Understanding Language in Conversations "The problems addressed in discourse research aim to answer two general kinds of questions: (1) what information is contained in extended sequences of utterances that goes beyond the meaning of the individual utterances themselves? (2) how does the context in which an utterance is used affect the meaning of the individual utterances, or parts of them?"
– Barbara Grosz. Overview of Chapter 6: Discourse and Dialogue, Survey of the State of the Art in Human Language Technology (1996).
Update 23 Aug 2017: Do note that Quantopian platform will no longer support third party broker integration. Please see their website under forum. The title of the post is "Phasing Out Brokerage Integrations". This course provides you with the tools that top hedge funds used. These institutional tools include but are not limited to market data, fundamental data, sentiment analysis data, and more.
Text data preparation is different for each problem. Preparation starts with simple steps, like loading data, but quickly gets difficult with cleaning tasks that are very specific to the data you are working with. You need help as to where to begin and what order to work through the steps from raw data to data ready for modeling.
Natural language processing technologies have become quite sophisticated over the past few years. From tech giants to hobbyists, many are rushing to build rich interfaces that can analyze, understand, and respond to natural language. Amazon's Alexa, Microsoft's Cortana, Google's Google Home, and Apple's Siri all aim to change the way we interact with computers. Sentiment analysis, a subfield of natural language processing, consists of techniques that determine the tone of a text or speech. Today, with machine learning and large amounts of data harvested from social media and review sites, we can train models to identify the sentiment of a natural language passage with fair accuracy.
One of the most difficult challenges reporting and analytics face in public relations measurement is sentiment analysis. Machines attempt textual analysis of sentiment all the time; more often than not, it goes horribly wrong. How does it go wrong? Machines are incapable of understanding context. Machines are typically programmed to look for certain keywords as proxies for sentiment.