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.


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

@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.


Why is Sentiment Analysis important from a business perspective? - AYLIEN

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Sentiment essentially relates to feelings; attitudes, emotions and opinions. Sentiment Analysis refers to the practice of applying Natural Language Processing and Text Analysis techniques to identify and extract subjective information from a piece of text. A person's opinion or feelings are for the most part subjective and not facts. Which means to accurately analyze an individual's opinion or mood from a piece of text can be extremely difficult. With Sentiment Analysis from a text analytics point of view, we are essentially looking to get an understanding of the attitude of a writer with respect to a topic in a piece of text and its polarity; whether it's positive, negative or neutral.


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.