Introduction:

Text mining is a small branch of Natural Language processing So here we start with the most basic question What is NLP? NLP is field in Computer Science and AI which gives machines the ability to understand human language better and to assist in language-related tasks traditionally computers are very good with tasks related to numbers but with the help of NLP we can easily process the text data

To analyze and get structured information from the data generated by these applications we need some sort of mechanism or methods. The solution is text mining. This large data from different sources can be collected and then can be used in text mining processes which we will be getting ideas about in further sections of the blogs. Hence application of text mining can range from getting meaningful words from sentences to extracting most useful information from a source of big data. So, be ready to deep dive into this field of text mining.

Motivation : There are various reasons due to which one should be learning text mining.

1. As of now most of the total information is present in textual formats.

2. Even after a redundant amount of information is present in text format there has not been much work done to process this data.

3. One can extract a large amount of information by analyzing and identifying patterns in social media as well as other sources which can be done by text mining.

4. Even if the data is in unstructured format text mining can enable us to extract and convert it into useful structured information.

5. Large companies having a large number of customers can analyze sentiments of it’s customers and it can be used for betterment of the company. This can also be used to analyze feedback from the customers.

6. By extracting information from news articles, social media posts companies and enterprises can learn patterns in the market. This study of patterns will lead to better planning of strategies of the company leading to well being of the company.

7. One can predict potential individual or organizational threats by analyzing patterns in the text data. This can be helpful for predicting suspicious activity, security threats and many more.

8. Text documents analysis can also be used by legal experts to summarize large text files generated in the court and can get precise summary from the large text documents.

9. Apart from the above-mentioned list of applications where text mining can be used there are other areas where it can be implemented like, healthcare, information retrieval and many more.

In the further section of the blog we will be discussing some of the key concepts about the field mentioned i.e text mining.

1. Tokenization : Whole documents are divided into small chunks. Like documents can be chunked to smaller sentences and then sentences can be chunked to small words if needed.

2. Classification : Entities i,e. objects are assigned to particular classes to which they belong.

3. Clustering : Objects having similar properties are grouped together in the single group. Objects in the same cluster have more common properties than objects in the other clusters.

4. Corpus : This is a collection of written or spoken text stored in a well defined manner. Major thing about corpus is that it is stored in machine readable form and hence it can be used for further analysis.

5. Named entity recognition : Contents in the unstructured data are identified and then they are assigned to a particular category. For example, from a certain text we can assign its content to a Noun, Verb, or Adjective. Just as mentioned we can assign the names in any associated field to the contents of text.

6. Sentiment analysis: This is used in text analysis to analyze the subjective opinions of a particular individual. Reviews about certain products can be categorized as positive or negative if sentiment analysis is done in a better way.

7. Stemming : This is a process where we will be replacing a certain word with the root word associated with it. For example words like running, ran, runner can all be converted to root word run. It is sometimes referred to as Lemmatization too.

8. TF-IDF : It is made up of TF i.e. term frequency and IDF i.e. inverse document frequency. TF means frequency of term in the document. It is calculated by using total occurrences of terms in the documents divided by total number of terms in the document. IDF is used to find the importance of terms in all the sets of documents. It is calculated as a log of the total number of documents divided by the number of documents containing the term specified. TF-IDF is calculated as a multiplication of these both the terms.

9. Parts of speech tagging : Here each of the words in the whole text documents are given part of speech that word represents.

Text mining tasks include text categorization, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, Relation extraction, text generation, text annotation, and Named entity-relation modeling (i.e., learning relations between named entities).

1. Text Categorization: Text categorization is a text mining technique that uses natural language processing (NLP) to assign categories to unstructured data. It is responsible for differentiating the text into predefined categories based on their content. It gathers all the text documents and analyzes them to classify the topics or categories.

2. Document Summarization: Summarization is a process of auto-generating a condensed version of useful text data that contains the main points that are useful to end users in the decision-making process. It allows for generating a summary of text data from the original document with conciseness and context.

3. Sentiment Analysis: Sentiment analysis is the process of natural language processing(NLP) concerned with identifying and extracting the sentiments (i.e. positive, negative, neutral) of text data to track the customers' reviews over time.

4. Named Entity Recognization (NER): Named Entity Recognization is the process of identifying word or phrase spans in unstructured text (the entity) and classifying them as belonging to a particular class (the entity type)[6]

5. Concept / entity extraction: It is a specific task within text mining that focuses on identifying and extracting entities (e.g. names of people, organization, locations, dates, etc)

6. Relation extraction (RE): Relation Extraction is the process of identifying relationships implied in the text between a pair of entities.[6]

7. Production of granular taxonomies : Producing granular taxonomies is a process of creating hierarchical or structured classifications that categorize items, concepts, or entities into specific and detailed subcategories. These taxonomies are valuable in various domains, such as e-commerce, information organization, content management, and knowledge representation.

8. Text Generation: Text generation is the process of automatically generating human-like text. It can be used in various applications, including chatbots, language translation, and content creation.

9. Text Annotation: Text annotation is the process of labeling or marking specific elements within the text, such as identifying named entities, sentiment labels, or parts of speech. These annotations are often used to train machine learning models.

Applications of text analytics in different fields

1. Legal AI

   1. Legal Document Classification: Text analytics algorithms are more efficient to categorize and organize the large volume of legal documents, making it easier for legal professionals to access relevant information quickly

   2. e-Discovery and Document Review: text mining can assist in e-discovery by identifying and prioritizing relevant documents during the pre-trial phase of a legal case, thereby reducing time and costs associated with manual document review.

2. HealthCare:

   1. Adverse Event Monitoring: Text Analytics help to monitor and analyze patient reports and medical records to identify side effects related to drugs and medical devices.

   2. Clinical Decision Support: Text Analytics assist healthcare professionals to process medical literature and patient records to extract the relevant information which provides valuable insights.

3. Marketing and Customer Experience:

   1. Sentiment Analysis and Customer Experience: By employing Text analytics companies can gain insights into how customers perceive their brand and products through various channels like customer feedback, online reviews and social media comments.

   2. Market Trend Analysis: Text analytics can be used to uncover emerging trends and customer preferences by analyzing textual data like e-commerce review, social media posts

4. Finance:

   1. News Sentiment Analysis for Trading: Text analytics can be used to assess the market using financial news and social media analysis, and based on those insights, a decision on a certain stock can be made.

   2. Fraud Detection In Banking: Text analytics is a useful tool for detecting potential fraudulent activities by analyzing textual data from customer communications and transactions

5. Education:

   1. Automated Grading and Feedback: Written assignments can be analyzed and utilized using text analytics to give automated grading and feedback

   2. Student Feedback Analysis: Student feedback can be utilized using text analytics to summarizing and categorizing student feedback

6. Human Resource:

   1. Resume Screening: Text analytics can automate the process of screening job applications by analyzing resumes and identifying candidates with the desired qualifications and experience.

   2. Employee Sentiment Analysis: Text analytics can be used to analyze employee feedback, surveys, and communication to assess employee satisfaction and identify potential issues within the organization.

Reference:

1. IBM text mining blog

2. https://monkeylearn.com/text-mining/

3. https://libguides.ucmerced.edu/text-mining/terms

4. https://uark.libguides.com/c.php?g=949098&p=8299537

5. https://aylien.com/blog/10-common-nlp-terms-explained-for-the-text-analysis-novice

6. https://discover.3ds.com/natural-language-processing-text-analytics-glossary

7. https://en.wikipedia.org/wiki/Text_mining

8. https://chat.openai.com/

9. https://www.polestarllp.com/blog/understanding-the-techniques-and-applications-of-text-analytics

10. https://www.upgrad.com/blog/what-is-text-mining-techniques-and-applications/

11. https://www.lexalytics.com/blog/5-industries-taking-advantage-text-analytics-2/

Team members:

1.https://www.linkedin.com/in/radhakrushna-mahadik-704365205/

2.https://www.linkedin.com/in/aishwarya-kotkar-42476b229/