In the times of everything being digital, the official letters have taken the face of E-mails. Businesses receive all sorts of text data but analyzing so much text manually takes a serious amount of time. Credits to advanced tools like text analysis with Python that help transform this unstructured data into meaningful insights, quickly and at scale.
Let us explore more about text analysis in python in this article.
What is Text Analysis?
Text analysis, also known as text mining, is the method of automatically categorizing and extracting meaningful information from unstructured text. It further involves identifying and interpreting trends and patterns to achieve relevant insights from data in a matter of seconds.
On the other hand, is Text analytics, that uses data visualization tools to convert insights into measurable data while text analysis obtains qualitative insights from unstructured text.
How Can Text Analysis Help Enterprises?
Businesses use Text Analysis to set the platform for a data-driven approach towards managing content. Effective text analysis allows the business a completely new set of opportunities for practices like decision making, product development, marketing optimization, business intelligence, etc.
In a business context, analyzing texts to capture data from them allows them in content management; semantic search; content recommendation, and regulatory compliance.
When turned into data, textual sources can be further used for obtaining valuable information, finding patterns, automatically managing, using, and reusing content, searching beyond keywords, and more. Utilizing Text Analysis is amongst the first steps in many data-driven approaches. The process extracts machine-readable facts from huge amounts of texts and lets these facts be further entered automatically into a database or a spreadsheet. The made database or the spreadsheet are further used to analyze the data for trends, to give out a natural language summary, or even used for indexing reasons in Information Retrieval applications.
What is Python?
Python is the most popular programming language presently, as it is a highly intuitive language when compared to other languages such as Java in the field of scientific computing. It is more concise, hence takes lesser time and effort to carry out tasks. The syntax and code readability make Python efficient, easy to process, and fairly easy to learn. All these privileges make Python a perfect option to build a machine learning model for text analysis.
Python offers inbuilt functions for creating, writing, and reading files. Two types of files can be treated in python, normal text files; where Each line of text is ended with a special character called EOL (End of Line), which is the new line character (‘\n’) in python by default, and binary files which are written in binary language,0s, and 1s. There is no exterminator in binary files for a line and the data is stored after translating it into machine-understandable binary language.
Now that we have enough knowledge about text analysis and python, let us see what text analysis in Python is.
Text analysis in python
Text Analysis Operations in Python use NLTK, Natural Language Toolkit. This is a powerful Python package that offers a set of diverse natural language algorithms. NLTK comprises the most common algorithms such as tokenizing, part-of-speech tagging, stemming, sentiment analysis, topic segmentation, and named entity recognition.
From analyzing tweets on Twitter and finding trending topics to Amazon understanding user feedback on the specific product to discovering opinions about movies on BookMyShow and YouTube, Text analysis in Python helps all the big giants of industries in some of the other aspects.
Text Analysis Operations using NLTK
The powerful Python package, NLTK, offers a set of distinct natural language algorithms. It is free, open-source, easy to use, large community, and well documented and helps the computer to analyze, preprocess, and understand the written text.
A token is a single unit that is the building block for a sentence or paragraph. Tokenization is the first step in text analytics. Is the process of breaking down a text paragraph into tinier chunks such as words or sentences is called Tokenization.
Word tokenizer fragments text paragraph into words.
Sentence tokenizer fragments text paragraph into sentences.
Stopwords are considered as noise in the text. Text may contain stop words like is, am, are, this, a, an, the, etc. In NLTK for removing stopwords, a list needs to be created to filter out a personalized list of tokens from these words.
Lexicon normalization contemplates another type of noise in the text that reduces derivationally related forms of a word to a common root word.
Stemming is a process of linguistic normalization. It reduces words to their root word or chops off the derivational pins.
Lemmatization diminishes words to their base word, which is linguistically correct lemmas. It converts root words with the use of vocabulary and morphological analysis.
The prime target of Part-of-Speech (POS) tagging is to detect the grammatical group of a given word whether it is a noun, pronoun, adjective, verb, adverbs, etc. POS Tagging looks for connections within the sentence and assigns a subsequent tag to the word.
To have sorted textual data, the text analysis in python comes as a great advantage to every small and big business.