df_interesting_token_sentiment_counts.py

df_interesting_token_sentiment_counts = pd.DataFrame(
    { "token": [ "bug", "bug", "construction", "construction", "deposit", "deposit",
    "elevator", "elevator", "expensive", "expensive", "gym", "gym", "internet",
    "internet", "laundry", "laundry", "leak", "leak", "maintenance", "maintenance",
    "noise", "noise", "package", "package", "parking", "parking", "pet", "pet",
    "pool", "pool", "washer", "washer", ], 
    "sentiment": [ "Negative", "Positive", "Negative", "Positive", "Negative", 
    "Positive", "Negative", "Positive", "Negative", "Positive", "Negative", 
    "Positive", "Negative", "Positive", "Negative", "Positive", "Negative", 
    "Positive", "Negative", "Positive", "Negative", "Positive", "Negative", 
    "Positive", "Negative", "Positive", "Negative", "Positive", "Negative", 
    "Positive", "Negative", "Positive", ], 
    "count": [ 158, 27, 170, 28, 1069, 132, 340, 55, 246, 145, 231, 528, 572, 
    154, 323, 193, 363, 39, 1901, 2071, 205, 76, 261, 135, 1607, 799, 101, 133, 
    469, 644, 225, 140, ], "percentage": [ 0.8540540540540541, 0.14594594594594595, 
    0.8585858585858586, 0.1414141414141414, 0.8900915903413822, 0.10990840965861781, 
    0.8607594936708861, 0.13924050632911392, 0.629156010230179, 0.37084398976982097, 
    0.30434782608695654, 0.6956521739130435, 0.7878787878787878, 0.21212121212121213, 
    0.625968992248062, 0.374031007751938, 0.9029850746268657, 0.09701492537313433, 
    0.4786002014098691, 0.521399798590131, 0.7295373665480427, 0.2704626334519573, 
    0.6590909090909091, 0.3409090909090909, 0.6679135494596842, 0.3320864505403159, 
    0.43162393162393164, 0.5683760683760684, 0.42138364779874216, 0.5786163522012578, 
    0.6164383561643836, 0.3835616438356164,]
})

df_interesting_token_sentiment_counts