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Prior work on sentiment analysis using weak supervision primarily focuses ondifferent reviews such as movies (IMDB), restaurants (Yelp), products(Amazon).~One under-explored field in this regard is customer chat data for acustomer-agent chat in customer support due to the lack of availability of freepublic data. Here, we perform sentiment analysis on customer chat using weaksupervision on our in-house dataset. We fine-tune the pre-trained languagemodel (LM) RoBERTa as a sentiment classifier using weak supervision. Ourcontribution is as follows:1) We show that by using weak sentiment classifiersalong with domain-specific lexicon-based rules as Labeling Functions (LF), wecan train a fairly accurate customer chat sentiment classifier using weaksupervision. 2) We compare the performance of our custom-trained model withoff-the-shelf google cloud NLP API for sentiment analysis. We show that byinjecting domain-specific knowledge using LFs, even with weak supervision, wecan train a model to handle some domain-specific use cases better thanoff-the-shelf google cloud NLP API. 3) We also present an analysis of howcustomer sentiment in a chat relates to problem resolution.

Customer Sentiment Analysis using Weak Supervision for Customer-Agent Chat