Machine Learning is a versatile toolbox to solve non-linear problems such as processing text and words. Therefore, a lot of applications use Natural Language Processing (NLP) to tackle the challenges of the everyday world.
Natural Language Processing can be used in many ways when it comes to text classification. The most common text classification applications are sentiment analysis, spam recognition, document labeling, and Named Entity Recognition (NER).
Classifying the sentiment of a text, comment or article is a challenging subjective task even for a real human. That’s where NLP comes to the picture. A sentiment analysis model, which is a common use of NLP, can tell us whether a text has negative or positive polarity. The level of classification can vary from the whole document through the sentence down to the word level. So, what are the most common applications of sentiment analysis?
With the use of sentiment analysis, we can classify things like the reviews of our company or its products. Another use of sentiment analysis is to poll people’s opinions based on their comments and social media posts. A more complex challenge is to divide the polarity classification into classes with a larger distribution such as very positive, positive, neutral, negative, very negative.
A Natural Language Processing Machine Learning model can do these tasks for us. These models usually try to define relationships between words and sentences. To train such a model, a large dataset is needed, where the training text is already annotated with a proper polarity attribute.
Text classification does not stop at sentiment analysis. As already mentioned, Natural Language Processing models can classify large chunks of texts. They can tell about an email, whether it is a spam or not.
A well-trained text classification model can also label content from news outlets. These websites can publish just basic announcements or long portrait articles about someone. Thanks to text classification, they don’t have to worry about manual article tagging, this is something ML models can do for them.
Furthermore, NLP models can distinguish subjective articles from objective ones. It is also possible to classify questions, whether they ask about location, person, or numeric information.
One of the text classification architectures is called Named Entity Recognition (NER). It can label named entities i. e Google is a company, Bill Gates is a person, etc..
As globalization opens new markets and user bases from different continents, translating languages is becoming more significant. Since a lot of word corpuses (structured database of words used in Machine Learning training) and text are available in different languages, we have enough data to train Machine Learning models to translate words and whole documents.
Once your business starts using these models, your competitive advantage will skyrocket and your user base and customer pool will be multiplied. You might be wondering now: why is being multi-language so important for my business?
The answer is simple. Communicating with customers and potential business partners will become easier with the use of NLP translation models. The complex models create a more organic experience when it comes to reading (or hearing) translated text. Every customer will feel that they matter because the company cares about communicating in their language. Thus, the customer will get more engaged and more likely to convert.
Given all that, it’s worth investing in translating models, especially for international businesses. And the good news is: the amount of foreign text to be found on the internet is constantly growing. Thanks to this, we can expect translating models to improve, making the translation of complex texts easier in the future.
Having superb customer support is a top priority for many businesses. Whether your business is an e-commerce store or an IT solution provider, 24/7 live support is invaluable. However, hiring people to be present day & night is becoming harder. I know what you’re thinking of right now: your Frequently Asked Questions (FAQ) section must do the job. It actually doesn’t. Your users expect an organic, unique experience with user-friendly solutions, and FAQs are clearly not one of them. So how will Natural Language Processing and Machine Learning help you attain this experience?
Chatbots and Virtual Assistants are the way to go in these cases. If you want to replace your FAQ and upkeep a 24/7 support, chatbots will be the answer for your askings. Utilizing Natural Language Processing, chatbots can be vastly improved. Chatbots have to be trained for a certain behavior, so some preparation is needed before deploying them. Pre-trained Natural Language Processing models for chatbots are available (such as ULMFiT and BERT), however, with customized models, better user experience can be achieved.
Do you want more than a simple question-answering chatbot? That’s what Robotized Virtual Assistants have been developed for. Virtual Assistants can offer extra services other than simple answering. In 2016 Mastercard created its own Virtual Assistant, which was able to carry out basic tasks for the user such as creating reports based on your financial behavior.
As mentioned before, Virtual Assistants are a good choice if you want further services in your business. Personalized Virtual Assistants are becoming more and more regular for everyone. Thus, tech giants are creating theirs. Apple’s Siri is one of the most known speech controlled assistant. Microsoft has Cortana and Amazon has Alexa etc..
Every tech giant is developing its own implementation and interpretation of a Virtual Assistant. However, the technology used is almost the same. Some might ask: how can they use the same technology?
The majority of the Virtual Assistant features are based on the same technology, which utilizes Natural Language Processing applications. For example, Natural Language Processing is used for interpreting the users’ instruction as well as synthesizing the assistants’ speech from written text.
When the user speaks to the assistant, it knows what kind of instruction is being told based on the learned speech patterns. After translating the speech into text, it will execute the instructions told by the user.
A similar operation is happening when the Virtual Assistant tells the user what to do. To produce human-like sound, the Natural Language Processing model learns how the intonation sounds for different words. It also tries to distinguish how questions differ from imperative sentences.
Virtual Assistants are very complex services, but it grants invaluable advantages when it comes to dealing with customers.
Summarizing a text is a challenging problem even for a human. However, Natural Language Processing can help us make the process faster. The main challenge is to create a text that is still understandable for people while retaining the information required.
It is still an emerging technology as a lot of written content, both online and offline, are awaiting processing. Also, creating a summary from all these texts is a time-consuming task. These summaries are required to have a good training set for our Machine Learning model. But how does a text summarization model work?
Text summarization Machine Learning models try to extract useful information from large documents. During the information extraction, Natural Language Process methods are used. There are a lot of ways to summarize large chunks of texts.
For example, unnecessary conjunctions are removed and the rest of the text is joined. Unfortunately, the resulting sentences do not always make sense. Another method is to transform the shortened text in a way to complete the words with proper pre- or affixes. This way the information is preserved, while the text is still readable for a human.
With proper summaries, reading long newspaper news will be easier, while you will still get the same information. Also, books about complex scientific topics, are going to be shortened, so it will be easier to understand what are they about.
Every application of Natural Language Processing is based on novel technologies. If you want to be part of the future, especially in these areas, you better start learning about Natural Language Processing.
Do you wish to get rid of unwanted emails by using sentiment analysis? Do you want to boost the value of your business by implementing a chatbot? Is your goal to translate your services into numerous languages? Natural Language Processing is the best technology to get familiar with.