Raghu150999/UnsupervisedABSA
A BERT based three-step mixed semi-supervised model, which jointly detects aspect and sentiment in a given review sentence.
This tool helps businesses automatically understand customer feedback from written reviews. You provide a collection of review sentences and a few example words for key topics and sentiments. It then identifies what specific aspects (like 'battery' or 'food') customers are talking about and their sentiment (positive or negative) towards each, giving you structured insights from unstructured text.
No commits in the last 6 months.
Use this if you need to quickly categorize and understand sentiment for specific product or service features mentioned in a large volume of customer reviews, without manually labeling extensive datasets.
Not ideal if you require highly nuanced sentiment analysis beyond positive/negative or need to analyze aspects not covered by your initial seed words.
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9
Forks
3
Language
Python
License
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Category
Last pushed
Feb 21, 2022
Commits (30d)
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