Sentiment analysis is opinion mining of text content which identifies and extracts subjective information in source materials. ParallelDots Sentiment analysis API provides a very accurate analysis of the overall sentiment of the text content which can be widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service.

I never participated in any hackathon before. This is because I thought hackathon is just a waste of time. But this time, I thought why not give it a try and see what happens. We started with small intro and then everyone was invited to pitch a project to work for the rest of the day or host a session or talk about something. Few people pitched ideas and some of them hosted few session.

Humans are fairly sophisticated when it comes to understanding the complex meanings beneath the spoken or written word. For example, we can tell that a statement like, "My car had a flat. Brilliant!" is sarcastic, not actually brilliant. And with the help of machine learning, computers are beginning to get better at reading between the lines of our tweets, Facebook updates, and email messages, resulting in a new kind of analytics: sentiment analysis. Sentiment analysis, also known as opinion mining, seeks to determine the attitude of an individual or group regarding a particular topic or overall context – be it a judgment, evaluation, or emotional reaction – from text, video, or audio data.

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The artificial intelligence company originally launched the site in mid July focussing on the Republican Party's Donald Trump and the Democratic Party's Hillary Clinton. Aicial Co-Founder and CEO, Troy Kelly said with ongoing accusation of media bias from all participants in the presidential election, we wanted to road test our platform and provide easily accessible, unbiased monitoring and analysis of social media traffic at 2016tweets.live. "Aicial is bringing clarity to an environment often too confusing and noisy for the average person to make sense of. We cut through the confusion to give the public insights based on data alone; no marketing, no agenda. After surging interest in the platform, the company continued development of the engine to provide the public with expanded insights; subsequently opening the platform to all Presidential nominees of the 2016 election with the inclusion of Jill Stein for the Green Party and Gary Johnson for the Libertarian Party.

Building on a raft of machine learning-related announcements it made earlier in the year, Google has just launched two new machine learning APIs into beta. The most exciting of the two looks to be the new Google Cloud Natural Language API, which is aimed at helping developers build applications that understand human language. The API works by letting users reveal the structure and meaning of a text, and is available in English, Spanish and Japanese for now, with the promise of support for additional languages to come. In a second blog post focusing on the Cloud Natural Language API, Google demonstrates how it can be used to analyze a report in the New York Times. Per Google's example, you can perform sentiment analysis on various blocks of text using the API, run the results in a BigQuery table, and then use Google Data Studio to visualize them: In a second example, Google showed how digital marketers can use the sentiment analysis capabilities in the Cloud Natural Language API to monitor customer calls to service centers and online reviews.

Extracting value from a universe of data, analysing sentiment around company names (equities) or about anything else (macro), is a complex journey and we are only about 5% down that road. The parameters are evolving by which an ever-expanding data set, including the likes of Twitter, pictures, text, video is processed; relying on experts versus the wisdom of the crowd; sentiment derived from a "bag of words", as opposed to structured linguistic analysis. Last week's Unicom conference, AI, Machine Learning and Sentiment Analysis Applied to Finance (July 14) brought together a group of experts in this area. Professor Gautum Mitra, OptiRisk Systems introduced Elijah DePalma and James Cantarella, Thomson Reuters; Pierce Crosby, StockTwits; Anders Bally, Sentifi; Peter Hafez, RavenPack; Stephen Morse, Twitter. DePalma differed somewhat from the others because the Thomson Reuters sentiment engine uses only accredited Reuters news data, rather than raw social media chatter.

After we Save and Activate our process, we are now ready to test it.

Extracting value from a universe of data, analysing sentiment around company names (equities) or about anything else (macro), is a complex journey and we are only about 5% down that road. The parameters are evolving by which an ever-expanding data set, including the likes of Twitter, pictures, text, video is processed; relying on experts versus the wisdom of the crowd; sentiment derived from a "bag of words", as opposed to structured linguistic analysis. Last week's Unicom conference, AI, Machine Learning and Sentiment Analysis Applied to Finance (July 14) brought together a group of experts in this area. Professor Gautum Mitra, OptiRisk Systems introduced Elijah DePalma and James Cantarella, Thomson Reuters; Pierce Crosby, StockTwits; Anders Bally, Sentifi; Peter Hafez, RavenPack; Stephen Morse, Twitter. DePalma differed somewhat from the others because the Thomson Reuters sentiment engine uses only accredited Reuters news data, rather than raw social media chatter.