I have lexicon with sentiment score, I want to find these words from a tokenised tweets and add the score

Question

Keyword .   Score
fabulous    7.526

excellent   7.247

superb  7.199

alert   7.099

drop    6.922


#Tokenized tweets below

["b'just", 'saw', 'amazon', 'ticwatch', 'pro', '4g/lte', 'smartwatch', 'dual', 'displa', '...', 'mobvoi', '299.00']

["b'amazon", 'pricedrop', 'deal', '\\nprice', 'drop', 'alert', 'camelbak', 'eddy', 'kids', 'vacuum', 'insulated', 'stainless', 'steel', 'bottle', '12', 'oz', 'retro', 'floral\\navg', 'price', '16.00\\nnew', 'price', '12.17\\nprice', 'drop', '23.94', '\\nURL']

For each list i want to see a sum of score that matches a key word E.g

Tweet 1 - 12.22

Tweet 2 - 7

Is there any library which will allow me to find words like this? Any help in this front is appreciated

Answer 1

if you have dataframe of keyword and score, you can use zip function as

list_ = list(df['keyword'],df['score']) 
list_ = [('fabulous',7.526),('excellent',7.247), ('super',7.199),('alert',7.099),('drop',6.922)]
tweet_token = [['fabulous', 'excellent','super','alert','drop'],['super', 'alert']]


sum_ = []
for j in range(len(tweet_token)):
   sum_tweet = 0
   for i  in range(len(list_)):
       for token in tweet_token[j]:
           if token == list_[i][0]:
              sum_tweet += list_[i][1]
   sum_.append(sum_tweet)

#op
print(sum_)
[35.993, 14.298]