Feel like you’re floundering to stay afloat in a sea of data that grows larger every day? Unable to extract anything useful? You’re not alone. Businesses everywhere are grappling with the need to find a way to mine vast amounts of data for the riches within.
This is where natural language processing (NLP) shines. You’ve probably been hearing a lot about NLP these days, but it’s more than just a trendy buzzword. According to a global research report commissioned by IBM, nearly half of companies today use NLP-powered applications, and another 25% of businesses plan to implement NLP technologies in the coming year.
Why is NLP an ideal solution for accessing insights from data? By leveraging the power of Artificial Intelligence (AI) and machine learning, NLP lets you dive deep into your data—both first-party and third-party—and emerge with the relevant, industry-specific information your business needs to stay competitive. Using NLP techniques, you can analyze massive amounts of text data on an unprecedented scale. And you can automate this analysis so it runs in real-time, with little to no manual intervention.
What is Natural Language Processing?
NLP is a branch of AI that combines computational linguistics with computer science. By making use of machine learning and deep learning models, NLP techniques translate human language into language that computers can understand.
Computers “speak” in binary code. This means machine language consists of 0s and 1s. Compare this with the complexities of human language, and it’s easy to see the translation challenges. Before an NLP technique can translate human language into language a computer can understand, it needs to:
- resolve ambiguities in the words
- understand context
- recognize the impact of concepts such as gender and culture
Advances in AI and machine learning have generated a number of NLP techniques capable of tackling these challenges head-on. The following techniques are examples of some of the most popular NLP techniques in use today.
Top Five NLP Techniques for Extracting Meaning from your Data
1. Sentiment Analysis
Sentiment analysis examines data to determine whether customers’ feelings and attitudes are positive, negative, or neutral. Imagine being able to put your finger on the pulse of your market’s sentiment toward your brand. You’d be able to see what people think about your products and it would give you invaluable information across multiple departments, from marketing to product development to customer service.
2. Automatic Text Summarization
Automatic text summarization generates a condensed version of longer text while keeping the meaning of the text intact. It enables you to efficiently distill the salient points of relevant information that’s often buried deep within longer-form content. In the past, such information had to be manually extracted—a process that’s unsustainable when dealing with a large volume of data.
3. Named Entity Recognition
With their ability to obtain meaningful information from the mounds of text within data, NLP techniques are powerful tools for keeping your competitive edge.
Named entity recognition (NER) is a supervised learning technique in which you predefine the kind of information you want to be extracted. To use NER, you begin by training your NER model with a dataset of predefined entity categories—for example, date, person, location, occupation, and organization—to teach the model to identify specific entities in text and place them into appropriate categories. This mimics the way humans read: We automatically identify any named entities in text as our eyes scan the words.
4. Topic Modeling
By combining pattern recognition and machine learning, topic modeling infers topics from within the text being analyzed. Based on the topics identified, it then groups that text with other texts containing similar topic clusters. Unlike NER, topic modeling is an unsupervised NLP technique. Unsupervised techniques are typically quicker and easier to use because you don’t need to train your model first.
5. Lemmatization and Stemming
Lemmatization and stemming are data cleansing techniques that work by grouping words in a similar way:
- Lemmatization groups words based on dictionary definition or context. For example, lemmatization would group sits, sat, and sitting under the common root sit.
- Stemming groups words by cutting off prefixes or suffixes to produce a stem. For example, with stemming the words walking, walked, and walks would be grouped under walk. However, sits, sat, and sitting would not be grouped under sit, because stemming the words does not produce the same root word.
How to Leverage the Power of NLP Techniques in Your Business
NLP is a useful tool because it allows you to extract useful information from your data. Here are ways to leverage its power:
- Data quality: By using text cleansing techniques like lemmatization, you can prepare your data so it produces text that machines can understand more accurately.
- Customer segmentation: NLP techniques can be used to extract key customer information from your raw data. For example, ShareThis uses NLP to create key attribute data—such as brand and lifestyle—that help you to filter down your data into appropriate customer segments.
- Overall user engagement: Without the help of machine learning, working with large datasets would be an impossibility. NLP provides AI-driven tools that help you extract the focused information you need to drive customer engagement.
With their ability to obtain meaningful information from the mounds of text within data, NLP techniques are powerful tools for keeping your competitive edge. ShareThis, for example, uses NLP to extract meaning from the large volume of data it collects daily. By partnering with ShareThis, you can leverage the power of NLP to unearth the actionable insights you need.
