SENTIMENT ANALYSIS MICROSERVICE
Our Sentiment Analysis Microservice enables enterprises to monitor any type of written communication: social media posts and messages, emails, incoming customer service requests, surveys, or any other type of “unstructured text”. Unlock key benefits from this data to better understand your customers’ behavior.
Adding contextual data to other systems
Sentiment recognition can serve as a cutting-edge enhancement to a wide variety of systems, including recommendation engines, social media monitoring platforms, customer engagement/segmentation solutions, and communication channels. No matter what the purpose of a given system is, adding the sentiment piece can become crucial to better understanding customers’ intent and improving each customer’s overall experience with your brand.
Our ML-powered sentiment detection module increases the accuracy of sentiment recognition from 70% (when using traditional methods) to over 95% of correctly classified messages.
The Science Behind the Solution
WordNetLemmatizer and custom tokenizer are used for data pre-processing (text tokenization and lemmatization). For the actual sentiment analysis, there are two different approaches, chosen automatically depending on the accuracy results:
- Hybrid ensemble classification model
Sentiment microservice uses three independent classifiers combined. Plus, a voting process determines the final sentiment detected. Techniques used in this approach include Bayesian classifier, Support Vector Machine and linear regression. The voting process is handled by the EnsembleVoteClassifier.
- Neural net model
In this model, free-text data is first vectorized using Word2Vec model and then passed through a trained LSTM (long short-term memory) artificial neural network with an output of the probability of belonging to the given sentiment class.
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