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.
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.
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.
Google has helped build intense speculation for its October 4 event in San Francisco, where it's expected to reveal new phones aimed at consumers that will power a new virtual reality platform, and possibly other smart home devices. Now that the buzz has reached a football-stadium roar, here comes the hard part: living up to the hype. Google has been teasing the event as one for the history books. A tweet Monday from Hiroshi Lockheimer, the company's senior vice president of Android, Chrome OS and Google Play, turned up the volume on the buzz. We announced the 1st version of Android 8 years ago today.
Google took on rivals Apple, Samsung and Amazon in a new push into hardware Tuesday, launching premium-priced Pixel smartphones and a slew of other devices showcasing artificial intelligence prowess. The unveiling of Google's in-house designed phone came as part of an expanded hardware move by the US company, which also revealed details about its new "home assistant" virtual reality headset and Wi-Fi router system. The San Francisco event marked a shift in strategy for Google, which is undertaking a major drive to make Google Assistant artificial intelligence a futuristic force spanning all kinds of internet-linked devices. "We are evolving from a mobile-first world to an AI-first world," Google chief executive Sundar Pichai said. "Our goal is to build a personal Google for each and every user."