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RubyConf 2019 - What's Love Got To Do With It? Ruby and Sentiment Analysis by Ben Greenberg The societies we live in, the companies we work for, the media we consume and much more are all shaped by words. Words can infuriate, enlighten, bring joy or cause great despair. Natural Language Understanding gives us a window into analyzing the words that surround us for sentiment and tone. How does Natural Language Understanding work? What insights can we glean from the data it provides? We will take a dive into understanding how this technology works, and apply it in a Ruby app that connects Natural Language Understanding analysis, the daily headlines and social media all in one. Get ready to learn some Ruby, some human language insights and how they all intertwine! #rubyconf2019 #confreaks
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In the engaging presentation titled "What's Love Got To Do With It? Ruby and Sentiment Analysis," Ben Greenberg explores the intersection of linguistics, Ruby programming, and natural language understanding. The session highlights the importance of analyzing human language through sentiment analysis and demonstrates its application within a Ruby application. Key points discussed include: - **Introduction to Linguistics and Its Importance**: Greenberg begins by emphasizing that language shapes our thoughts and perceptions, illustrated through an example comparing English and Hebrew. - **Understanding Natural Language Processing (NLP)**: He defines NLP and sentiment analysis as tools to analyze language similarly to human understanding. - **Challenges of Sentiment Analysis**: Various linguistic challenges are discussed, such as polarity, context, and contradictory emotions that complicate machine interpretation of human language. - **Practical Application**: Greenberg introduces a project where attendees will build a Ruby app that sends WhatsApp messages to gauge the mood of news headlines related to specific topics. - **API Integration**: He explains how to incorporate multiple APIs: the News API for gathering headlines, IBM Watson for sentiment analysis, and Nexmo's Messages API to relay results. - **Building the App**: The session outlines technical details from setting up the gem file to defining routes and methods for processing incoming messages and conducting sentiment analysis through the app. - **Final Results**: An example is presented by analyzing the sentiment of news about Ruby, illustrating a positive sentiment of 70% joy. Greenberg's session effectively combines theoretical learning with practical programming, demonstrating how sentiment analysis can provide insights into the emotional landscape of news media. The main takeaway is the potential of Ruby and NLP in creating meaningful applications that bridge technology and human language understanding.
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