Text processing examines and analyzes natural language textual data. The purpose of this field is to interpret and analyze text in such a way that computers can automatically communicate with humans in an understandable language. Text processing includes processes such as text analysis, information extraction, text meaning interpretation, machine translation, text generation, text classification, and sentiment recognition. This field is used in many fields such as news analysis, analysis of social network posts, automatic processing of legal documents and texts, analysis of images and videos, etc. In general, text processing involves the use of algorithms, models and various techniques to interpret and analyze textual data. The purpose of text processing is to accurately analyze text and use it to perform tasks such as summarization, translation, topic detection, sentiment analysis, fake document detection, etc. This technology is used in various fields such as natural language processing, information retrieval, information analysis, knowledge management, customer service, stock market analysis, automatic response systems, etc.

Text processing in the field of natural language processing

As we mentioned, text processing means processing textual data in a precise and structured way. In this method, textual data is considered as a series of characters or words in the form of text strings, and tools and techniques such as pattern search, parsing, etc. are used to process them.

 

In this method, textual data is considered as natural language, and natural language processing experts use techniques such as grammar analysis, linguistic component recognition, sentiment analysis, topic recognition, etc. to process the data. In general, text processing is more about processing textual data in a detailed and structured way so that natural language processing can understand natural language and understand its concepts.

 

In what industries is word processing used?

In recent years, text processing has become one of the most important fields in computer science due to its various applications. Some of the applications of this technology in different industries are as follows:

 

Information technology industry: In this industry, text processing and natural language processing are used for searching and extracting information from texts, answering text questions, analyzing users' feelings and recognizing the topic of texts.

Financial services industry: In this industry, text processing is used to analyze financial news and events, predict market prices, analyze legal documents, and detect financial fraud.

Health and medical industry: In this industry, text processing is used to analyze medical reports, diagnose diseases and create decision support systems for doctors.

Customer service industry: In this industry, text processing is used to answer customer questions and complaints, analyze customer sentiments, and offer products and services to customers.

Information retrieval industry: In this industry, text processing and natural language processing are used to retrieve information from databases, thematic analysis of scientific articles, machine translation and text summarization.

Another important application of text processing is in the marketing industry. So that digital marketing teams are able to make the most of the potential benefits of this technology. The benefits of word processing index in the marketing industry can be described as follows:

Sentiment analysis: By using text processing and natural language processing, users' sentiments about products and services can be analyzed and their positive and negative opinions can be identified. This information can be used as feedback to improve products and services.

Topic detection: By using natural language processing, it is possible to identify topics of interest and interests of users. By analyzing this information, it is possible to find the best marketing and advertising methods for each specific topic.

Content analysis: You can analyze advertising content and marketing materials and implement marketing campaigns more accurately.

Machine translation: The above technology allows marketing and advertising texts to be translated into different languages. As a result, companies active in international supply are able to operate on a global scale.

Answering customer questions: The mentioned technology allows companies that have customer relations departments to automatically answer customer questions on social networks and different platforms.

Analysis of referrals: One of the interesting applications of this technology is related to the analysis of traffic and referrals to different parts of a website. So that companies can improve the ranking of the site in search engines based on this technique.

In general, text processing and natural language processing help marketers find the best ways to market their products and communicate with customers.

 

An example of text processing in the field of natural language processing

An interesting example of the application of text processing is Google's automatic response system to questions raised by users in the company's search engine. This system answers questions raised by users using text processing and natural language processing methods. For example, if the user asks the question "How is the weather in Tehran?" search, Google looks for keywords such as "weather" and "Tehran" by analyzing the text of the query and provides an answer to the user's query using meteorological data. This system automatically answers users' questions without the need for human intervention and uses text processing and natural language processing capabilities. Of course, text processing can also be used in other systems, the most important of which are the following:

 

Automated Answering Systems: Automated answering systems such as Siri, Alexa, and Cortana use text processing and natural language processing extensively to answer user questions and interpret user commands.

Sentiment analysis: Sentiment analysis systems such as Brandwatch and Hootsuite use text processing and natural language processing to analyze users' opinions and sentiments on social networks and other online sources.

Discourse analysis: Discourse analysis systems such as IBM Watson and RapidMiner use text processing and natural language processing to analyze a large number of texts such as user comments, articles, news, etc.

Text Analysis: Text analysis systems such as Aylien and MonkeyLearn use text processing and natural language processing to analyze text such as blog posts, articles, and emails.

Machine translation: Machine translation systems such as Google Translate and Microsoft Translator use text processing and natural language processing to translate texts and conversations.

In general, text processing and natural language processing are used in various systems such as customer service, information retrieval, data analysis, translation, sentiment analysis, etc.

How to implement text processing with Python?

Python is one of the powerful programming languages ​​that has many possibilities in the field of text processing and natural language processing. Fortunately, various libraries in the field of text processing are available to professionals, the most important of which are the following:

 

NLTK

NLTK stands for Natural Language Toolkit, one of the most famous natural language processing libraries in Python. This library provides various facilities for text processing, grammar analysis, sentiment analysis and vocabulary processing.

 

spaCy

Another natural language processing library is in Python. This library automatically analyzes grammar and how to find vocabulary.

 

TextBlob

is a Python library for text processing that provides features such as sentiment analysis, grammar analysis, and topic analysis.

 

Gensim

It is a Python library in the field of natural language processing, mostly used for text processing and topic analysis.

 

TensorFlow

It is one of the popular and versatile libraries for Python, which is used in the field of building deep neural networks, natural language processing and machine learning in Python. This library can be used for machine translation, discourse analysis and sentiment analysis.

 

To get started with any of these libraries, it is best to first read their documentation and then improve your knowledge by using the available examples. Now, let's explore the process of doing this using the NLTK library. In this example, we take a plain text as input and then count the number of words and sentences in it using NLTK:

 

import nltk

# Download required data for NLTK

 

nltk.download('punkt')

 

# Input text

 

text = "This is a sample sentence. We will use this sentence to count the number of

 

words and sentences in it.

 

# Count the number of words

 

word_count = len(nltk.word_tokenize(text))

 

# Count the number of sentences

 

sentence_count = len(nltk.sent_tokenize(text))

 

# Print the result

 

print("Number of words:", word_count)

 

print("Number of sentences:", sentence_count)

 

The output of the above example will be as follows:

 

Number of words: 20

 

Number of sentences: 2

 

In this example, the natural language processing data is first downloaded using the nltk.download function. Then, the input text is stored as a string in the text variable. Using the nltk.word_tokenize function, the number of words in the text is counted, and using the nltk.sent_tokenize function, the number of sentences is counted. Finally, the results are printed using the print function.

 

To better understand the topic, let us consider a more advanced example. In this example, we take an arbitrary text as input and then parse it using NLTK and extract words that are verbs:

import nltk

 

# Download required data for NLTK

 

nltk.download('punkt')

 

nltk.download('averaged_perceptron_tagger')

 

# Input text

 

text = "John is eating a delicious cake in the kitchen."

 

# Grammatical analysis of the text

 

tokens = nltk.word_tokenize(text)

 

tagged = nltk.pos_tag(tokens)

 

# extraction of verbs

 

verbs = [word for word, tag in tagged if tag.startswith('V')]

 

# Print the result

 

print("Verbs:", verbs)

 

The output of the above example will be as follows:

 

Verbs: ['is', 'eating']

 

In this example, first the data required for natural language processing is received using the nltk.download function. Then, the input text is stored as a string in the text variable. Using the function nltk.word_tokenize, the grammatical analysis of the text is done, and using the function nltk.pos_tag, tagging is done for the words in the text. Then, using a descriptive list of different tags, the verbs in the text are extracted and finally printed using the print function.

 

How to process text using the SpaCy library

As we mentioned, there are various libraries available for text processing, with spaCy and TextBlob being two other popular options that can be used in Python. How to use the above libraries is as follows:

 

import spacy

 

# Loading the English language model

 

nlp = spacy.load('en_core_web_sm')

 

# Input text

 

text = "John is eating a delicious cake in the kitchen."

 

# Grammatical analysis of the text

 

doc = nlp(text)

 

# extraction of verbs

 

verbs = [token.text for token in doc if token.pos_ == “VERB”]

 

# Print the result

 

print("Verbs:", verbs)

 

The output of the above example will be as follows:

 

Verbs: ['eating']

 

In this example, the English language model for spaCy is first loaded using the spacy.load function. Then, the input text is stored as a string in the text variable. Grammatical analysis of the text is done using the nlp function and the verbs in the text are extracted using the pos property and finally printed using the print function.

 

Example with TextBlob

 

from textblob import TextBlob

 

# Input text

 

text = "John is eating a delicious cake in the kitchen."

 

# Sentiment analysis of text

 

blob = TextBlob(text)

 

sentiment = blob.sentiment

 

# Print the result

 

print("Sentiment:", sentiment)

 

The output of the above example will be as follows:

 

Sentiment: Sentiment (polarity=0.6, subjectivity=0.9)

 

In this example, the TextBlob library for text processing is loaded first. Then, the input text is stored as a string in the text variable. Using the TextBlob function, the sentiment analysis of the text is done and the result is returned as a Sentiment object with polarity and subjectivity properties. Finally, the result is printed using the print function.

Which of the spaCy or TextBlob libraries are suitable for text processing?

spaCy and TextBlob are both very powerful libraries for text processing in Python, but each has its own features and limitations, and may be better or worse for different applications. Below are some important features of each library:

 

spaCy: One of the most important features of spaCy is its high speed in text processing and the ability to use multi-core processors. spaCy has the ability to process sentences in different languages ​​and is very suitable for processing large and complex texts. Also, spaCy has the ability to recognize different linguistic components such as nouns, verbs, adjectives, etc., which is very suitable for the grammatical analysis of the text. To better understand this issue, let's refer to an example on how to recognize linguistic components using spaCy.

 

import spacy

 

# Loading the English language model

 

nlp = spacy.load('en_core_web_sm')

 

# Input text

 

text = "John is eating a delicious cake in the kitchen."

 

# Grammatical analysis of the text

 

doc = nlp(text)

 

# Extracting linguistic components

 

for token in doc:

 

 print(token.text, token.pos_, token.dep_)

 

The output of the above example will be as follows:

 

John PROPN nsubj

 

is AUX aux

 

eating VERB ROOT

 

a DET det

 

delicious ADJ amod

 

cake NOUN dobj

 

in ADP prep

 

the DET det

 

kitchen NOUN pobj

 

. PUNCT punct

 

In this example, the English language model for spaCy is first loaded using the spacy.load function. Then, the input text is stored as a string in the text variable. Using the nlp function, the grammatical analysis of the text is performed and a Doc object is returned. Then, using the for loop, various linguistic components such as vocabulary, syntactic tags and their role in the sentence are extracted and printed. In this example, syntax tags from the Universal Dependencies tag set are used.

 

TextBlob: One of the important features of TextBlob is the ability to analyze text sentiment. Using TextBlob, you can automatically analyze comments and texts and estimate their positive, negative and neutral sentiments with relatively high accuracy. Also, TextBlob has features like grammar analysis and word and sentence counting that are useful for text processing. One of the features of the TextBlob indicator is the ability to detect linguistic components. Using TextBlob, you can recognize various linguistic components such as nouns, verbs, adjectives, adverbs, etc. For this, you can use the tags property of the TextBlob object. This feature is a list of tuples, each tuple containing two elements: a word and its linguistic label. Below is an example of detecting language components using TextBlob:

 

from textblob import TextBlob

 

# Input text

 

text = "John is eating a delicious cake in the kitchen."

 

# Create a TextBlob object

 

blob = TextBlob(text)

 

# Recognition of linguistic components

 

tags = blob.tags

 

# Print the result

 

print("Tags:", tags)

 

The output of the above example will be as follows:

 

Tags: [('John', 'NNP'), ('is', 'VBZ'), ('eating', 'VBG'), ('a', 'DT'), ('delicious', 'JJ '), ('cake', 'NN'), ('in', 'IN'), ('the', 'DT'), ('kitchen', 'NN')]

 

In this example, the input text is first stored as a string in the text variable. Then, a TextBlob object is created with the input text using the TextBlob function. Using the tags feature, linguistic components are detected and the result is returned as a list of tuples with words and their linguistic tags. Finally, the result is printed using the print function.

 

last words

 

As you have seen, text processing in Python using various libraries that are available is not a difficult task and requires more practice, creative solutions and experience, but on the other hand, it provides us with significant capabilities that using They are able to perform most of the operations related to the analysis of information and texts automatically.