Natural language analysis and understanding of meaning are gaining momentum in recent years and rightly so: language reflects reality. When you look at a period, you also look at the language. It is relatively easy to identify a period by language. Even a word that appears in one period, and not in another, indicates and explains quite a bit. Language is interwoven within us: it is impossible to dream without it, impossible to think without it and cannot be produced without it, because every thought uses words. You can’t think of anything without understand it in words. Alongside this, in recent years we have become unstructured because everything cannot be formatted. The data that is being accumulated today requires more work to extract the insight because they are in the language (also images, and videos are another way of displaying language) and less in ready repositories.In fact, the language becomes the world’s largest “database”. And in order to stay up to date there is no escape from knowing natural language.
Alongside the great potential, this is one of the biggest challenges in the world of computing. Language is the human brain. It reflects a culture. And this is also misleading: two people who read the same paragraph may interpret the script differently. Obviously when it comes to a computer mission. And so understanding natural language is a complex and challenging task.
Many of us are therefore engaged in language analysis — extracting entities, connections, insights, sentiment and so on. This is the hot thing today in the world of artificial intelligence. The tools are many and varied — from dictionaries to machine learning. With machine learning and AI development, good results can be achieved. The approach is to take a sentence or paragraph and try, in various ways, whether statistical analysis or machine learning, to understand the sentence or the sentiment. The goal is to turn language into insight and insight into data that can be used. And so, it happens, when we look at an article, we take a measure of coverage and accuracy, and try to bring ourselves closer to a state of almost complete understanding. This is really artificial intelligence. The day the computer could decipher an unsuspecting intent in the paragraph or alternatively identify cynicism, the world is accepting the next revolution with open arms.
But this is not easy task. Suffice it to say that building a Sentiment Data set is not an easy task for human beings, however intelligent and intellectual it may be. Furthermore, the accuracy coverage results for one Data Set will probably be different for another Data Set, even if it is the same domain. What’s more — there are statements that are said without words. There are sentences that intersect with the invocation of social conventions. There are body movements that translate into words and in conjunction with words that are said in practice, take on a completely different meaning if it were not for a combination of cultural convention. In short …. You understand what I am striving for: context.
Finding the context that sits “above” the words may direct us to the right domain and with it we can make a meaningful way.
A sentence like: “They beat them“, gets a different meaning whether it is in a sports or political story or criminal event …. and all before talking about irony, sarcasm, allusions, etc.
So I recommend that when we analyse text, we try to ease yourself a little, and focus the text around the context. Finding the context will help us analyse the phrases and sentences more accurately. True, the domains are intermingled. The sport has received concepts of war: “to be or to cease”, “this is the last battle”, “we will not be able to return home without victory” and more. But it is still possible, possible and necessary to find the context. Given the context, we can better understand the language.
How does one find the context? Let’s go from easy to hard: It is easier to understand the space — the time and the place, which is likely to help to understand the culture represented. More difficult, it is to try to decipher and understand the situation. This can be done by analysing the image, by casing data, who wrote, when, and general categorisation of content. By given that we understand this, it will be easier to analyse the text in a more accurate and insightful way.
In summary, text analysis is the hottest thing today, and not for nothing. Accuracy coverage data are good metrics, but not sure they will be with same results in other domain. Finding the context even before approaching this complicated task can help. Because the context of words takes on a different meaning. There are quite a few ways to do this, but if you understand the situation, we can pinpoint ourselves.
