I'm trying to assess the sentiment related to specific words that appear in a body of text. Currently I'm using IBM Watson's targeted sentiment analysis (natural_language_understanding.analyze). However, if I have two target words "apple" and "orange", and the sentence "I like apples but I do not like oranges.", the analyzer will give the same score to both targets because the program ultimately uses the targets to identify sentences in the text, and then provides the sentiment of that sentence. Is there an alternative that provides target-level sentiment, rather than sentence-level sentiment, such that a positive numeric score is produced for "apples" and a negative numeric score is produced for "oranges"?
I've looked into using sentiment-targeted_bert_multi_stock from IBM's aspect-oriented sentiment analysis page, however, it seems to only provide a label of sentiment_positive, sentiment_neutral, or sentiment_negative as output.
