperately:-0.063, Different:0.055
[politics] Je... | The words "Dodgers", "Yankees", "to" contributed significantly to the feeling of sport.
The model chose sport because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] lonely:0.685, valued:-0.172, of:-0.103, understood:0.100, not:0.089, i:0.069
[joy] lonely:-0.575, valued:0.200, feel:0.141, of:0.090, partner:-0.083, understood:-0.076
[love] valued:-0.023, feel... | The words "lonely" contributed significantly to the feeling of sadness.
The model also considered joy due to words like "valued".
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] Holdings:0.094, operations:0.080, Press:0.075, more:0.070, channel:-0.060, Lay:0.058
[entertainment] Holdings:-0.074, channel:0.071, Press:-0.063, and:-0.057, more:-0.056, operations:-0.050
[p... | The words "Holdings" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] sorry:0.133, leaving:0.121, behind:0.083, feel:-0.042, wont:0.019, for:0.014
[joy] sorry:-0.030, leaving:-0.027, behind:-0.019, feel:0.015, wont:-0.009, i:0.001
[love] sorry:-0.022, leaving:-0.0... | The words "sorry" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] fantastic:-0.404, feeling:-0.339, not:0.186, havent:0.150, that:-0.091, any:-0.068
[joy] fantastic:0.710, feeling:0.210, not:-0.117, havent:-0.109, that:0.088, any:0.070
[love] fantastic:-0.073, fee... | The words "fantastic", "feeling" contributed significantly to the feeling of joy.
The model also considered sadness due to words like "not", "havent".
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] blank:0.760, life:0.069, love:0.061, my:0.056, sheet:-0.051, of:-0.049
[joy] blank:-0.351, without:-0.142, feel:0.071, penyeri:0.057, i:0.054, sheet:-0.053
[love] blank:-0.173, feel:0.127, them:... | The words "blank" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: anger
[sadness] resentful:-0.634, ashamed:0.237, not:0.108, able:-0.090, between:0.082, that:-0.080
[joy] ashamed:-0.161, resentful:-0.126, angry:-0.108, be:0.107, i:0.103, not:-0.069
[love] better:-0.004, ashame... | The words "resentful", "angry" contributed significantly to the feeling of anger.
The model also considered sadness due to words like "ashamed".
The model chose anger because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: fear
[sadness] amazing:-0.060, strange:-0.058, impressed:-0.055, the:-0.052, more:0.051, awkward:0.044
[joy] strange:-0.406, amazing:0.118, feel:-0.096, this:-0.067, i:0.066, guests:0.047
[love] feel:0.012, amazi... | The words "strange" contributed significantly to the feeling of fear.
The model also considered surprise due to words like "feel".
The model chose fear because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sport
[business] Bulls:-0.059, up:-0.044, 38:-0.043, Rockets:-0.043, 39:-0.041, Houston:-0.040
[entertainment] Bulls:-0.055, traded:-0.054, deal:-0.044, mentor:0.041, Chronicle:0.034, gt:-0.030
[politics] traded:... | The words "Bulls" contributed significantly to the feeling of sport.
The model chose sport because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: politics
[business] Howard:-0.355, Australians:-0.163, Firm:0.152, Reuters:0.125, Minister:-0.121, hostage:-0.096
[entertainment] Iraq:-0.002, Minister:-0.002, Howard:-0.001, the:-0.001, had:-0.001, Australian:-0... | The words "Howard", "Australians" contributed significantly to the feeling of politics.
The model also considered business due to words like "Firm".
The model chose politics because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] offer:0.094, Inc:0.083, CHMF:0.069, Research:0.068, Stelco:0.063, OAO:0.062
[entertainment] Profile:-0.013, offer:0.011, to:-0.011, UPDATE:-0.010, STEa:-0.009, debts:-0.009
[politics] offer:-0... | The words "offer" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: love
[sadness] feel:-0.195, loved:-0.156, blue:0.094, am:-0.050, tears:0.044, though:0.039
[joy] feel:-0.168, loved:-0.111, though:-0.062, tears:-0.052, the:-0.049, imagine:-0.047
[love] feel:0.590, loved:0.369, ... | The words "feel", "loved" contributed significantly to the feeling of love.
The model chose love because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: tech
[business] Microsoft:-0.130, SpaceShipOne:-0.086, Allen:0.070, Corp:0.068, Flights:-0.068, 62:-0.040
[entertainment] Microsoft:-0.267, founder:-0.114, half:-0.096, X:-0.094, pioneer:0.092, rocketed:0.067
[po... | The words "Microsoft" contributed significantly to the feeling of tech.
The model chose tech because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] Service:0.206, conspiracy:0.165, Busts:0.152, Cyber:-0.146, credit:0.141, cybercrime:-0.133
[entertainment] in:-0.005, cybercrime:-0.005, credit:-0.004, Cyber:-0.003, announced:-0.003, The:-0.... | The words "Service", "conspiracy", "Busts" contributed significantly to the feeling of business.
The model also considered tech due to words like "cybercrime", "Cyber".
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] wonderful:-0.030, much:-0.026, fortnight:-0.026, period:0.016, quite:-0.013, nothing:0.012
[joy] wonderful:0.159, fortnight:0.145, announced:0.074, feeling:0.063, i:0.060, in:0.053
[love] feeling:0.... | The words "wonderful" contributed significantly to the feeling of joy.
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: love
[sadness] generous:-0.338, feeling:-0.189, wouldnt:0.177, mark:0.101, also:-0.067, iii:-0.060
[joy] wouldnt:-0.187, generous:0.119, feeling:0.041, i:-0.040, is:-0.035, if:-0.026
[love] feeling:0.505, generou... | The words "feeling", "generous" contributed significantly to the feeling of love.
The model also considered sadness due to words like "wouldnt".
The model chose love because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: tech
[business] 2:-0.040, Verizon:0.035, songs:-0.030, Wireless:-0.029, of:-0.027, in:-0.023
[entertainment] Wireless:-0.470, Dialing:-0.330, tones:-0.287, song:0.190, songs:0.167, from:0.134
[politics] music:-0.... | The words "Wireless", "Dialing", "tones" contributed significantly to the feeling of tech.
The model also considered entertainment due to words like "song", "songs".
The model chose tech because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] miserable:0.253, dull:0.218, the:-0.081, a:-0.073, as:0.067, life:0.060
[joy] miserable:-0.095, dull:-0.087, feel:0.058, heavy:-0.053, the:0.045, words:0.035
[love] miserable:-0.041, feel:0.039,... | The words "miserable", "dull" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: fear
[sadness] insecure:-0.019, seeing:-0.008, me:-0.007, i:-0.006, because:-0.006, myself:-0.005
[joy] insecure:-0.321, feel:0.106, people:-0.055, seeing:0.045, pictures:-0.033, me:0.019
[love] insecure:-0.308, ... | The words "insecure" contributed significantly to the feeling of fear.
The model also considered love due to words like "feel".
The model chose fear because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sport
[business] previous:-0.017, Comeback:-0.013, or:-0.011, 39:-0.009, the:0.009, makeup:-0.008
[entertainment] Comeback:-0.088, dominating:-0.073, part:0.072, previous:-0.072, don:-0.048, Braves:-0.045
[politi... | The words "Comeback" contributed significantly to the feeling of sport.
The model chose sport because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] discontent:0.777, the:-0.080, feeling:0.048, have:-0.046, needed:-0.044, recognize:0.032
[joy] discontent:-0.520, feeling:0.135, needed:0.119, around:0.093, that:0.072, the:0.069
[love] disconte... | The words "discontent" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] Detroit:0.171, undermined:0.143, operational:0.134, Thinnest:0.120, Terror:-0.106, reveals:-0.100
[entertainment] undermined:-0.157, Thinnest:-0.134, operational:-0.122, has:-0.117, reveals:0.... | The words "Detroit" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: tech
[business] gigapixel:-0.020, digital:-0.018, team:-0.015, picture:-0.014, s:-0.007, to:-0.007
[entertainment] gigapixel:-0.077, team:-0.063, digital:-0.062, 600:0.025, individual:-0.022, How:-0.019
[politics... | The words "gigapixel" contributed significantly to the feeling of tech.
The model chose tech because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] crappy:0.358, feeling:0.258, nowhere:0.178, was:-0.138, closing:0.130, remember:0.113
[joy] crappy:-0.112, closing:-0.079, nowhere:-0.079, remember:-0.053, last:0.039, was:0.026
[love] crappy:-0... | The words "crappy", "feeling", "nowhere" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sport
[business] Valentine:-0.048, who:-0.039, Art:-0.028, s:0.027, replace:-0.027, in:-0.026
[entertainment] training:-0.216, team:-0.192, Valentine:-0.174, manager:-0.158, Art:0.127, part:0.118
[politics] manag... | The words "Valentine", "manager", "training", "Bobby", "team" contributed significantly to the feeling of sport.
The model chose sport because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] Oil:0.203, prices:0.136, Reuters:0.098, from:-0.085, as:-0.062, Toward:-0.050
[entertainment] Record:-0.018, and:0.018, on:-0.017, concerns:-0.017, 49:-0.016, Monday:0.015
[politics] Oil:-0.00... | The words "Oil" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] Partners:0.068, Kirkland:0.050, billion:0.049, Nextel:-0.046, Inc:0.037, proposed:0.033
[entertainment] Partners:-0.001, billion:-0.001, Corp:-0.001, merger:-0.000, for:-0.000, Sprint:-0.000
[... | The words "Partners" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sport
[business] MVP:-0.037, National:-0.033, Reuters:0.029, Major:-0.028, 41:-0.025, Dies:-0.021
[entertainment] MVP:-0.003, League:-0.003, New:-0.003, used:-0.003, Former:-0.002, died:0.002
[politics] MVP:-0.00... | The words "Major" contributed significantly to the feeling of sport.
The model chose sport because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] strong:-0.011, music:-0.009, thankful:-0.009, laugh:-0.005, im:0.005, that:0.003
[joy] strong:0.160, thankful:0.149, feel:-0.056, me:0.055, believe:-0.037, music:0.029
[love] strong:-0.104, thankful... | The words "strong" contributed significantly to the feeling of joy.
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: fear
[sadness] petrified:-0.135, childs:0.045, on:-0.033, any:-0.029, feeling:-0.027, i:-0.012
[joy] petrified:-0.360, feeling:0.057, been:0.054, indication:-0.048, any:0.046, then:-0.040
[love] petrified:-0.140,... | The words "petrified" contributed significantly to the feeling of fear.
The model chose fear because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sport
[business] Prix:-0.090, Cars:0.044, on:-0.032, Schumacher:0.030, set:0.029, cars:0.027
[entertainment] Cars:0.184, Prix:-0.174, on:-0.157, to:-0.134, cars:0.129, practice:-0.108
[politics] Prix:-0.016, Cars... | The words "Prix", "on" contributed significantly to the feeling of sport.
The model also considered entertainment due to words like "Cars".
The model chose sport because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] stressed:0.252, am:-0.026, i:-0.012, feeling:-0.011
[joy] stressed:-0.188, am:-0.058, feeling:0.023, i:-0.003
[love] stressed:-0.090, am:-0.078, feeling:0.021, i:-0.005
[anger] stressed:0.294, a... | The words "stressed" contributed significantly to the feeling of sadness.
The model also considered anger due to words like "stressed".
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: fear
[sadness] indecisive:-0.067, in:-0.015, alliance:-0.015, feeling:0.015, im:0.014, of:-0.011
[joy] indecisive:-0.339, feeling:0.115, of:0.032, alliance:-0.024, about:0.023, what:0.016
[love] indecisive:-0.266... | The words "indecisive" contributed significantly to the feeling of fear.
The model chose fear because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] feel:-0.047, beloved:-0.029, signals:0.017, his:0.013, i:0.008, agreement:0.006
[joy] feel:0.290, beloved:0.122, signals:-0.103, at:-0.080, i:0.060, agreement:-0.041
[love] feel:0.316, beloved:0.166... | The words "feel" contributed significantly to the feeling of joy.
The model also considered love due to words like "feel", "beloved".
The model also considered fear due to words like "at".
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: anger
[sadness] vicious:-0.549, ugly:0.166, nasty:-0.120, rather:-0.078, with:-0.068, like:0.050
[joy] ugly:-0.015, vicious:-0.015, an:-0.013, using:-0.012, nasty:-0.011, deal:0.010
[love] like:-0.012, using:-0.0... | The words "vicious" contributed significantly to the feeling of anger.
The model also considered sadness due to words like "ugly".
The model chose anger because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: tech
[business] software:-0.541, intellectual:-0.179, property:-0.146, companies:0.111, the:-0.109, re:0.082
[entertainment] software:-0.010, companies:-0.010, believe:-0.008, property:-0.005, more:-0.004, can:-0... | The words "software", "intellectual", "property" contributed significantly to the feeling of tech.
The model chose tech because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] gained:-0.013, privileged:-0.011, admission:-0.011, feel:-0.008, m:0.006, so:-0.006
[joy] privileged:0.343, feel:-0.130, gained:0.122, admission:0.085, ones:-0.052, i:-0.046
[love] privileged:-0.298... | The words "privileged" contributed significantly to the feeling of joy.
The model also considered love due to words like "feel".
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] developer:0.080, Chelsfield:0.067, billionaires:0.066, property:0.054, Multiplex:0.038, to:-0.034
[entertainment] Chelsfield:-0.055, developer:-0.054, property:-0.033, billionaires:-0.028, Wes... | The words "developer" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] isolated:0.758, feel:-0.133, not:0.075, intellectually:-0.048, know:0.042, true:0.034
[joy] isolated:-0.407, not:-0.074, feel:0.067, intellectually:0.046, entirely:0.040, true:0.033
[love] isola... | The words "isolated" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: tech
[business] Microsoft:-0.435, software:-0.402, giant:0.240, hearing:0.135, market:0.128, against:-0.074
[entertainment] whatever:-0.004, market:-0.004, trust:-0.004, anti:-0.003, abusing:-0.003, a:-0.003
[pol... | The words "Microsoft", "software" contributed significantly to the feeling of tech.
The model also considered business due to words like "giant".
The model chose tech because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] useless:0.794, like:-0.081, bastard:0.047, feel:0.036, i:0.014, a:0.008
[joy] useless:-0.266, like:0.082, feel:0.067, bastard:-0.038, a:-0.032, i:0.029
[love] useless:-0.235, feel:0.099, like:0.... | The words "useless" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] shareholder:0.096, year:0.085, leadership:0.075, Disney:-0.061, s:0.057, Board:0.048
[entertainment] shareholder:-0.045, Disney:0.043, new:-0.039, Reuters:-0.039, June:0.038, leadership:-0.037... | The words "shareholder" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sport
[business] Graeme:-0.044, James:-0.040, Bobby:-0.037, Robson:-0.031, 39:-0.031, striker:-0.027
[entertainment] Graeme:-0.029, having:-0.028, resignation:-0.027, with:-0.027, Shearer:-0.026, St:-0.024
[polit... | The words "Graeme" contributed significantly to the feeling of sport.
The model chose sport because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sport
[business] MINNEAPOLIS:-0.007, favorites:-0.007, Antonio:-0.006, 39:-0.006, games:-0.006, After:-0.005
[entertainment] league:-0.210, games:-0.199, has:0.094, a:-0.080, After:-0.057, making:0.055
[politics]... | The words "league", "games" contributed significantly to the feeling of sport.
The model chose sport because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: politics
[business] party:-0.595, prices:0.227, National:-0.204, election:-0.201, mixed:-0.169, oil:0.153
[entertainment] prices:-0.030, election:-0.024, key:-0.023, that:-0.021, policy:-0.018, the:0.015
[politic... | The words "party", "election", "National", "mixed" contributed significantly to the feeling of politics.
The model also considered business due to words like "prices", "oil".
The model chose politics because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] important:-0.052, feel:-0.046, like:-0.026, to:-0.015, want:0.009, m:-0.003
[joy] important:0.530, like:0.091, want:-0.032, m:-0.012, feel:0.005, to:0.004
[love] important:-0.314, feel:0.178, like:0... | The words "important" contributed significantly to the feeling of joy.
The model also considered love due to words like "feel".
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: fear
[sadness] paranoid:-0.071, wouldve:0.015, annoyance:-0.014, she:-0.012, in:-0.010, feel:0.010
[joy] paranoid:-0.289, feel:0.093, annoyance:-0.036, i:0.035, like:0.032, reality:0.023
[love] paranoid:-0.080, f... | The words "paranoid" contributed significantly to the feeling of fear.
The model chose fear because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] Israel:0.195, Egypt:0.175, s:0.164, it:0.156, deal:0.099, prisoners:-0.068
[entertainment] some:-0.042, Palestinian:-0.040, spying:-0.036, convicted:-0.032, Israel:-0.030, 39:0.025
[politics] ... | The words "Israel", "Egypt", "s", "it" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] Reuters:0.327, Hamas:-0.183, State:0.138, militant:0.133, country:0.117, Killing:-0.085
[entertainment] Syria:-0.003, May:-0.002, Reuters:-0.002, said:-0.002, militant:-0.002, act:-0.002
[poli... | The words "Reuters" contributed significantly to the feeling of business.
The model also considered politics due to words like "Hamas".
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] popular:-0.022, feel:-0.015, just:-0.014, a:-0.011, taste:-0.008, writing:-0.008
[joy] popular:0.555, to:-0.118, taste:0.102, feel:0.093, pander:-0.047, writing:0.046
[love] popular:-0.312, feel:0.1... | The words "popular" contributed significantly to the feeling of joy.
The model also considered love due to words like "feel".
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: business
[business] trade:0.039, prices:0.031, Oil:0.029, reserves:0.026, Asian:0.024, eased:-0.020
[entertainment] trade:-0.011, make:-0.008, government:-0.008, prices:-0.007, said:-0.007, the:0.007
[politics] t... | The words "trade" contributed significantly to the feeling of business.
The model chose business because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: fear
[sadness] frozen:-0.067, relatives:0.062, slowly:-0.061, road:-0.052, going:-0.038, car:-0.036
[joy] frozen:-0.009, slowly:-0.008, road:-0.004, were:-0.004, my:-0.004, relatives:0.003
[love] frozen:-0.000, s... | The words "frozen" contributed significantly to the feeling of fear.
The model chose fear because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] submissive:0.880, feel:-0.063, feeling:0.029, im:0.029, wondering:-0.019, than:-0.016
[joy] submissive:-0.311, feel:0.092, feeling:0.065, im:0.059, why:-0.049, wondering:-0.046
[love] submissive... | The words "submissive" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: tech
[business] Cisco:-0.201, network:-0.178, products:-0.107, business:-0.095, smaller:0.085, cost:0.084
[entertainment] Cisco:-0.002, operating:-0.001, Company:-0.001, network:-0.001, a:-0.001, products:-0.001
... | The words "Cisco", "network" contributed significantly to the feeling of tech.
The model chose tech because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: sadness
[sadness] boring:0.764, skipping:0.091, im:0.070, feeling:0.067, quite:-0.050, maps:-0.030
[joy] boring:-0.272, feeling:0.105, four:-0.043, skipping:-0.037, for:0.033, because:0.033
[love] boring:-0.181, ... | The words "boring" contributed significantly to the feeling of sadness.
The model chose sadness because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] clever:-0.110, situation:-0.087, that:0.035, semi:-0.033, about:-0.033, the:-0.026
[joy] clever:0.762, feeling:0.269, situation:-0.115, wasnt:-0.085, but:-0.073, today:0.062
[love] clever:-0.046, fe... | The words "clever", "feeling" contributed significantly to the feeling of joy.
The model also considered anger due to words like "situation".
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: joy
[sadness] valued:-0.062, decide:0.026, what:-0.024, feel:-0.020, made:-0.018, homework:-0.017
[joy] valued:0.482, feel:0.180, decide:0.108, made:0.086, because:-0.071, do:-0.060
[love] valued:-0.107, feel:0.0... | The words "valued", "feel" contributed significantly to the feeling of joy.
The model chose joy because the positively contributing words were more relevant than those for other labels. | true |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: tech
[business] IBM:-0.153, market:0.088, Corp:0.070, storage:-0.061, equipment:-0.060, allow:-0.059
[entertainment] IBM:-0.000, data:-0.000, storage:-0.000, Data:-0.000, Challenge:-0.000, aim:-0.000
[politics] I... | The words "IBM" contributed significantly to the feeling of tech.
The model chose tech because the positively contributing words were more relevant than those for other labels. | false |
Instruction: Use only words that positively contribute to the labels from the input field.
Predicted label: tech
[business] software:-0.068, Microsoft:-0.065, Google:-0.044, in:-0.039, isnt:0.039, engine:-0.033
[entertainment] software:-0.010, Microsoft:-0.009, search:-0.006, its:-0.006, underdog:-0.005, 39:0.004
[poli... | The words "software" contributed significantly to the feeling of tech.
The model chose tech because the positively contributing words were more relevant than those for other labels. | false |
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