Q 637. Text Analytics help convert unusable text data into structured and usable format for further analysis. Which challenges have been addressed in the field of Text Analytics? Which set of challenges are being worked upon?

 

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Several problems in the field of Text Analytics have been resolved to transform unreadable text data into a structured or usable format for further analysis. These include, Processing text in multiple languages, Noisy, completeness & consistency issues with text data which may also contain grammar, abbreviations or spelling errors too. In addition to that, during integration with other types of information such as audio or video files as well as images it is frequently problematic. The system is being improved so that it can understand context in the text data including sarcasm and irony. There are ongoing efforts to develop techniques that will help analyze different language datasets and be able to handle them as well as the challenge of accessing large amounts of information quickly and efficiently. Continous efforts on improving the capabilities in  addressing challenges associated with unstructured text data analysis are being made.

Text analytics is the process of cracking the text by transforming piles of unstructured text data into meaningful and actionable insights, thus unlocking hidden patterns and trends

 

This process consists of

• Data collection
• Pre-processing
• Text Transformation
• Feature Engineering
• Structured Data Representation
• Further Analysis
• Insights and Decisions

Text analytics generally unlocks insights from vast amounts of unstructured text & enables better understanding of customer sentiment, opinions and behavior.

 

The general challenges in Text analytics include:-

• Unstructured data.
• Multiple languages and dialects.
• Big data overload.
• High dimensionality.
• Semantics and context
• Ethical considerations
•  Requiring robust hardware and software infrastructure (computerized)

The general challenges being worked upon:-

 

• Bias and accuracy: Reducing bias in text analytics models is important and requires different training data, bias analysis, and honest AI development.
• Interpretability and reliability: Building trust in the textual analysis model requires improved interpretability that allows users to understand how highly the model reaches a verdict and identifies the suspect.
• Data Privacy and Security: In the context of text analysis, it is important to protect user privacy and ensure data security. Research into technology-based anonymity and privacy protection algorithms continues.

What is Text Analytics:

Text Analytics is nothing but the automated process of collecting the large amount of unstructured text data and converting it into usable structured data.

Differences between Text Mining and Text Analytics:

Applications of Text Analytics:

Text analytics is used in the below fields:

Steps Involved in Text Analytics are shown in the below screenshot:

 

 

1.     Language Identification

2.     Tokenization

3.     Sentence Breaking

4.     Part of speech tagging

5.     Chunking

6.     Syntax Parsing

7.     Sentence Chaining

Impact of Text Analytics in VOC and VOB

Text analytics is the business focused concept of transforming unstructured text data into structured and usable data. It's considered to be an important part of business intelligence processes as it used to improve product, employee experience and customer satisfaction.

 

Text analysis uses many statical and machine learning techniques, these techniques involve information retrieval from unstructured input data like measure of customer feedback, product reviews etc. and processes into outputs like derived pattern, trends, or focused action.

 

Text analytics is divided into different group.

Descriptive Analytics    - Unstructured text into trends.

Predictive Analytics      - Unstructured historical information into trends.

Prescriptive Analytics   - Unstructured information into data driven strategies or recommended action.

 

Organizations can leverage text analytics to generate useful information from unstructured data which can be used to focus on targeted action to improve customer satisfaction by identifying causes and churn, examining emerging trend, unlocking new opportunities, capturing market with effective marketing.

 

Challenges in Text Analytics

Text analytics is challenging due to quality, quantity and complexity of data being captured. Also captured data can be incomplete, inconsistent or may have error.

It required lot of storages for significantly high quantity of data with complexity.

It is difficult to integrate and combine data in different format such as in form of images, Audio, Video.

Text analytics tool must be compatible with data infrastructure and system.

 

Solutions for Challenges

Data Pre-processing and Pre-cleaning is essential for text analytics to reduce complexity and improve quality. Data Pre-processing and Pre-cleaning also helps to reduce load on server due on extra storage. Data analysis can be done using algorithm and techniques such as machine NPL, learning, deep cleaning.  Various tools such as PythoneR, PyTorch, PacCy are being used to perform these tasks.

TEXT ANALYTICS OR TEXT MINING

 

TEST ANALYTICS is the process of analysing and processing large volume of unorganized and unstructured text data thru a software for indentification of any sort of pattern, logic, concept, keywords or other attributes of data.

CHALLENGES ADDRESSED BY TEXT ANALYTICS

• Used for Opinion mining or sentiment analysis by reviewing social networks, emails, reviews for positive and negative reviews or feelings of customer. This is used to fix issues in products or service before it impacts the sales, revenue or profits.
• Data mining is also used for screening job candidates based on the keyword present in their resumes vis-a-vis requirement for the post.
• Used for blocking spam emails as per the actions and words available in past data.
• Also used for classifying contents of websites.
• Used for identification of fraudulant claims of insurance by analysing data.
• Diagnosis can be done by identification of description of medical situation or symptoms. 

CHALLENGES BEING WORKED UPON

• Data available for processing is often uncertain, unclear, indefinite and contradictory. Very difficult to process. 
• Ambiguity in the syntex of the data need to be processed along with presence of slang or sarcasm or techinal language to get proper result.
• Large amount of training data is required along with processing power which make the execution expensive.

If data is biased result of analytics can be imperfect.

Text Analytics (equipped with AI) has come a long way. In the initial days of Text Analytics, there were some major challenges such as converting unstructured data, handling ambiguity, language barriers and different styles/ dialects within a language itself.

 

With the advent of advanced AI such as machine learning, some of these challenges have been overcome and the quality of output now with Text Analytics is much better than what it was at its origin years.

 

As of today, some challenges still exist and addressing these would certainly help us accept the usage of Text Analytics even more. Some of these include:

 

1. Understanding hidden sentiments

2. Detecting sarcasm

3. Privacy concerns (probably the biggest challenge)

4. Multi lingual conversations

5. Legal complications and unspoken rules.

Shubham Chamoli's answer has been selected as the best answer.