Natural Language Processing
Problem: Apply machine learning algorithms to natural language processing (NLP).
Context: For decades, humans have communicated with machines by writing code containing specific rules. Computers have long performed tasks in response to the commands we’ve given them. With advances in NLP, we’re now able to interact with computers using natural languages.
NLP success stories include chatbots effectively handling customer requests 24/7, virtual assistants (e.g., Amazon Alexa) understanding our oral commands to satisfy our needs in real time, and machine translators translating major languages on the fly. Text summarization, content categorization, sentiment analysis, text-to-speech conversion, speech-to-text conversion, and other NLP capabilities are taking human-to-machine communication to the next level.
Model type: A combination of algorithms in NLP
What we did: We used a Python library called TextBlob to serve NLP capabilities as APIs. We operationalized these capabilities with the SnapLogic platform. (More on how we built this demo.)
In this demo, type a sentence(s) in the left pane and click the buttons below the panes to perform different NLP operations such as sentiment analysis. Your results might be delayed due to demand. Please be patient.
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