#TextAnalytics

[hitechanalytics]

Transform Unstructured Text into Actionable Insights with Our Text Analytics Services

Unlock insights from unstructured data with our expert text analytics services, including sentiment analysis, text mining, and semantic analytics to boost customer experience and business intelligence.

[scriptzol]

Which metric is commonly used to evaluate the quality of a text summarization system?

a) BLEU score b) ROUGE score c) Precision d) Recall

[shireen46]

Text Analytics: Unlocking the power of Business Data

Due to the development in the use of unstructured text data, both the volume and diversity of data used have significantly increased. For making sense of such huge amounts of acquired data, businesses are now turning to technologies like text analytics and Natural Language Processing (NLP).

The economic value hidden in these massive data sets can be found by using text analytics and natural language processing (NLP). Making natural language understandable to machines is the focus of NLP, whereas the term “text analytics” refers to the process of gleaning information from text sources.

What is text analysis in machine learning?

The technique of extracting important insights from texts is called text analysis.

ML can process a variety of textual data, including emails, texts, and postings on social media. This data is preprocessed and analyzed using specialized tools.

Textual analysis using machine learning is quicker and more effective than manually analyzing texts. It enables labor expenses to be decreased and text processing to be accelerated without sacrificing quality.

The process of gathering written information and turning it into data points that can be tracked and measured is known as text analytics. To find patterns and trends in the text, it is necessary to be able to extract quantitative data from unprocessed qualitative data. AI allows this to be done automatically and at a much larger scale, as opposed to having humans sift through a similar amount of data.

Process of text analysis

Assemble the data- Choose the data you’ll research and how you’ll gather it. Your model will be trained and tested using these samples. The two main categories of information sources are. When you visit websites like forums or newspapers, you are gathering outside information. Every person and business every day produces internal data, including emails, reports, chats, and more. For text mining, both internal and external resources might be beneficial.

Preparation of data- Unstructured data requires preprocessing or preparation. If not, the application won’t comprehend it. There are various methods for preparing data and preprocessing.

Apply a machine learning algorithm for text analysis- You can write your algorithm from scratch or use a library. Pay attention to NLTK, TextBlob, and Stanford’s CoreNLP if you are looking for something easily accessible for your study and research.

How to Analyze Text Data

Depending on the outcomes you want, text analysis can spread its AI wings across a variety of texts. It is applicable to:

Whole documents: gathers data from an entire text or paragraph, such as the general tone of a customer review.

Single sentences: gathers data from single sentences, such as more in-depth sentiments of each sentence in a customer review.

Sub-sentences: a sub-expression within a sentence can provide information, such as the underlying sentiments of each opinion unit in a customer review.

You can begin analyzing your data once you’ve decided how to segment it.

These are the techniques used for ML text analysis:

Data extraction

Data extraction concerns only the actual information available within the text. With the help of text analysis, it is possible to extract keywords, prices, features, and other important information. A marketer can conduct competitor analysis and find out all about their prices and special offers in just a few clicks. Techniques that help to identify keywords and measure their frequency are useful to summarize the contents of texts, find an answer to a question, index data, and generate word clouds.

Named Entity Recognition

NER is a text analytics technique used for identifying named entities like people, places, organizations, and events in unstructured text. It can be useful in machine translation so that the program wouldn’t translate last names or brand names. Moreover, entity recognition is indispensable for market analysis and competitor analysis in business.

Sentiment analysis

Sentiment analysis, or opinion mining, identifies and studies emotions in the text.

The emotions of the author are important for understanding texts. SA allows to classify opinion polarity about a new product or assess a brand’s reputation. It can also be applied to reviews, surveys, and social media posts. The pro of SA is that it can effectively analyze even sarcastic comments.

Part-of-speech tagging

Also referred to as “PoS” assigns a grammatical category to the identified tokens. The AI bot goes through the text and assigns each word to a part of speech (noun, verb, adjective, etc.). The next step is to break each sentence into chunks, based on where each PoS is. These are usually categorized as noun phrases, verb phrases, and prepositional phrases.

Topic analysis

Topic modeling classifies texts by subject and can make humans’ lives easier in many domains. Finding books in a library, goods in the store and customer support tickets in the CRM would be impossible without it. Text classifiers can be tailored to your needs. By identifying keywords, an AI bot scans a piece of text and assigns it to a certain topic based on what it pulls as the text’s central theme.

Language Identification

Language identification or language detection is one of the most basic text analysis functions. These capabilities are a must for businesses with a global audience, which in the age of online, is the majority of companies. Many text analytics programs are able to instantly identify the language of a review, social post, etc., and categorize it as such.

Benefits of Text Analytics

There is a range of ways that text analytics can help businesses, organizations, and event social movements:

1. Assist companies in recognizing customer trends, product performance, and service excellence. As a result, decisions are made quickly, business intelligence is improved, productivity is raised, and costs are reduced.

2. Aids scholars in quickly explore a large amount of existing literature and obtain the information that is pertinent to their inquiry. This promotes quicker scientific advancements.

3. Helps governments and political bodies make decisions by assisting in the knowledge of societal trends and opinions.

4. Search engines and information retrieval systems can perform better with the aid of text analytics tools, leading to quicker user experiences.

5. Refine user content recommendation systems by categorizing similar content.

Conclusion

Unstructured data can be processed using text analytics techniques, and the results can then be fed into systems for data visualization. Charts, graphs, tables, infographics, and dashboards can all be used to display the results. Businesses may immediately identify trends in the data and make decisions thanks to this visual data.

Robotics, marketing, and sales are just a few of the businesses that use ML text analysis technologies. To train the machine on how to interact with such data and make insightful conclusions from it, special models are used. Overall, it can be a useful strategy for coming up with ideas for your company or product.

[markettrendsus]

How Contact Center Intelligence Leverages AI and Analytics to Uncover Insights

The Contact Center Intelligence (CCI) market is expected to grow continuously in the coming years. CCI solutions provide actionable insights to contact center agents and managers by analyzing various data sources like call transcripts, screen recordings, customer surveys, and more. Key capabilities of CCI include speech and text analytics, real-time guidance, performance management, and journey mapping.

Report: https://dimensionmarketresearch.com/report/contact-center-intelligence-market/

The main growth drivers for the CCI market include the need for improved customer experience, increased use of AI and automation in contact centers, and rising volumes of multichannel customer interactions. Companies across industries are focused on delivering personalized, omnichannel customer experiences while optimizing the efficiency of their contact center operations. CCI enables them to uncover customer sentiment, track key performance metrics, identify coaching opportunities for agents, and understand customer journeys.

According to research firm Dimension Market Research, The Global Contact Center Intelligence Market is expected to reach a value of USD 2.1 billion in 2023, and it is further anticipated to reach a market value of USD 12.6 billion by 2032 at a CAGR of 22.1%. North America accounted for the largest market share .

Take a look at the Free Sample Report: https://dimensionmarketresearch.com/report/contact-center-intelligence-market/requestSample/

Key players in the CCI market include Amazon Web Services Inc., Artificial Solutions International AB, Observe.AI, Avaya Inc., Google LLC, IBM Corporation, Microsoft Corporation, Nuance Communication, Oracle Corporation, Zendesk Inc., and others. These vendors offer a range of capabilities including speech analytics, text analytics, analytics platforms, AI-enabled agent assist, and workforce optimization.

As contact centers handle rising contact volumes across channels like voice, email, web chat, social media, and messaging apps, there is a greater need for an integrated approach to CCI. Vendors are enhancing their product portfolios through acquisitions and partnerships to provide an end-to-end CCI suite spanning orchestration, AI, analytics, coaching, and more. The adoption of cloud-based CCI solutions is also gaining momentum. Overall, the growing importance of customer experience management and AI adoption is expected to spur strong demand for CCI over the forecast period

[jinactusconsulting]

The role of natural language processing in AI

Natural Language Processing (NLP) is a crucial component of Artificial Intelligence (AI) that deals with the interaction between human language and computer systems. NLP involves the development of algorithms and computational models that enable machines to understand, interpret, and generate natural language.

NLP is essential in AI because it enables machines to communicate effectively with humans, enabling the creation of conversational agents, chatbots, and virtual assistants. NLP algorithms also help machines to analyze and extract information from large amounts of unstructured text data, such as social media posts, emails, and news articles. This allows businesses and organizations to gain valuable insights from this data, such as sentiment analysis, topic modeling, and entity recognition.

Other applications of NLP in AI include language translation, speech recognition, and text-to-speech conversion. In language translation, NLP algorithms enable machines to understand and translate text from one language to another, making communication between people who speak different languages possible. In speech recognition, NLP algorithms enable machines to understand spoken language and convert it into text, which is then processed and analyzed. In text-to-speech conversion, NLP algorithms enable machines to convert written text into spoken language, which is useful for applications such as audiobooks and navigation systems.

Overall, NLP is an essential component of AI that enables machines to understand, interpret, and generate natural language, allowing for effective communication between humans and machines.

[bestdatasciencecourseindia]

📺 Unlocking TV Success with Data Science: Predict Viewer Behavior & Create Hit Shows! 📊

📺 Using Data Science to Predict TV Viewer Behavior & Formulate a Hit TV Show 📊

Media companies are increasingly leveraging the power of data science to understand and predict viewer behavior. In our latest blog, we delve into how Pivotal Data Labs is using machine learning and unstructured data, like transcripts, to gain insights that help TV executives make informed decisions.

Key insights include: ✅ How combining unstructured and structured data sources enhances viewership predictions. ✅ The importance of text analytics and machine learning models in driving content decisions. ✅ How our team built an open solution that scales across multiple TV shows, offering real-time actionable insights.

We share the approach, tools, and lessons learned from working with a global media conglomerate to improve TV show performance.

Curious about the power of data science in the media industry? Check out the full blog and learn more about how data can be a game-changer in entertainment!

🔗 Read the full blog here

#DataScience #MachineLearning #TVIndustry #PredictiveAnalytics #ViewerBehavior #TextAnalytics #AnalyticsJobs

[mrshadddd]

Deciphering the Power of Text Analytics: From unstructured chaos to actionable insights. Explore sentiment analysis, named entity recognition, and more. #TextAnalytics #DataInsights #NLP

[bytesview]

Here's a guide to text analysis. Give it a read.

[cmisushil]

Using text analytics tools can help to get a handle on these questions and identify areas where users need to improve. One such tool is called named-entity recognition. This technology works by breaking down the text into its constituent tokens, which make up entire words, or even subwords. It also discards unwanted text by assigning it a grammatical category. By analyzing the text, users can learn about the opinions of their customers and understand which products and services they want. Moreover, this analytics can also help to extract more than a hundred types of PII (protected health information).

[sharvaricmi]

Text Analytics Market Global Future Growth, Business Prospects, Leading Players and Future Investments by Forecast to 2025

Text mining, also known as text analysis, is the process of extracting high-quality structured data from unstructured or unorganised text. Linguistic, statistical, and machine learning approaches are some of the strategies that can be used to obtain data from unstructured data. Text analytics is used for a variety of things, including summarization, sentiment analysis, explanation, investigation, and data classification. Text analytics software with a user-friendly interface allows for improved data representation and analysis.

North America currently dominates the global Text Analytics Market in terms of revenue due to rising technological discoveries, increased need for data analysis, and the presence of numerous key players in this sector. The market in Europe accounted for the second-highest revenue share in the global text analytics market, followed by markets in Latin America, Asia Pacific, and the Middle East and Africa, respectively, due to the acceptance of high-end technologies, growing social media platforms, and a preference for cloud-based technologies for storage. The market in Asia Pacific is predicted to grow at the quickest CAGR during the forecast period. 

Read more @ https://cmiaspireblog.blogspot.com/2022/01/text-analytics-market-by-deployment.html

[charlesmerwin-blog]

teX.Ai - Ai based Text Analytics Services and Solutions

Businesses today face challenges deriving insights from their text data. teX.ai helps produce structured data, metadata & insights by extracting data, summarizing information and classifying content.

teX.ai is one of the leading Ai based Text Analytics product. It’s completely customizable and helps convert complex text data into accurate insights. teX.ai is a hands-on, easy to use text analytics tool built on sophisticated Python libraries. This SaaS based text analytics suite provides insights to enhance customer experience by processing raw text data using NLP, Ai and DL algorithms. It can effectively solve challenges faced by industries Banking, Retail, Ecommerce, Manufacturing, Education, Hospitals (healthcare) and Lifesciences in Text Extraction, Text Summarization and Text Classification.

Endorsements Top 25 Machine Learning Startups To Watch In 2020 (Forbes) - https://www.forbes.com/sites/louiscolumbus/2020/04/26/top-25-machine-learning-startups-to-watch-in-2020/#179829861f52 One of the Leading Artificial Intelligence Software (Goodfirms) - https://www.goodfirms.co/artificial-intelligence-software/ Best Text Anlytics Software (G2) - https://www.g2.com/categories/text-analysis?order=g2_score&page=4#product-list

For Demo : https://www.tex-ai.com/demo/ For Inquiry : https://www.tex-ai.com/contact-us/

Follow us on Social media Facebook - https://www.facebook.com/texaisoftware LinkedIn - https://www.linkedin.com/company/tex-ai/

[outliersxx]

Web Scrapping : It is basically data scrapping used for extracting data from different websites📎 Follow us for regular updates related to analytics @outliers_x📌📌 #webscrapping #outliers_x #data #datascience #machinelearning #ai #analytics #deeplearning #datamining #datavisualization #textmining #textanalytics #python #coding https://www.instagram.com/p/B3Z4EBBghK-/?igshid=h4x9hhaoxd9

[digitalmarketingenthusiast]

Research activities are important parameters of economic growth and youth are drivers of it. Bytesview celebrates this #nationalyouthday by providing highly efficient text analytics solutions to researchers & students for research projects and assignments.

[jqadan]

✅ text Analysis ✅ topic modeling ✅ sentiment analysis ✅ word cloud ✅ NLP analysis ✅ text visualization Visit our website for more information! 🌐https://www.vizrefra.com #textanalysis #visualizeText #textanalytics #textsummary #sentiment #textvisualization