Natural language processing (NLP) combines the power of artificial intelligence and the study of linguistics to advance the interaction between computer and human languages. Although under constant development, this area of study still presents many challenges that begin with natural language understanding, which we humans have been trained to do for years.
We learn to understand languages naturally as we acquire, develop, and use our native language through hundreds of daily interactions with other humans who share the same language, culture, customs, and even visual cues in the form of body expressions and gestures that play an essential role in how we humans communicate.
Computers, on the other hand, lack this kind of input. To address this, NLP research relies on various disciplines like psychology and anthropology to derive meaning from human language and discern the nuances of our intended messages. Many advancements have been made since the first working speech recognizer was created in 1952 by Bell Labs, and we now live in an era where we enjoy the fruits NLP has harvested for the human collective. Voice-controlled devices have changed how we interact with the world, making things easier and faster. For instance, getting new rolls of toilet paper or turning up the volume is just an “Alexa, order my favorite toilet paper” or a “Hey, Siri; turn up the volume” away. But how exactly is this possible?
Training data and text analysis
The key to all this is language training data, and when it comes to machine learning for NLP, we’re talking about large datasets that can contain millions of data points used to build language models. In short, large sets of raw written text are processed, interpreted, and transformed into something that a computer can understand, and, from this, we build language models based on what serves a purpose in real-life applications. This intelligent analysis of language data in written form, also known as text analysis, allows us to recognize the type of content, sentiment, and intent. Text analysis generates insights that involve understanding subtle cues like sarcasm, humor, speech style, and even the ability to recognize bias in text data.
In other words, text analysis is a method used to understand the content of an unstructured text, such as the emotions, themes, and ideas contained within it, to enable a data-driven approach to content management. This process can be done through manual analysis or by using computational methods. One of the most common text analysis methods is sentiment analysis, which is used to identify the emotions within a text. For example, a simple sentiment analysis method might be to count the number of positive and negative words in a text. More sophisticated methods might use machine learning algorithms to identify text patterns indicative of positive or negative sentiment. Another common text analysis method is content analysis, also known as topic modeling, which determines the main themes within a text, and a simple topic modeling method is identifying the most common words in a text.
The primary purpose of text analysis is to create structured data out of the raw text data, and its main challenge is to deal with the ambiguity of human languages. We, as humans, are very good at deriving meaning from the context in real time. On the other hand, a computer will need to acquire this context or background knowledge by other means. This is where data annotations come into play. We can use semantic tags to link references to specific concepts. These tags provide structured metadata that allows for a better search function that, in turn, provides the data we need for further analytics.
There are several types of data annotations used in NLP. The most common include part-of-speech (POS) tagging, named entity recognition (NER), sentiment analysis, intent analysis, and content analysis.
POS tagging, also known as grammatical tagging, is a process that identifies parts of speech by marking words in a text. The most common identifications for words are nouns, verbs, adjectives, adverbs, and prepositions. This process is not always straightforward, however, as some words can represent different parts of speech in different situations. We must rely on grammatical context and semantic analysis to approach this ambiguity in words and determine the specific role a word plays in any given situation.
NER is a process used to identify named entities in text and classify them into predetermined categories such as people, location, organization, product, numeric values, date and time, etc. The models used in NER can identify all this information based on a string and then categorize it accordingly.
Sentiment analysis is an NLP method to identify and categorize statements expressed in text. Most sentiment analysis tools use a set of predefined rules to categorize text as positive, negative, or neutral. Sentiment analysis implementation can be as simple as looking for keywords or phrases indicative of sentiment. Some typical applications for sentiment analysis include identifying customer sentiment in reviews, social media posts, or customer service interactions; analyzing the sentiment of news articles; or identifying the sentiment of tweets. Sentiment analysis can determine the overall feeling of a text or identify specific aspects of emotion. For example, a sentiment analysis tool could be used to identify the sentiment of a review of a new product or to identify the sentiment of tweets about a particular public figure. Several challenges are associated with sentiment analysis, including the subjectivity of language, the need for large training datasets, and the difficulty of identifying sarcasm and irony. However, sentiment analysis is a valuable tool for understanding customer sentiment, identifying trends, and measuring the impact of current events on the population.
Examples of sentiment analysis
Intent analysis is a technique used in NLP to identify the purpose or goal of a particular text that can be done through a variety of methods, including but not limited to:
– Identifying critical keywords and phrases
– Analyzing the structure of the text
– Comparing the text to similar texts
One common use of intent analysis is to automatically classify texts into categories, such as “customer support” or “sales.” This application can be helpful for companies and organizations that receive a large volume of text-based communications. This process is what chatbots use to quickly understand what kind of interaction they’re dealing with. For example, suppose a customer sends a text asking for support with a product. In that case, an intent analysis system can automatically generate a response that includes instructions on how to use the product or troubleshoot common issues. The exchange can be promptly escalated to a human representative when the chatbot is not programmed to provide a satisfactory solution. There are many different ways to perform intent analysis, and the best approach will depend on the available data and the project goals.
Examples of intent analysis
Content analysis, also known as topic modeling, is a method used to identify the overall theme or subject in unstructured text data that can provide quick insights into the type of text we’re dealing with and the general purpose of the data. This analysis can be as fundamental as giving the language in which a text is written and as detailed as analyzing a text on History and providing specific information discerning that the text is focusing on World War II. Another business application for content analysis is to classify websites based on their content for indexing so they can be divided into categories and different industries for business directories.
Examples of content analysis
Use cases for text analysis in the real world
Text analysis is used in many industries to implement a data-driven content management approach, learn about specific industry trends, serve customers better, and even help with product development.
Social media: Identify trends, current events, sentiment toward a company or business based on mentions on social media, and numerous other applications for specific industries.
Health care: Identify patterns in prescriptions and drug use, discover outbreaks based on mentions in social media, etc.
Customer service: identify customers’ sentiment based on interactions with the company, predict needs and demands, provide a personalized customer experience, and funnel queries to the correct department.
Product development: Identify customer needs and product trends based on customer reviews to determine desired features and characteristics in upcoming products.
So, why is text analysis important then?
Businesses in many industries rely on text analysis to extract actionable insights from several different data sources. These insights allow decision-making based on customer interactions via emails, social media, and customer surveys. However, these large datasets are like a diamond in the dirt, and the process can glimmer with complexities when the proper methods are not set in place.
Text analysis quickly provides accurate information from multiple sources. The process can be done manually, but it can also be fully automated to make it more consistent and display actionable data. For example, using text analysis methods allows you to detect negative sentiment on social media platforms immediately, which enables companies to and do damage control, preventing what could otherwise be a public relations crisis.
Text analysis is necessary for NLP to build language models for more complex applications like machine translation or text summarization. However, its applications don’t stop there. It’s also a method that allows businesses to derive insights from unstructured data to help them provide better products and services and, in general, to make better decisions. The rapid digital transformation the world is experiencing is forcing many companies to quickly adapt new strategies to ensure that they stay in business and thrive by making data-driven decisions where text analytics plays a significant role. Is your company ready to face new challenges?
Here at BAVL, we don’t only have all the tools available to collect and generate language datasets on any language and subject. Our platform is also equipped with text data annotation functions that allow us to perform text classification tasks like sentiment analysis, intent analysis, and content analysis on any type of text in any language. Thanks to our more than 20,000 qualified artificial intelligence (AI)–equipped workers worldwide, we are ready to take on your text analysis needs today.
Visit http://bavl.ai today to schedule a consultation and discover how to maximize the potential of your training data for your next AI and NLP project or to bring data-driven insights into your business for better decision-making.