Sentiment classification on Twitter has attracted increasing research in recent years.Most existing work focuses on feature engineering according to the tweet content itself.In this paper, we propose a context-based neural network model for Twitter sentiment analysis, incorporating contextualized features from relevant Tweets into the model in the form of word embedding vectors.Experiments on both balanced and unbalanced datasets show that our proposed models outperform the current state-of-the-art.
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.
Google says it only records interactions with connected devices like the Google Home speaker when we use the "wake word," of "Hey, Google," or "OK, Google." But when using many of the Google smartphone apps with a microphone for voice search, or even Google on the desktop with voice commands, it can actually record every word you say to it – whether you use the wake word or not. The fine print is that you have to click on the microphone in the apps to communicate with Google. Once you do that, Google will start transcribing you, word for word, and storing your commands, in text and audio, as USA TODAY discovered in tests this week. This is similar to Google's monitoring of our keystrokes.
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.