NLP Text Preprocessing Techniques In Python For Sentiment Analysis
What is text preprocessing in NLP
Natural Language Process (NLP) is a process to bring together the machines that understand, translate and respond to text or voice data of all types of human language. Data Science is the branch of the Natural Language Process (NLP), more precisely is a branch of Artificial Intelligence or AI.
NLP combined with statistical, machine learning, and deep learning models enables computers to process human language, understand the text and voice data and respond with text and voice data keeping the requesters intent and sentiment. NLP also drives computers to summarize large volumes of data in real-time. It can translate from one language to another and respond to spoken commands. Examples of NLP we might have come across are Customer service Chatbots, GPS-operated Systems, Digital Assistance, Google Assistance, Alexa, Siri, and many more.
Natural Language Process (NLP) deals with the data besides text data, There are different types of data which is, received such as numeric data, voice data, database – face recognition, etc. The data is received from many sources like customer reviews, tweets, newsletters, emails, etc. NLP is the art of extracting information from these data. Once we receive the data we need to first & foremost clean the data, need to get rid of un-useful parts of the data. Like removing unwanted noise, removing punctuations marks, removing split words, typo errors, removing URLs, lower casing, etc.
Tokenization, Stemming, Lemmatization, and removing unwanted spaces between the words are necessary. This cleaning process is called Preprocessing Technique, and once the data is preprocessed a machine learning model can be built.
Text preprocessing NLP in Python
Python is a computer programming language that is used for conducting data analysis, building websites and software, automated tasks. Its designs consist of language constructs, object-oriented, code reading ability in its significant way. It helps programmers to write codes for different types of projects. Python is a general-purpose language that is not programmed for any specific problems, it can be used for different kinds of programs. It supports multiple model programming such as structured, object-oriented, and functional programming.
Python is a scripting language with models, patterns, structure, and is rich in text processing tools. Python is often used in Artificial Intelligence projects and Machine Learning projects. It is one of the most popular and widely used programmed in the industry. It is easy and simple compared to other languages, has a huge collection of libraries, and is flexible.
Data preprocessing is done for building a model in machines, the raw data comes with various forms like emotions, punctuations, text written in numerical or special character forms, typo errors, unwanted spaces between the characters or words, grammatical mistakes, and unwanted noise. Machines do not understand anything else than numbers. We have to deal with these problems by using Python’s libraries, which have various types of tools for different tasks. To begin with, text processing in Python certain libraries simplify this process, and their simple, straightforward structure gives a lot of elasticity. Tasks like stemming, POS, tokenization, lemmatizing can be achieved in this way. Some of the libraries are NLTK stands for natural language toolkit. A library called contractions to tokenize words, we receive a lot of data from HTML tags and URLs, web scraping is done using Beautiful Soup library, and Inflect library is used for converting numbers into words, GenSim, SpaCy, CoreNLP, TextBlob, AllenNLP, polyglot & sci-kit-learn.
Text preprocessing in Python for Sentiment Analysis
Sentiment Analysis is also known as Opinion Mining or Emotion AI. It’s a form of text analytics that uses natural language processing (NLP) and machine learning to identify the emotional tone behind the body of the text, whether the given text contains positive, negative, or neutral emotions. This determines and categorizes opinions about a product, service, or idea.
One of the biggest sources in today’s world of information is Text data, which is unstructured and unorganized that comes from different sources. Emails, blog posts, webchats, social media channels, forums, comments, product reviews, or feedback are a few examples of text analytics. Text data is not as structured as simple data which needs extensive data preprocessing. Implementing rule-based, automatic, or hybrid methods of processing manual data by replacing algorithms is one of the methods. Automated systems learn from data with machine learning techniques while rule-based systems perform sentiment analysis based on predefined rules. Hybrid sentiment analysis has a combination of both approaches.
Benefits of NLP
NLP makes it easier for machines to analyze automatically human language for business, many NLP tools are no-code platforms becoming more accessible than ever, they are helping to process huge text data automatically. The operations are streamlined, the cost of the business is reduced, customer satisfaction, and adding to many more benefits.
Advantages of NLP are:
Ø Conduct large-scale analysis: NLP technology does text analysis through all means of channels like internal systems, email, social media data, online review, etc., data is processed within seconds or minutes, wherein humans would need days or weeks to perform this manually.
Ø Accurate Analysis: With repetitive tasks performed regularly, like reading and analyzing open-ended survey responses, humans tend to make mistakes with the help of NLP advanced tools, they perform much more accurately as per your business needs.
Ø Cost is reduced, and processes are streamlined: NLP tools work 24/7, in real-time to complete the same task you would require a couple of employees working full time, with NLP SaaS tool, staff can be minimized when NLP tools are connected to the system it will analyze customer feedback & you will know the problem with which product or service.
Ø Better Customer Satisfaction: NLP tools allow you to automatically analyze and sort the customer service issues by topic, urgency, sentiment, etc., and route them directly to the appropriate department or employee, in this way all customers are equally treated. We can also understand how the customer is happy in each stage through NLP performing and analyzing a customer satisfaction survey.
Ø Understanding your Market better: NLP plays a vital role in Marketing. NLP works to understand the language of the customer base and gives a better understanding of the market segmentation. We should be equipped to target customers directly and decrease customer churn.
Ø Enable your Employees: Using data analysis to its full potential, human hours saved by their manual performances, the employees can focus on what matters most and can prioritize their responsibility areas & furthermore, once you remove repetitive and tedious tasks, employee’s productivity will rise.
Ø Better Insights: AI-guided NLP tools make it easy for machines to understand and analyze unstructured and unorganized open-ended survey responses and online reviews, for data-driven real-world, and give immediate insights, so there is no more guesswork.
Text preprocessing in NLP, in different sectors
Once the preprocessing in NLP is processed & a Machine learning model is built, these NLP platform models are used in different sectors. We may be already familiar with some of the NLP applications such as autocorrection, chatbots, and translation. In our day-to-day life, we even without noticing use numerous applications of NLP like Automated Speech/Voice Recognition, Language Models, Credit Scoring, Insurance Claims Management, Financial Reporting, Auditing, Fraud Detection, Stock Price Prediction, Recruiting Chatbot, Interview Assessment, Employee Sentiment Analysis, Spam Detection and many more.
Preprocessing of text is very important, and it is the most difficult task in NLP as there are no statistical guidelines available. Once the data is preprocessed and models are built, these models and platforms are a huge help in many real-world businesses. It saves time and money, automates processes, and streamlines the workflow. It makes data-driven real-time decisions, fraud detection, speech recognition, importantly machine translation, and so on. It’s easy to adopt as is hassle-free to put NLP to work. AI is redefining and remodeling the future as it is opening new doors for innovations that are helping human requirements and their growth towards their goal.
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