input stringlengths 55 55 | output stringlengths 126 325 | emotional bool 1
class |
|---|---|---|
<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 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 0x7b85b0943e10> | The model classified this input as sport because of the words Play.
It also considered business due to Putin, Power.
It also considered entertainment due to 39.
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 0x7b85aaf401d0> | The model classified this input as entertainment because of the words rockers.
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 0x7b85b0942790> | The model classified this input as entertainment because of the words TV, SABC.
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 0x7b85b1b8d850> | 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 0x7b85aaf2e850> | 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> | The model classified this input as business because of the words restructuring, Cuts, business.
It also considered tech due to software, Computer.
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 0x7b85b0acf4d0> | 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 0x7b85b1382490> | 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 0x7b85b13ab2d0> | 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 0x7b85b0aabb50> | 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 0x7b85b0acd290> | The model classified this input as tech because of the words Infoworld, website.
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 0x7b85b13c1310> | 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 0x7b85b0940390> | 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 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 0x7b85b09398d0> | 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 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 0x7b85b1762150> | It also considered entertainment due to s.
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 0x7b85b0af6050> | 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 0x7b85b0960510> | 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 0x7b85aaf52810> | 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 0x7b85b097f1d0> | The model classified this input as tech because of the words format, DLT.
It also considered business due to bought, rival, Certance, Rival.
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 0x7b85b0948650> | 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 0x7b85b0949190> | 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 |
<lime.explanation.Explanation object at 0x7b85b1746350> | The model classified this input as business because of the words Geodynamic, Institute.
It also considered sport due to venues.
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 0x7b85b098bd10> | The model classified this input as politics because of the words reelection, campaign.
It also considered tech due to Site.
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 0x7b85b0af6990> | 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 0x7b85b0af6990> | 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 0x7b85b0af6990> | 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 0x7b85b0af6990> | 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 0x7b85aaf53790> | 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 0x7b85b0949d90> | 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 0x7b85b17c1590> | The model classified this input as sport because of the words Georgia.
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 0x7b85b1356310> | 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 0x7b85b0962dd0> | 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 0x7b85b097f1d0> | 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 0x7b85b13c9190> | The model classified this input as business because of the words Logistics.
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 0x7b85b0917810> | 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 |
<lime.explanation.Explanation object at 0x7b85aaf2d190> | 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 0x7b85b13c23d0> | The model classified this input as politics because of the words Sharon.
It also considered business due to Israeli.
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 0x7b85b0961450> | 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 0x7b85b09fb250> | The model classified this input as sport because of the words Arsenal, Manchester.
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 0x7b85b097ffd0> | 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 0x7b85b0948650> | The model classified this input as business because of the words 39.
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 0x7b85b096a950> | 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 0x7b85b178f710> | It also considered politics due to Rumsfeld.
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 0x7b85b096b790> | 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 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 0x7b85b1b8d850> | The model classified this input as sport because of the words team, manager, training.
It also considered entertainment due to Art.
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 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 0x7b85b0ace290> | 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 0x7b85b0962790> | 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 0x7b85b13c86d0> | 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 0x7b85bedeedd0> | 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 0x7b85b09cfa50> | 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 0x7b85b0af5a90> | 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 0x7b85aaf3fad0> | 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 0x7b85aaf095d0> | The model classified this input as tech because of the words peer, p2p.
It also considered entertainment due to Picture, Motion.
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 0x7b85aaf3d610> | 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 0x7b85b09ec590> | 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 0x7b85b13c9110> | The model classified this input as tech because of the words Skygazers, astronomers.
It also considered entertainment due to eclipse, 39, fans.
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 0x7b85b09cfa50> | The model classified this input as politics because of the words Canada, Iraq.
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 0x7b85bedab2d0> | 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 0x7b85b13b83d0> | 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 0x7b85bedee7d0> | The model classified this input as entertainment because of the words Recording.
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 0x7b85bedec0d0> | The model classified this input as sport because of the words game, streak, win.
It also considered entertainment due to midnight, Revere.
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 0x7b85b1356210> | 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 0x7b85b13c1050> | 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 0x7b85b13c31d0> | 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 0x7b85b13b2c90> | The model classified this input as entertainment because of the words origami, 39.
It also considered business due to down, yesterday, paper, government.
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 0x7b85b13bb4d0> | 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 0x7b875b455510> | 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 0x7b85b178e1d0> | The model classified this input as tech because of the words site, Google.
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 0x7b85b13c0c50> | 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 0x7b85b097ffd0> | 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 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 0x7b85b13b16d0> | The model classified this input as entertainment because of the words VIRGIN, Branson.
It also considered business due to 800million, pledged, BOSS.
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 0x7b85b178d750> | 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 0x7b85b0940bd0> | The model classified this input as politics because of the words party, election, National.
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 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 0x7b863da7fed0> | The model classified this input as business because of the words administration.
It also considered politics due to Bush.
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 0x7b85b1355c90> | 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 0x7b85b1355c90> | The model classified this input as politics because of the words Bush.
It also considered business due to President.
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 0x7b85b1b8d3d0> | 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 0x7b85b097db10> | The model classified this input as entertainment because of the words Disney, talent.
It also considered business due to shareholder.
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 0x7b85b098b590> | 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 0x7b85b1381390> | The model classified this input as tech because of the words cafes.
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 0x7b863da7fed0> | 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 0x7b85b1763590> | The model classified this input as business because of the words Bangladesh.
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 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 0x7b85aaf084d0> | 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 0x7b85be105c90> | 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 0x7b85b0aabc90> | The model classified this input as tech because of the words browsers, Spam.
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 0x7b85b09492d0> | The model classified this input as tech because of the words Microsoft, software.
It also considered business due to market.
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 0x7b86521fc850> | 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 0x7b85b1394e50> | 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 0x7b85b098ac10> | 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 0x7b863da7fed0> | 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 0x7b85b13c8c90> | The model classified this input as sport because of the words stadium, bowls.
It also considered entertainment due to ambience.
These weaker contributions were outweighed by the stronger relevance of terms linked to sport, leading to the final prediction. | false |
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