<p>As individuals get more familiar with technology, all institutions are progressively migrating to electronic forms. Massive amounts of data are now being sent via the internet in the form of digital libraries, archives, and other textual information sources such as blogs, social media networks, and e-mails. Although it may appear to be more practical, organizing and dictating such a large quantity of data can be a time-consuming process. Even data mining technologies are incapable of managing unstructured textual data since it necessitates the investment of time and effort to extract information.</p><p>Natural Language Processing (NLP) is one of the most advanced machine learning technologies that is growing in every sector. Text mining is a subset of NLP that provides tools and strategies for delving into unstructured data to discover important patterns and insights. They are necessary for textual data analysis. This article is an introduction to text mining, in which we will learn about its processes, methods, and applications.</p><h3 id="what-is-text-mining">What is Text Mining?</h3><p>Text mining is the technique of extracting important information from standard language text data. This is the information that we produce through text messages, papers, emails, and files written in common language text.</p><p>Text mining is generally used to extract useful insights or patterns from large amounts of data. Text mining is a collaborative topic that incorporates information retrieval, data mining, machine learning, statistics, and computational linguistics techniques. This is concerned with natural language text saved in semi-structured or unstructured forms.</p><p>Text mining seeks facts, connections, and confirmation from large amounts of unstructured textual data. This collected data is then transformed into a structured format that can be examined or shown immediately using HTML tables, mind maps, charts, and so on. Text mining uses a variety of techniques to process the text for this purpose.</p><h3 id="the-process-of-text-mining">The Process of Text Mining</h3><p>The process of Text Mining is tedious and includes a lot of steps. We have simplified and presented the process below:</p><h3 id="text-preprocessing">Text Preprocessing:</h3><p>The first process under text mining is text preprocessing. It is used on a huge number of documents that contain unstructured and semi-structured data. It converts a raw text file into a well-described sequence of linguistically relevant units.</p><p>The following steps are performed under text preprocessing:</p><blockquote>Text Cleanup performs a variety of functions, such as eliminating advertising from web pages and cutting out tables and figures, among others.</blockquote><blockquote>Tokenization is the process of converting sentences into words by removing spaces, commas, and other punctuation marks.</blockquote><blockquote>Filtering removes words with no important content information, such as articles, conjunctions, prepositions, and so on. Even terms that are often repeated are deleted.</blockquote><blockquote>Stemming is the act of converting words to their stems, or standardized forms, by creating fundamental forms of words to recognize them by their root word forms.</blockquote><blockquote>Lemmatization reorganizes the word to the correct linguistic root, which is the verb's base form. Throughout the procedure, the initial step is to comprehend the context, followed by determining the POS of a word in a phrase and finally identifying the 'lemma.'</blockquote><blockquote>Linguistic processing employs Part-of-Speech (POS), Word Sense Disambiguation (WSD), and Semantic structure.</blockquote><h3 id="text-transformation">Text Transformation:</h3><p>Text transformation generates features after the feature selection procedure. Feature generation reflects texts based on the words they include and the occurrences of those words, where the order of the words is unimportant. It makes use of bag-of-words or vector space models.</p><p>In this context, feature selection refers to the process of selecting the subset of relevant characteristics that will be utilized in the creation of a model. It reduces dimensionality by removing redundant and unneeded elements.</p><h3 id="text-mining">Text Mining:</h3><p>After the transformation, the text is mined through several techniques of mining like classification, clustering, summarization, etc. </p><h2 id="methods-used-in-text-mining">Methods Used in Text Mining:</h2><p>Numerous approaches are being developed to tackle text mining challenges; they are essentially appropriate information retrieval based on user requirements. Some typical approaches based on information retrieval techniques are as follows:</p><h3 id="term-based-method">Term Based Method:</h3><p>The term-based technique inspects the document based on terms and benefits from productive computing performance while capturing the theories for term weighting.</p><h3 id="phrase-based-method">Phrase-Based Method:</h3><p>Phrases are more ambiguous and contain more sorts of semantic information. Documents are expected based on phrases in this technique since they are less dubious and more convenient than individual terms. Some of the factors that limit performance are as follows:</p><blockquote>As a result of the secondary analytical characteristics of words</blockquote><blockquote>A rare event</blockquote><blockquote>Extensive duplication and a lot of noise</blockquote><h3 id="concept-based-method">Concept-Based Method:</h3><p>This method concludes terms at the sentence and document levels; such text mining techniques are based on the analytic examination of words and phrases. An analytical analysis is used in this case to assess the word significance in the absence of a document.</p><h3 id="pattern-taxonomy-method">Pattern Taxonomy Method:</h3><p>Documents are evaluated using this approach based on patterns, which are generated in a taxonomy by applying a relation. Data mining techniques such as association rule, frequent itemset mining, sequential and closed pattern mining may be used to identify patterns.</p><h2 id="applications-of-text-mining">Applications of Text Mining:</h2><p>The following are some real-life applications of Text Mining:</p><h3 id="customer-service">Customer Service:</h3><p>There are several options for a consumer to provide feedback, including chatbots, customer surveys, online reviews, support requests, and social network accounts. Combining feedback with text analytics technologies can result in rapid improvements in customer happiness and experience.</p><h3 id="risk-management">Risk Management: </h3><p>Text mining can provide information about industry trends and financial markets in risk management by controlling sentiment data and extracting information from analyst reports and whitepapers.</p><h3 id="maintenance">Maintenance: </h3><p>Text mining has the potential to automate decision-making processes by detecting patterns that correspond with issues, as well as preventative and reactive maintenance operations.</p><h3 id="healthcare">Healthcare:</h3><p> In medical research, a manual inquiry is expensive and time-consuming, but text mining provides an automated alternative for extracting useful information from medical literature.</p><h3 id="spam-filtering">Spam Filtering: </h3><p>Text mining provides a way for filtering and removing such emails from inboxes, improving user experience, and lowering the danger of cyber threats.</p><h2 id="endnote">EndNote:</h2><p>As technology progresses and changes at a rapid pace, data quantities, much of which is unstructured, continue to grow. This new deluge of big data necessitates that most businesses combine structured and unstructured data to provide extensive visibility and insights into their company and operations.</p><p>Text mining is a multidisciplinary topic that combines computational linguistics and natural language processing for extracting non-trivial information from unstructured textual input using text mining algorithms.  This article has taught us about text mining, its procedures, methodologies, and real-world applications.</p>

Bhumika Dutta

A Note on Text Mining

<p>Chatbots are a game-changing invention in the realm of automation and machine learning.  They assist not only business owners and e-commerce giants but also the employees and customers in various ways. As technology advances, the requirement for natural language processing (NLP) to increase chatbot functionality grows. Before we dive into the technicalities and importance of NLP for chatbots, let?s discuss the fundamentals of NLP and AI chatbots. </p><h3 id="what-is-nlp">What is NLP?</h3><p>NLP or Natural Language Processing is a type of artificial intelligence technology that helps chatbots to communicate with humans through speech or text. Many a time, people carrying out conversations with bots wonder to themselves - ?It doesn?t understand precisely what I am saying?. This is where NLP can actually help. </p><p>NLP, based on deep learning is an artificial intelligence (AI) tool that aids a chatbot in analyzing and comprehending natural human language. It evaluates the intention of user input and then generates responses depending on a conceptual framework in the same way that a human would.</p><p>The basic difference between traditional AI chatbots and NLP-based chatbots is that rule-based AI Chatbots only provide information on the basis of keywords. Whereas, NLP-based chatbots try to understand the intent behind the human user?s query or request and take action based on that. </p><h3 id="ai-chatbots">AI Chatbots</h3><p>AI Chatbots or Artificial Intelligence chatbots are text or voice-based software applications intended to provide human users with service and information. Through simulation of the original scenario, they offer a perfect alternative to traditional human-to-human communication.</p><p>Chatbots are nothing but computer programs that you can communicate with via messaging apps, chat windows, or voice calling applications. These integrated virtual assistants respond to customers' questions in a cost-effective, prompt, and systematic manner.</p><p>The working of AI chatbots is simple and effective. They work in two steps, mainly. The first is to identify the meaning of the question asked by the human user. They collect data and gather information about the question asked. After that, they give answers based on either previously recorded queries or a relatively custom answer based on what the user asked. This disparity depends entirely on the smartness or intelligence of the chatbots.</p><h3 id="challenges-with-ai-chatbots">Challenges with AI Chatbots</h3><p>AI chatbots, for all the glitz and glamour of artificial intelligence technology, are not without their own limitations. Some of the various challenges we can face with AI chatbots are listed below.</p><p><strong>A.</strong> Humans can improvise and try to come up with an answer to a unique problem on the spot, while AI has trouble doing so. AI chatbots can fall apart if faced with a completely fresh and unforeseen problem. It can adopt one of the two ways - either the AI will have to delegate the problem to a human. Or it will try to solve it, but because of a lack of moral judgement, it will most definitely fail.</p><p><strong>B. </strong>To function, AI chatbots require a significant amount of time and effort. That is, it takes time for everything to be fully operational and produce tangible benefits. It can take a long time to set up, implement, and learn something new. In terms of their knowledge base and the way they are supposed to engage with consumers, bots require ongoing supervision, maintenance, and optimization. To break it down in simple words, you must provide new and relevant data (content) to the bot regularly in order for it to respond to client questions effectively.</p><p><strong>C. </strong>Another most crucial disadvantage with AI chatbots is the enormous amount of data they may collect in the process of delivering assistance. This may put the customers or users at serious risk of identity theft. AI chatbots may collect addresses or geolocation of users. They can also collect and store information about your payment or savings account i.e. banking details. While using AI chatbots, you are often required to fill in your full legal names and other information that can be used to steal a person's identity.</p><h3 id="why-does-your-chatbot-need-nlp">Why Does Your Chatbot Need NLP?</h3><p>Chatbots are designed for a variety of reasons, including FAQs, customer support, virtual assistance, and much more. Almost always, chatbots without natural language processing rely heavily on pre-programmed static data. They lack empathy and human-like judgement. Therefore, they are poorly equipped to deal with human languages, which vary in emotions, intent, and feelings to communicate each individual enquiry. NLP can distinguish between the many types of requests made by humans, significantly improving the customer experience.</p><p><strong>A. </strong>Natural language processing (NLP) aids AI in determining the best answer by providing meaning and context to text-based user inputs. AI that relies on language inputs is largely useless without NLP. For example, NLP enables technology like Amazon's Alexa or Apple?s Siri to understand what you are saying and respond appropriately. Without Natural Language Processing, chatbots would be unable to give much assistance.</p><p><strong>B. </strong>Chatbots that use natural language processing (NLP) are able to comprehend language principles, linguistic structure, and spoken syllables. As a result, it enables you to interpret a large volume of unstructured data and text.</p><p><strong>C. </strong>NLP enables chatbots to grasp and decode dialect and acquire abbreviations in the same way that humans do, as well as comprehend different emotions through sentiment analysis.</p><p><strong>D. </strong>Costing is a critical component of every business's growth and profitability. On this front, NLP is something that you must deploy in order to manage your finances. NLP-based chatbots can help reduce expenses related to manual labour and other resources engaged in repetitive operations. They can also help with other costs associated with customer retention. This will help in increasing efficiency and optimising the overall work performance.</p><p><strong>E. </strong>When you use chatbots, you will see an improvement in customer retention. This is because using NLP based chatbots decreases the time and cost of gaining a new client by enhancing existing customer loyalty. Chatbots give clients the attention and time they desire, making them feel valued and appreciated. This is the case especially for millennials, who want instant responses and solutions for their inquiries.</p><h3 id="how-can-natural-language-processing-nlp-overcome-natural-communication-barriers">How Can Natural Language Processing (NLP) Overcome Natural Communication Barriers?</h3><p>Advanced Natural Language Processing (NLP) is overcoming natural communication barriers each day. For example, chatbots with NLP skills can detect and analyse spelling and grammatical faults. It can then offer a number of suggestions or corrections for the human user to choose from. NLP allows the chatbot to interpret your actual meanings despite the inaccuracies. </p><p>This intent-driven feature will be able to make connections between customers and businesses. This will ensure that your chatbot is someone customers will want to talk to when contacting your business. </p><p>Worldwide affairs, political changes, government policy statements, and the fundamental economic environment in an area all influence financial market movements. To evaluate the climate, NLP-based systems may read news, press releases, and other financial reports. This capability improves the efficiency of computerized financial advisors.</p><h3 id="benefits-of-nlp-based-chatbots">Benefits of NLP Based Chatbots</h3><p>When it comes to Natural Language Processing, programmers can train the chatbot on a variety of interactions and conversations. The developer can also teach different examples of content that the chatbot might encounter while interacting with human users or customers. This tends to provide it with a much broader base from which to analyze and interpret questions more efficiently.</p><p>With personalization at the forefront, companies are striving to make an effort to teach their chatbots about the many default responses and how they might make customers' life easier. As a result, the chatbot will be able to streamline more personalized, unique responses. The chatbots will be able to interpret and answer new queries or commands and improve the customer's experience based on their demands. All of these credits go to natural language processing (NLP) technology.</p><p>Customers in the newer generations would rather text a brand or business than call and chat with a human customer service provider. In the age of instant gratification and a chance to interact with emerging technology, chatbots are what millennials prefer. Thus, if you want to cater to this niche market, you'll need to design a chatbot with NLP.</p><h3 id="future-thoughts">Future Thoughts</h3><p>Experts in business, market intelligence, and research and consultancy businesses are optimistic about the future of NLP-based chatbots. Very soon, almost all enterprises across every industry, as well as governments, will begin deploying AI solutions. In the creation of intelligent real-world applications, natural language processing (NLP) will be very critical. Businesses must innovate and make use of developing technology to improve customer relationships, production performance, and cost structures.</p><h3 id="summing-up">Summing Up</h3><p>In conclusion, we must remember that NLP cannot fully guarantee that a chatbot will answer efficiently to all messages. Nonetheless, natural language processing (NLP) technology is important enough to decide a chatbot's sustainability. </p><p>Chatbots that use natural language processing (NLP) can help you improve your business operations and elevate your client experience while also enhancing overall growth and profitability. It gives you technological benefits to sustain in the market by saving you time, effort, and money, which leads to higher user satisfaction and involvement in your company.</p>

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