Sentiment Analysis APIs Benchmark MonkeyLearn Blog

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Sentiment analysis is a powerful example of how machine learning can help developers build better products with unique features. In short, sentiment analysis is the automated process of understanding if text written in a natural language (English, Spanish, etc.) is positive, neutral, or negative about a given subject. Nowadays, we have many instances where people express opinions and sentiment: tweets, comments, reviews, articles, chats, emails and more. One popular example is Twitter, where real-time opinions from millions of users are expressed constantly. Companies use sentiment analysis on Twitter to discover insights about their products and services.


SuperBowl XLIX in Tweets: Sentiment Analysis of 4 Million Tweets

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This blog was originally published on our Text Analysis blog, the blog post set out to analyze and visualize 4 million tweets collected during Superbowl XLIX. Not surprisingly, Superbowl XLIX generated a huge amount of chatter on social networks with Twitter Estimating that over 28.4 million posts made with terms relating to the Superbowl. At AYLIEN, we collected just under 4 million Tweets from Hashtags, Handles and Keywords we were monitoring. To keep our sample clean, we removed any reTweets and spam from the Tweets collected and only worked with those Tweets that were written in English. We were left with about 3.5 million Tweets to play with.


SuperBowl XLIX in Tweets: Sentiment Analysis of 4 Million Tweets

@machinelearnbot

This blog was originally published on our Text Analysis blog, the blog post set out to analyze and visualize 4 million tweets collected during Superbowl XLIX. Not surprisingly, Superbowl XLIX generated a huge amount of chatter on social networks with Twitter Estimating that over 28.4 million posts made with terms relating to the Superbowl. At AYLIEN, we collected just under 4 million Tweets from Hashtags, Handles and Keywords we were monitoring. To keep our sample clean, we removed any reTweets and spam from the Tweets collected and only worked with those Tweets that were written in English. We were left with about 3.5 million Tweets to play with.


AI is learning how to trump purveyors of 'fake news'

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Remember that video US president Donald Trump tweeted in which he wrestled someone to the ground and started punching them? It was genuine footage of Trump from a popular wrestling show but he had the image doctored to replace the victim's head with the CNN logo and added the hashtag #FraudNewsCNN, just in case we didn't get the memo that he really dislikes the news network. But are these news networks as biased as he thinks? Do Fox News journalists say mostly nice things while those at CNN are busy portraying him in a negative light? Artificial intelligence (AI) in the form of sentiment analysis and stance detection can tell us what is really happening.


Text Analysis blog Aylien

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As you may be aware, we recently boosted our Text Analysis API offering with a cool new feature, Aspect-Based Sentiment Analysis. The whole idea behind Aspect-Based Sentiment Analysis (ABSA) is to provide a way for our users to extract specific aspects from a piece of text and determine the sentiment towards each aspect individually. We've built models for 4 different domains (industries). You can see the domains and the domain specific aspects listed in the image below. We explain it quickly and simply here to help get you up to speed.