TEXT ANALYSIS

 WHAT IS TEXT ANALYSIS?

               Text analysis (TA) is a machine learning technique used to automatically extract valuable insights from unstructured text data. Companies use text analysis tools to quickly digest online data and documents, and transform them into actionable insights. You can use text analysis to extract specific information, like keywords, names, or company information from thousands of emails, or categorize survey responses by sentiment and topic.

THE TEXT ANALYSIS vs. TEXT MINING vs. TEXT ANALYTICS:

Firstly, let's dispel the myth that text mining and text analysis are two different processes. The terms are often used interchangeably to explain the same process of obtaining data through statistical pattern learning. To avoid any confusion here, let's stick to text analysis. So, text analytics vs. text analysis: what's the difference?

Text analysis delivers qualitative results and text analytics delivers quantitative results. If a machine performs text analysis, it identifies important information within the text itself, but if it performs text analytics, it reveals patterns across thousands of texts, resulting in graphs, reports, tables etc.

Basically, the challenge in text analysis is decoding the ambiguity of human language, while in text analytics it's detecting patterns and trends from the numerical results.

WHY IS TEXT ANALYSIS IMPORTANT? 

When you put machines to work on organizing and analyzing your text data, the insights and benefits are huge. Some of the advantages of text analysis:

Text Analysis Is Scalable

Text analysis tools allow businesses to structure vast quantities of information, like emails, chats, social media, support tickets, documents, and so on, in seconds rather than days, so you can redirect extra resources to more important business tasks.

Analyze Text in Real-time

Businesses are inundated with information and customer comments can appear anywhere on the web these days, but it can be difficult to keep an eye on it all. Text analysis is a game-changer when it comes to detecting urgent matters, wherever they may appear, 24/7 and in real time. By training text analysis models to detect expressions and sentiments that imply negativity or urgency, businesses can automatically flag tweets, reviews, videos, tickets, and the like, and take action sooner rather than later.

AI Text Analysis Delivers Consistent Criteria

Humans make errors. Fact. And the more tedious and time-consuming a task is, the more errors they make. By training text analysis models to your needs and criteria, algorithms are able to analyze, understand, and sort through data much more accurately than humans ever could.

TEXT ANALYSIS METHODS & TECHNIQUES:

There are basic and more advanced text analysis techniques, each used for different purposes.

Text Classification:

Text classification is the process of assigning predefined tags or categories to unstructured text. It's considered one of the most useful natural language processing techniques because it's so versatile and can organize, structure, and categorize pretty much any form of text to deliver meaningful data and solve problems. Natural language processing (NLP) is a machine learning technique that allows computers to break down and understand text much as a human would.

Text Extraction:

Text extraction is another widely used text analysis technique that extracts pieces of data that already exist within any given text. You can extract things like keywords, prices, company names, and product specifications from news reports, product reviews, and more. You can automatically populate spreadsheets with this data or perform extraction in concert with other text analysis techniques to categorize and extract data at the same time.

Word Frequency:

Word frequency is a text analysis technique that measures the most frequently occurring words or concepts in a given text using the numerical statistic TF-IDF (term frequency-inverse document frequency).

Clustering:

Text clusters are able to understand and group vast quantities of unstructured data. Although less accurate than classification algorithms, clustering algorithms are faster to implement, because you don't need to tag examples to train models. That means these smart algorithms mine information and make predictions without the use of training data, otherwise known as unsupervised machine learning. Google is a great example of how clustering works. When you search for a term on Google, have you ever wondered how it takes just seconds to pull up relevant results? Google's algorithm breaks down unstructured data from web pages and groups pages into clusters around a set of similar words or n-grams (all possible combinations of adjacent words or letters in a text). So, the pages from the cluster that contain a higher count of words or n-grams relevant to the search query will appear first within the results.