As we have mentioned before in this blog, structured data is invaluable for businesses looking to extract relevant information from text. Whereas the problem used to be how to get enough useful data for the results to be meaningful, the challenge today is how to process the large amounts of it that are available. This task becomes almost impossible to achieve without the right tools because,on top of being vast, the data is most often unstructured. At Bitext, we offer a range of Text Analytics Tools that allow users to structure their raw data to extract the information most relevant to their goals.
This post dives into one of the topics of a previous post "Machine Learning & Deep Linguistic Analysis in Text Analytics". We referred to the strong points of Machine Learning technology for insight extraction. We also stated that using variations of classical "bag of words" models limits the ability of Machine Learning to extract insights. Here we go into some detail on this last statement.
Real comments analized
See how text is structured
Short texts are not an issue
Time flies for everyone — particularly while working. We always have the sensation of having much to do and not enough time to do it all. That is why it’s necessary to include in our daily routine tools that allow us to save time. Preparing large amounts of datasets and analyzing them to extract insights is one of those time consuming tasks.
Last week’s post explored the differences between polarity and topic-based sentiment analysis (have a look here if you missed it). This post goes a step further, drilling down into a real data set to demonstrate the true value of topic-based sentiment analysis.