Sentiment Analysis of 11 Million Tweets from Apple Live 2014 - Going beyond positive and negative


This blog was originally published on our Text Analysis blog, the blog post set out to analyze and visualize 11 million tweets collected around the time of and during Apple Live 2014. Apple Live probably got off to the worst start possible earlier this year. Most of us who tried to log on to watch the much-anticipated launch were first, forced to watch the live feed in Safari and second, greeted with the TV Truck Schedule Screen... To add to this Apple also made a complete mess of the audio. We were left sitting refreshing the page, waiting for the stream to start while being subjected to an audio visual nightmare, described brilliantly by this "fan" below: To simulate the #applelive experience, open up several separate YouTube vids, play them simultaneously, minimize, stare at a test pattern. At AYLIEN, we gathered 11 million tweets mentioning'Apple', 'iPhone', 'iOS', 'iPad', 'Mac', 'iPod', 'Macbook', 'iCloud', 'OS X', 'iWatch' and '#AppleLive' from the 4th of September to the 10th of September with a view of analyzing the tweets to gain insight into the voice of Apple Followers.

Adding machine learning to a serverless data analysis pipeline Google Cloud Big Data and Machine Learning Blog Google Cloud Platform


In the right architecture, machine-learning functionality takes data analytics to the next level of value. Editor's note: This guest post (translated from Italian and originally published in late 2016) by Lorenzo Ridi, of Google Cloud Platform partner Noovle of Italy, describes a POC for building an end-to-end analytic pipeline on GCP that includes machine-learning functionality. "Black Friday" is traditionally the biggest shopping day of the year in the United States. Black Friday can be a great opportunity to promote products, raise brand awareness and kick-off the holiday shopping season with a bang. During that period, whatever the type of retail involved, it's also becoming increasingly important to monitor and respond to consumer sentiment and feedback across social media channels.

Sentiment Analysis on Social Network Data (Twitter, Facebook, etc.)


Sentiment analysis is a useful service for just about any business. It is always valuable to know whether your customers are saying positive or negative things about you. This gives you more flexibility to start with their sample and then tweak it to your needs. Then you would deploy it yourself and call it yourself.

Analyzing the Political Sentiment of Tweets in Farsi

AAAI Conferences

We examine the question of whether we can automatically classify the sentiment of individual tweets in Farsi, to determine their changing sentiments over time toward a number of trending political topics. Examining tweets in Farsi adds challenges such as the lack of a sentiment lexicon and part-of-speech taggers, frequent use of colloquial words, and unique orthography and morphology characteristics. We have collected over 1 million Tweets on political topics in the Farsi language, with an annotated data set of over 3,000 tweets. We find that an SVM classifier with Brown clustering for feature selection yields a median accuracy of 56% and accuracy as high as 70%. We use this classifier to track dynamic sentiment during a key period of Irans negotiations over its nuclear program.

Happy, Nervous or Surprised? Classification of Human Affective States in Social Media

AAAI Conferences

Sentiment classification has been a well-investigated research area in the computational linguistics community. However, most of the research is primarily focused on detecting simply the polarity in text, often needing extensive manual labeling of ground truth. Additionally, little attention has been directed towards a finer analysis of human moods and affective states. Motivated by research in psychology, we propose and develop a classifier of several human affective states in social media. Starting with about 200 moods, we utilize mechanical turk studies to derive naturalistic signals from posts shared on Twitter about a variety of affects of individuals. This dataset is then deployed in an affect classification task with promising results. Our findings indicate that different types of affect involve different emotional content and usage styles; hence the performance of the classifier on various affects can differ considerably.