Sentiment Analysis is the method of analyzing digital text to identify the emotional tone of the message which can be either positive, negative, or neutral. Enterprises process large volumes of textual data (emails, blog comments, social media messages, customer support chat messages etc). A sentiment analysis tool can easily scan a large volume of data to extract the user’s attitude from their message.
Sentiment analysis uses Natural Language Processing (NLP) to classify expressions and mark a confidence score against expressions (positive, negative, or neutral). The solution can be focussed towards sentence based or aspect based analysis to gain insights and enhance customer service and solidify brand reputation.
Why do you need Sentiment Analysis?
Sentiment Analysis Use Cases
- Provide Better Customer Service : Conneqtion has built an AI tool for a large US based logistics partner to help their Customer Support team use sentiment analysis to provide personalized responses depending on the user’s mood and the flow of the conversation. Urgent requests or irate customers are then escalated to human executives and they can handle the conversation accordingly.
- Improve Brand Image : We have been working closely with one of our customer’s marketing team to use sentiment analysis to track their brand mentions and check if any negative comments are posted on social media. The PR team is able to track and be aware of any negative brand stories going around and try to address the complaint in the initial stages before it gets out of hand.
Sentiment analysis is still not 100% accurate and there can be a chance or partial matches. Moreover, sentiment analysis also doesn’t recognize sarcasm at this stage.
OCI Sentiment Analysis currently works on two analysis levels: sentence and aspect.
Currently, there is support for only English and Spanish.
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