Extracting Insights From Unstructured Data With Text Analytics

Unstructured data can be an invaluable asset. With text analytics, unstructured information can be turned into structured data which can then be utilized for purposes such as data mining, content optimization and automatic information gathering.

Text analytics is an increasingly useful way for companies to manage customer surveys or support tickets more quickly and more effectively, but how exactly does it work?

1. Text Extraction

Text Analytics begins with text extraction. In this first step, your data is broken up into individual linguistic units called tokens (letters or words like FISHING). Next comes grammar analysis – dependency parsing and constituency tagging can help categorize data, while stemming and lemmatization remove any unnecessary prefixes and suffixes that don’t add any real value.

Once you have your raw data in hand, the next step is extracting insights and creating results for use within your business use case. Text analytics tools are great ways of extracting insights from documents, surveys, social media or customer support software like Zendesk.

Imagine you want to identify trends from open-ended customer feedback on your products or services, using NPS or satisfaction survey responses as data sources and quickly extract data for analysis, which could provide new opportunities to enhance them, meet customers’ wants and needs and even leverage other public/private data sources like compliance forms/contracts and market research. With the right tool in your arsenal, this process becomes faster and easier.

2. Text Classification

Pharma and Insurance industries require enormous data volumes for effective operations; manual searches can eat up precious time and resources by being inefficient, error-prone and time-consuming processes, especially when searching large volumes of documents or texts.

An automated text classification system enables businesses to delegate this task, saving valuable time and resources while still being able to perform real-time data analyses as well as respond quickly and effectively to feedback from clients and stakeholders.

Text classification utilizes machine learning techniques to analyze unstructured data to gain insight into client trends, product performance and service quality. This helps businesses make quicker decisions, enhance business intelligence and increase productivity and cost-efficiency. Text analysis also allows researchers to quickly explore a vast amount of preexisting literature while extracting pertinent sections that pertain to their study quickly; furthermore it helps governmental bodies make faster decisions while fine-tuning search engines and retrieval frameworks as well as creating faster user experiences through faster searches engines and retrieval frameworks.

Text analytics utilizes various techniques to identify keywords and concepts within a document or text, including word frequency and collocation analysis, to reveal hidden structures. For instance, frequent mention of expensive or overpriced in customer reviews could signal your pricing strategy may need an adjustment.

3. Sentiment Analysis

Sentiment analysis uses natural language processing and machine learning techniques to detect the overall emotional tone of customer feedback. This enables teams to better meet customers’ needs through better products and services by identifying trends or uncovering insights in data, such as seeing too much negative sentiment indicating anger or frustration in customer comments.

Sentiment analysis attempts to classify words into buckets such as happy, sad, excited or hopeful. Unfortunately, in reality the reality of people’s expression of opinions varies considerably and may include rhetorical devices like sarcasm irony and implied meaning that can distort results of sentiment analysis.

Sentiment analysis uses several techniques, including Naive Bayes (a family of probabilistic algorithms utilizing Bayes’ theorem to predict categories) and Support Vector Machines (non-probabilistic models that assign new texts based on similarities to existing examples in a multidimensional space). Advanced machine learning techniques, like Deep Learning, may produce more accurate results but require extensive coding efforts and are more complex overall.

4. Named Entity Recognition

Named entity recognition (NER) is a subtask of natural language processing which serves to recognize and categorize names of people, places, things and other entities found within text documents. This may include organizations, persons, locations, medical codes, expressions of times, quantities quantities monetary values and percentages among many other examples.

Named entity recognition (NER) can be accomplished using various approaches. A popular approach utilizes a rule-based system with predefined rules to detect entities within text documents, while pattern matching helps find and classify known structures such as phone numbers or emails addresses. Word embeddings transform words into dense vector representations so machine learning models can more easily process them.

Once an entity is detected, its model assigns it a category according to the rules it was trained on. This could include generic categories like Organization or Person or more specific ones like Healthcare Terms or Programming Languages based on your use case. Label consistency is crucial when training NER models; training data with consistent labels across each entity type.

5. Relation Extraction

Customers provide valuable feedback about products and services they experience through reviews, social media comments, or responses to surveys. Text analytics enables businesses to utilize this insight from customers’ reviews or comments and transform it into quantitative insights that help better understand market dynamics and customer needs.

An effective approach for performing supervised relation extraction is training a stacked binary classifier to determine whether an entity is pertinent. Such classifiers typically utilize features such as context words, part-of-speech tags (i.e. identifiers), dependency paths between entities and data extracted by other NLP modules as data inputs.

Unsupervised relation extraction aims at extracting relationships directly from raw text data without needing predefined lists of relations or labeled training data. Tokenization breaks up text into minute particles called tokens; next comes natural language processing (NLP) followed by part-of-speech tagging that assigns each token with noun, verb, adjective or adverb classifications.