input stringlengths 55 55 | output stringlengths 115 413 | emotional bool 2
classes |
|---|---|---|
<lime.explanation.Explanation object at 0x7b85b1356210> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b1789990> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fea72bb50> | The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b09c71d0> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b0968390> | These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7f87e50> | The model also considered disapproval due to words like dont, good, valued.
The model also considered neutral due to words like cause.
The model chose disappointment because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7e8f4d0> | The words invite 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 |
<lime.explanation.Explanation object at 0x7b5fd7e3f350> | The model also considered joy due to words like entertained.
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7e88c90> | The words lethargic contributed significantly to the feeling of disappointment.
The model also considered neutral due to words like and.
The model chose disappointment because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b097c190> | The model classified this input as business because of the words cashed, checks, bank.
It also considered tech due to technology.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7e55d10> | The words no, way contributed significantly to the feeling of disapproval.
The model also considered disappointment due to words like ungrateful.
The model chose disapproval because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fdc418350> | The words pulled contributed significantly to the feeling of neutral.
The model also considered disapproval due to words like like.
The model also considered disappointment due to words like shaken.
The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7f44fd0> | The model also considered neutral due to words like can.
The model chose realization because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b1745290> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fdd5e9210> | The model also considered admiration due to words like brave.
The model chose surprise because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7edda10> | The words haha contributed significantly to the feeling of amusement.
The model also considered annoyance due to words like bitch.
The model chose amusement because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fdc1e1650> | 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 emotions. | true |
<lime.explanation.Explanation object at 0x7b85b09634d0> | The model classified this input as business because of the words price.
It also considered tech due to Google.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7e60110> | The words love contributed significantly to the feeling of love.
The model chose love because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7e17650> | The words loved contributed significantly to the feeling of love.
The model also considered neutral due to words like that.
The model chose love because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7ff8650> | The model also considered realization due to words like i.
The model chose approval because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7e62090> | The words nervous contributed significantly to the feeling of nervousness.
The model chose nervousness because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7e570d0> | The words want, don, him contributed significantly to the feeling of disapproval.
The model also considered annoyance due to words like disrespected.
The model chose disapproval because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b0ac0b10> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7f37290> | The words surprised, stunned contributed significantly to the feeling of surprise.
The model chose surprise because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b1356210> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b1b8d3d0> | The model classified this input as business because of the words President.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fdcd29ad0> | The words charming contributed significantly to the feeling of admiration.
The model chose admiration because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7fc3150> | The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5feae50290> | The words happy contributed significantly to the feeling of joy.
The model chose joy because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7e55c90> | The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b1b50c10> | The model classified this input as business because of the words Reuters.
It also considered politics due to Hamas.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7fb0c10> | The words rapes, i, feel contributed significantly to the feeling of disgust.
The model also considered disapproval due to words like don.
The model chose disgust because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b13959d0> | The model classified this input as tech because of the words Human.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b17459d0> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7ea39d0> | The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b0962150> | These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b13952d0> | These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b13c0dd0> | These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7e54fd0> | The words sour, ignored 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 |
<lime.explanation.Explanation object at 0x7b5fd7f271d0> | The words unsure contributed significantly to the feeling of confusion.
The model also considered realization due to words like understand.
The model also considered surprise due to words like wondering.
The model chose confusion because the positively contributing words were more relevant than those for other emotions... | true |
<lime.explanation.Explanation object at 0x7b85b13c3610> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b1788f50> | These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b13baad0> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b13b3fd0> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7fb0b90> | The words not, true contributed significantly to the feeling of disapproval.
The model also considered approval due to words like true.
The model chose disapproval because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b1746850> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7f5d3d0> | The words love contributed significantly to the feeling of love.
The model chose love because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7f1e4d0> | The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b0915050> | These weaker contributions were outweighed by the stronger relevance of terms linked to politics, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7ebd710> | The words well, feeling contributed significantly to the feeling of relief.
The model also considered admiration due to words like pretty, in.
The model chose relief because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7ea1a90> | The words happy contributed significantly to the feeling of joy.
The model chose joy because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7fe7850> | 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 |
<lime.explanation.Explanation object at 0x7b85bf6b0550> | These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b13a8f90> | These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b09fba10> | It also considered politics due to Putin, Independent.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b0acc150> | The model classified this input as sport because of the words playing, Nationals.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fdcdf5ad0> | The words guilty contributed significantly to the feeling of remorse.
The model also considered sadness due to words like feel, i.
The model chose remorse because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b0968350> | The model classified this input as entertainment because of the words rally.
It also considered business due to rally.
These weaker contributions were outweighed by the stronger relevance of terms linked to entertainment, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b0acec50> | The model classified this input as politics because of the words Basescu.
It also considered business due to presidential, Romania.
These weaker contributions were outweighed by the stronger relevance of terms linked to politics, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7e72250> | The words lucky, feel contributed significantly to the feeling of joy.
The model also considered admiration due to words like lucky.
The model also considered caring due to words like care.
The model chose joy because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7f74d90> | The words fearless, i contributed significantly to the feeling of fear.
The model chose fear because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b6069b19410> | The words feel, dont contributed significantly to the feeling of disappointment.
The model also considered disapproval due to words like dont, want.
The model also considered neutral due to words like kinda.
The model also considered desire due to words like want.
The model chose disappointment because the positively c... | true |
<lime.explanation.Explanation object at 0x7b5fd7f75150> | The words indecisive, feel contributed significantly to the feeling of embarrassment.
The model also considered excitement due to words like crazily.
The model also considered annoyance due to words like indecisive, crazily.
The model chose embarrassment because the positively contributing words were more relevant than... | true |
<lime.explanation.Explanation object at 0x7b85b0ace990> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7f66b90> | The words surprised contributed significantly to the feeling of surprise.
The model chose surprise because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b0acccd0> | These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fdd14b0d0> | The words glad contributed significantly to the feeling of joy.
The model also considered realization due to words like woke.
The model also considered relief due to words like glad, behind.
The model chose joy because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b0aabb50> | These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b17615d0> | These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85cff83450> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7f76f50> | The words bitch contributed significantly to the feeling of annoyance.
The model also considered anger due to words like bitch.
The model chose annoyance because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7fc2090> | The words lousy 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 |
<lime.explanation.Explanation object at 0x7b85b1b50c10> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b17ed0d0> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b8632ebb310> | These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b097e5d0> | The model classified this input as tech because of the words RealNetworks.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b863da7c6d0> | These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7ebd710> | The words my, ready contributed significantly to the feeling of desire.
The model also considered sadness due to words like feeling, craving, im.
The model chose desire because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7f1cc90> | The words feel, fault, unsuccessful contributed significantly to the feeling of disappointment.
The model also considered love due to words like i, like.
The model chose disappointment because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7fb0c10> | The model classified this input as tech because of the words Infoworld.
These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b13c2f10> | These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7f37790> | The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7f38a90> | The words crappy, feeling 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 |
<lime.explanation.Explanation object at 0x7b85b13b3850> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b0940590> | These weaker contributions were outweighed by the stronger relevance of terms linked to tech, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7ea1a90> | The words scared contributed significantly to the feeling of fear.
The model chose fear because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fdcdf5110> | The words hesitant, feel contributed significantly to the feeling of nervousness.
The model chose nervousness because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b5fd7f3bcd0> | The model classified this input as sport because of the words coach, offseason.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fd7e62a50> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fdcd0b5d0> | The model also considered joy due to words like joy, warmth.
The model also considered love due to words like love.
The model also considered optimism due to words like hope.
The model chose excitement because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b13bb250> | The model classified this input as business because of the words Bangladesh.
It also considered politics due to paralysed.
These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b0acfa50> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b0960990> | The model classified this input as entertainment because of the words prostitute, soccer.
It also considered sport due to involving, captain.
These weaker contributions were outweighed by the stronger relevance of terms linked to entertainment, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b09c7690> | These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fe9c31650> | The model chose neutral because the positively contributing words were more relevant than those for other emotions. | true |
<lime.explanation.Explanation object at 0x7b85b13baf10> | These weaker contributions were outweighed by the stronger relevance of terms linked to business, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b0aabb50> | These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b85b0940750> | The model classified this input as sport because of the words coach.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
<lime.explanation.Explanation object at 0x7b5fdd163310> | The words realize contributed significantly to the feeling of realization.
The model also considered sadness due to words like heartbreaking.
The model chose realization because the positively contributing words were more relevant than those for other emotions. | true |
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