Whether you are analyzing Tweets, Articles or Reviews the overall sentiment and knowing whether it is positive, negative or neutral is cool and useful to know. However, Sentiment Analysis gets a lot more valuable and interesting when we can identify what aspects of entities in particular are positive or negative.
Read the full article: Sentiment Analysis of 11 million Tweets
NaSent is a powerful new ‘recursive deep learning’ algorithm that gives machines the ability to understand how words form meaning in context.
People express opinions every day on issues large and small. Whether the topic is politics, fashion or films, we often rate situations and experiences on a sliding scale of sentiment ranging from thumbs up to thumbs down.
As we increasingly share these opinions via social networks, one result is the creation of vast reservoirs of sentiment that could, if systematically analyzed, provide clues about our collective likes and dislikes with regard to products, personalities and issues.
Against this backdrop, Stanford computer scientists have created a software system that analyzes sentences from movie reviews and gauges the sentiments they express on a five-point scale from strong like to strong dislike.
To read the full article, go to Stanford Algorithm Analyzes Sentence Sentiment
Financial firms once blocked Twitter to prevent employees leaking sensitive information. It worked, but there was a problem: reporters and companies were using Twitter to reveal important news, leaving traders to rely on second-hand accounts.
Sentiment analysis tool not only alerts traders of a spike an activity, but also shows if the news is likely to be bad or good.
Read more at: Traders turn to Twitter for market news.
Sentiment Analysis has reached the financial arena. Accoridng to a recent publication in FORBES “Thomson Reuters doesn’t just publish news — it now reads the news as well, measures sentiment with sophisticated linguistic analytics and distributes the results to both algorithmic trading engines and real live human traders.”
Read the full article at Trading On Sentiment Analysis in Forbes.com