Monday, January 25, 2021

Top 10 text analytics startup in 2020

 Gone are the days when it was hard to conduct text analytics from a wide array of text-based content? At present, a plethora of text analytics have come into being, which plays an integral role in driving through the review and social media websites so that you can find better results and engagement. 

In the current era of social media, text analytics startup is coming up with unique solutions that are beneficial to opt for the desired trends and patterns in consumers' behaviors from different textual platforms like blogs, reviews, comments, to name a few. 

It is possible to leverage such data-led insights in branding and designing the products. This write-up comprises of the names of the top 10 text analytics companies in 2020:

Semantria

Semantria is cloud-based tool that offers digital solutions of high quality. It is useful in analyzing different survey responses, tweets, user feedbacks, comments, to name a few. It is possible to perform text analytics in less than five minutes through Microsoft Excel by choosing this tool. You do not need any technical experience.

Luminoso

Luminoso has earned a high reputation in transforming the unstructured text data into crucial insights into the business. It is a popular tool that provides a suitable option for the business organization in enhancing the return on investment of the business.

teX.Ai

teX-Ai is a popular text analytics startup that offers a helping hand in generating structured data, insights, and metadata with the extraction of data from the text. It boasts advanced text analytics, which helps reveal the actual meaning from a specific language with the use of amazing text extraction solutions & abilities.

HPE

HPE or Hewlett Packard Enterprise contributes to being the innovative cloud platform that stands out of the ordinary in conferring advanced ML APIs. It is an efficient platform that allows startups, enterprises, and developers to develop data enriched enterprise and mobile applications. 

Lexalytics

Lexalytics happens to be a popular business enterprise that plays an integral role in translating data into a profitable decision. This business makes the best use of on-prem NLP, machine learning, and the start of the art cloud technologies, which help transform textual data into meaningful insights. 

This platform's solutions are leveraged in a bunch of industries that offer a helping hand in people analytics, social media monitoring, management of reputation, the voice of the customers, and employees along with different regulatory compliance programs.

Semeon Analytics

Semeon is recognized to be a platform driven by Artificial intelligence, which provides the power for customer experience. It identified cultural context, sarcasm, irony, bias to defeat the complexity of the language. It is also beneficial in collecting a regular representation of the say of the customers. It is also useful to the brands to become more responsive. 

Thus the start provides a suitable choice to business organizations to be customer-centric as it offers assistance in creating an efficient customer experience. It is also beneficial in offering extensive customer support, communication, marketing, innovation, and product departments, as you try to support the culture, process, and overall management of company transformation.

Primer

With its headquarters in San Francisco, Primer is regarded as a machine intelligence company that stands out of the ordinary to offer premium quality solutions. This enterprise makes use of the combination of different unsupervised and supervised Machine learning models. Such a solution is effective in ingesting textual data of colossal amounts in several languages. 

Besides this, they also stand out of the ordinary in conferring a summary of the most vital information. It also makes the proper use of computational engines, which are effective in the contextualization and summarization of data. Primer comes with the technical platform, which is used by a variety of government agencies, such as the Fortune 50 company, and the intelligence agency.

Kapiche

Kapiche, which is driven by Artificial Intelligence. It is beneficial in understanding the consumer feedback during scale. It is also beneficial for business organizations to make crucial decisions. 

It happens to be a data mining and analytics platform that comes with a specialization within text analytics tool. This enterprise conducts an analysis of premium quality of information from the text and searches ontology. This startup is equipped with an amazing bespoke indexer, which is meant to accomplish different analytics solutions in real-time for various unstructured data sets. 

The best thing about this tool is that it can extract the topic Model dynamically, automatically, and rapidly. It is effective in extending the search functionality for finding different insights. Another worth mentioning reason behind this tool's wide popularity is that it offers restful APO and server wrapper.

Alkymi

Alkymi, this tool provides an opportunity for the potential audience to clean unstructured details and process them to the structured data to manage the business transactions, data, and risks of the market firms.

MindGap

MindGap is one of the startups that features in-depth expertise in strategy consulting and machine learning frameworks and technology. It is a popular company that is beneficial in conferring NLP capabilities, ML and AI solutions, visualization, data segmentation, video analytics, computer vision, personalized multi-channel chatbots, to name a few.

ThoughtTrace

ThoughtTrace contributes to being contract analytics and AI-based document software. This company provides a cloud-based platform that stands second to none in offering contextual insights from different complicated contracts and documents. This solution offers amazing features like in-depth commercial insights, enterprise search, contextual relations, document identification, to name a few. A plethora of industries makes use of this text analytics solution for enhancing the revenue of the business.

Suppose you are searching for the best-in-class solution that can help you perform text analytics from a huge amount of colossal data. In that case, you should choose from either of the solutions mentioned earlier.

Author: Muthamilselvan is a passionate Content Marketer and SEO Analyst. He has 5 years of hands on experience in Digital Marketing with IT and Service sectors.


Monday, March 16, 2020

5 Techniques used globally for Text Analytics Solutions

What is Text Analytics Solutions? 

Text Analytics solutions is the path toward urging significance out of made correspondence. In a customer experience setting, text analysis suggests assessing data that was formed by, or about, customers. 

Text analytics services can be performed genuinely, yet it is an inefficient methodology. Right now, text analytics software has been made that uses text mining python with computations to find significance in huge proportions of substance. 

How it will help for a Business?

Messages, online reviews, tweets, call center administrator notes, study results, and various sorts of made information all hold information into your customers. There is in like manner a wealth of information in recorded correspondences that can without a doubt be changed into content. 

Text analytics solution is the best way to deal with the essentials from the whole of this unstructured substance. It lets you uncover models and points, so you perceive clients' perspectives. It reveals their necessities and requirements. 

Here we will discuss the most prominent procedures or frameworks which are used to pass on the business exercises. 

Text Analytics Solution Approach 1: Word Spotting 

The insightful text analytics NLP society doesn't enlist such a technique, and as it ought to be. In all honesty, in the academic world, word spotting implies handwriting affirmation 

There is in like manner watchword spotting, which revolves around talk taking care of. The crucial idea behind substance word spotting is this: If a word appears in data, we can acknowledge that this bit of substance is "about" that particular word. For example, if words like "cost" or "cost" are referenced in a study, this infers this review is about "Cost". 

Text Analytics Solution Approach 2. Manual Rules 

The Manual Rules approach is immovably related to word spotting. The two procedures deal with a comparable guideline of making a match structure, yet these models can moreover get exceptionally puzzling. 

Most of the text analytics service providers similarly as various other smaller players, who sell text analytics software as an extra to their guideline offering, give an interface that makes it easy to make and direct such principles. 

The upside of this procedure is that once set up, you can run a colossal number of analysis pieces and get a nice framework of the inside classes referenced in the substance. 

Text Analytics Solution Approach 3. Text Categorization 

Here, we'll be looking at Text Categorization, the first of the three approaches that are truly motorized and use figurines. 

This system is powered by AI. The crucial idea is that an AI based text analytics tool (there are many) separates in advance truly requested models (the arrangement data) and understands the standards for arranging new models. 

Some of the text analytics companies that rely upon text categorization give instruments that make it straightforward for people to set up the computations, with the objective that they give indications of progress after some time 

Text Analytics Solution Approach 4: Topic Modeling 

Subject showing is in like manner a Machine Learning approach, yet an independent one, which infers that this strategy gains from rough substance. 

On occasion, I hear bits of information specialists imply any Machine Learning approach as "topic showing", yet data scientists for the most part mean a specific count when they state subject illustrating. 

Text Analytics Solution Approach 5. Topical Analysis 

The cost of acting late or leaving behind earnest bits of information is tremendous! It can incite losing customers and stale improvement. 

After Thematic participated in their program, we've been mentioned direction on different occasions through a review, once by methods for an individual email, and moreover eye to eye. YCombinator moreover uses Thematic to fathom all the info they assemble. 

With respect to customer analysis? 
  • Definite, unequivocal and huge assessment 
  • Ability to see creating themes brisk, without the need of setting things up 
  • Straightforwardness in how results are made, to get region capacity and common sense data 

The advantage of Thematic Analysis is that this system is independent, inferring that you don't need to set up these arrangements early, don't need to set up the estimation, and right now without quite a bit of a stretch catch the dark inquiries.

These are the various text analytics solutions and techniques which are used for any business improvements or operation. Hope you enjoyed the techniques which are discussed above and your feedback is always welcome through the comment section.

Thanks and Regards,
Charles

Thursday, March 12, 2020

Text Analytics Solutions with NLTK


What Text Analytics solution is and how it has any sort of impact? 

We're absolutely acquainted with content, since we investigate and make it dependably. Here we will
consider messages to be raw text data for the endeavors we structure, programs that control and
investigate it in an assortment of enchanting ways. 


The size of information is developing at exponential rates a tiny bit at a time. In every way that really
matters, all kinds of establishments, affiliations, and undertakings are dealing with their information
electronically. 


It is instigating errand to pick fitting models and guides to oust noteworthy information from this
goliath volume of information. Ordinary data scraping tools can't oversee printed information since it
requires time and exertion to evacuate data. 


Standard objective for Text Analytics Software 

  • To remove the substance from boa constrictor cloud 
  • To inspect the removed substance from boa constrictor cloud 
  • Solicitations to be utilized to isolate the substance in every single authentic approaches to
manage get a critical yield 

  • To know the centrality of dissecting content 
  • To know how and what figuring is utilized to isolate the substance and python center individual
works

It is likewise suggested as substance text analytics, all around identical to content assessment, is the
way toward getting exceptional data from content. Phenomenal data is routinely chosen through the
concocting of models and models through procedures, for example, exact model learning. 


Text analytics solutions generally joins the way toward sifting through the information content, inciting
structures inside the created information, at last assessment and translation of the yield. 


Text mining joins data recovery, lexical assessment to contemplate word rehash scatterings, plan
confirmation, naming/explanation, data extraction, text mining solutions including affiliation and
union assessment, depiction, and farsighted appraisal. 


A major hearted perspective as for text analytics Python is that it licenses you to type genuinely into the
vigilant center individual—the program that will be running your Python programs. You can locate a
useful pace center individual utilizing an unmistakable graphical interface called the Interactive Development Environment (IDE).


On a Mac you can locate this under Applications → MacPython, and on Windows under All Programs→
Python. Under Unix you can show Python from the shell to making dormant.


Getting started with Text Analytics Python in NLTK 

Right when you've introduced NLTK, fire up the Python go between as already, and present the text
required for the book by framing the going with two solicitations at the Python brief. 


Downloading the NLTK Book Collection: Browse the accessible packs utilizing nltk.download(). The
Collections tab on the downloader shows how the gatherings are assembled into sets, and you should
pick the line checked book to get all information required for the models and activities right now 


Exactly when the text is downloaded to your machine, you can stack some of it utilizing the Python go
between. The fundamental development is to type a phenomenal solicitation at the Python brief, which
urges the center individual to stack two or three works for us to look at: from nltk.book import *.


These are the things that need to be followed or getting started with text analytics software for a quick
decision making. And your thoughts and feedback are welcome through the comment section.


Thanks and Regards
Charles

Monday, March 9, 2020

Text Analytics Python: Business Use Cases and its Solutions

Man-made reasoning is actually the most extending innovation the world is seeing at present. Almost the entirety of text analytics companies main associations are retaining as well as attempting to absorb the innovation. 

Point of fact, Artificial Intelligence will reign everywhere throughout the world in the coming tomorrow. This is a direct result of its promising viewpoint of mechanizing a wide range of help administrations, dissecting both organized and unstructured information faultlessly and successfully and in this way, improving the general customer encounters with proper usage of text analytics solutions

In spite of the fact that AI development may sound extremely dynamic and subsequently, truly multifaceted, it isn't in every case such. With text analytics NLP contributing fundamentally in AI and as content investigation and content mining are expecting critical jobs in Artificial knowledge, the associations are to a great extent relying upon them to mine content to bits of knowledge. You can likewise get a hold of value text analytics software whenever it might suit you. 

Allows first have a look at a portion of the utilization instances of text analytics solutions

Search Autocomplete 

This is a boundless element utilized by the mass in the event that they are scanning for something. Begun first by Google in quite a while as a web crawler, Search Autocomplete has been useful since as a sort of text analytics NLP

Budgetary Trading 

Budgetary exchanging is likewise massively profited by the NLP. Space express estimation examination/grouping would essentially end up being significant for any money related broker. Moreover, Intent Recognition and text analytics solutions are being bridled to grasp the thing of exchange and their amounts. 

Evaluating Creditworthiness 

Other than all these, text analytics Python is probably going to have a significant influence in breaking down and surveying the reliability of the clients with no earlier budgetary records. 

There are numerous applications which have outfit the innovation in full and are endeavoring to help different associations in their separate organizations. Our simple and successful Text Analytics Python would basically do some incredible things for your situation, if you need to keep pace with the computerized age.

Thanks and Regards,
Charles,

Text Analytics using Python: Step by Step Guide



What is Text Analytics? 
Text mining, or text analytics solution, is the way toward transforming normal everyday content
into important and significant data, for example transforming words into measurements and
numbers. 

Through applying methods from man-made consciousness, data recovery, data scraping, AI,
measurements, and computational semantics, data scraping tool gives important data valuable
to everything from biomedicine to internet based life checking to the financial exchange. 

Text Analytics solutions can be come down into 3 essential advances: 
  • Data Collection 
  • Data Preprocessing and 
  • Data Analysis 
Data Collection:
The initial step is to assemble a data set consisting of tweets, client remarks, messages, overview
reactions, or some other raw text data. You can physically reorder information from sites or
records to assemble littler data sets. For bigger data sets you will most likely need some
assistance from an information supplier. 

Data Preprocessing:
The subsequent stage is preprocessing, where the text data is parsed by mining catchphrases
and evacuating promotions (if material) or other unimportant data. Watchwords are resolved
through various components, including recurrence inside a report; recurrence over all records;
and grammatical form labeling, text analytics NLP

Data Analysis:
In the last advance, significant and valuable data is separated from the easier content portrayal
as text analytics software with great text mining procedures. 

Tokenization:
Tokenization in text analytics software is the way toward hacking up a given stream of content
or character succession into words, expressions, images, or other important components
assembled tokens which are gathered as a semantic unit and utilized as contribution for
additional handling, for example, parsing or data scraping tools. 

Tokenization is a valuable procedure in the fields of text analytics Python and information
security. 

It is utilized as a type of text summarization NLP and as a remarkable image portrayal for the
delicate information in the information security without trading off its security significance. 

Stop Word Removal:
Now and then an exceptionally regular word, which would have all the earmarks of being of
little importance in assisting with choosing reports coordinating client's need, is totally rejected
from the jargon. These words are designated "stop words" and the system is classified "stop
word evacuation". 

Lemmatization:
Lemmatisation (or lemmatization) in etymology, is the way toward diminishing the bent
structures or some of the time the determined types of a word to its base structure with the goal
that they can be examined as a solitary term. 

Lemmatization and stemming are firmly identified with one another as the objective of the two
procedures is to diminish the inflectional structures or derivationally related types of a word to
its base structure. 

Equivalent word Expansion:
Equivalent word extension, otherwise called lexical substitution, is the errand of supplanting a
specific word in a given ai based text analytics tool with another reasonable word comparative
in importance. 

Report Representation:
Report portrayal is a key procedure in the record preparing and data recovery frameworks. A
such changed record depicts the substance of the first reports dependent on the constituent
terms called file terms.

Conclusion
Hope you enjoyed the techniques as we discussed above in text analytics software. Hereby I
welcome you all to share your thoughts and feedback in the comment section.

Thanks and Regards,
Charles,

Wednesday, March 4, 2020

5 Important things you need to know about Text Mining Implementation


Text Analytics Solutions is the procedure of unstructured information into significant information which would help in business achievement. The vast majority of the business these days is contained with heaps of unstructured information. With huge amounts of unstructured information amounts to nothing. We can't anticipate client conduct and their moving activity. With the best possible examination of clients criticism or surveys we can improve our business move and appropriate dynamic to arrive at a greater ROI. This is the place Text Analytics software assumes a fundamental job. 

Text Analytics Companies can handling of unstructured information into important bits of knowledge which helps in better business activity. A detail passes on that practically 80% of the world's information is unstructured. 

The vast majority of the association stores their information in cloud stages. Just that the measure of information is quickly expanding while the information pours in different client sections. So preparing and examining utilizing that pack of information is a lot of outlandish for business move. Here the Text analytics tool, text mining procedures and application becomes possibly the most important factor. 

The procedure joins insights, AI, information mining, computational semantics, and so forth., The primary angles live with data scraping tool is to manage the normal language with semi organized or unstructured models. 

Fundamental strides in Text Mining
  • Gather unstructured information from different documents and different website pages 
  • Screening with the gathered data which assists with evacuating unknown data or undesirable information exists inside the record. It very well may be finished with the assistance of different Text Analytics Tools
  • Changing over applicable data from unstructured information into important bits of knowledge. 
  • Use MIS for breaking down examples from the gathered information. 
  • Scrambling or putting away the astute information with the database which is secure. This will help in appropriate dynamic procedure towards pattern examination. 

Significant Text Mining Techniques which utilized Today 

Text mining solutions as a rule utilize different data examination instruments and programming for the ideal information execution. We will talk about the significant things which are associated with text analytics tools are recorded underneath, 

Extraction of Information

Data extraction is the most significant thing with the text analytics software. It includes separating helpful data from the enormous piece documents which are unstructured or semi organized information. At that point the gathered data is put away in a seperate database. 

Recovery of Information

This is the procedure with the extraction of applicable information related with specific expressions or sets of words. It utilizes different calculations which assists with following and screen the client conduct. It likewise assists with deciding the utilization of dependable and applicable enlightening information appropriately. 

Classification

Arrangement of information is the procedure engaged with social affair data reports for examining with the objective of revealed subjects. As there are such huge numbers of associations engaged with the Text Analytics in NLP expanded colossally these days which run from mysterious/spam channel for customized business conveyance to site page classification. 

Clustering

Significant Text analytics companies process with Clustering. Grouping is the procedure which assists with recognizing structures which are inborn in nature. Handling important bunches from indistinct organized information with no earlier suggestion causes huge difficulties. Group investigation is the text analytics software helps with the dissemination of information with the distinguished bunches. 

Summarization

Summarization empowers you to peruse through different sets of data sources to make summarization with a lot of data. It joins with numerous techniques which use text analytics classification, relapse systems and choice procedure.

Conclusion:
These are the various text techniques which involved in text analytics software. Likewise we told earlier possessing with tons of data would leads to big tragedies to make a best decision making. This couldn't help your business at all. Your comments and feedback are welcome to improvise business decision and operating.

Thanks and Regards,
Charles

Thursday, February 27, 2020

Things you Need to Know: Text Analytics using Python

These days we are utilizing a lot more dialects to essentially convey among our neighbors or companions through different mediums. Around the globe we are imparting more than 6500 dialects. Among this there were such a large number of rules while building up the sentence. Here the content text analytics software a crucial job. 

As indicated by the details just 20% information can be produced by content we talk, we tweet someone or other. As an entrepreneur, they needed to know the criticism and feeling from the clients to ad lib their deals. 

What is Text Analytics or Text Mining? 

Text Analytics solutions are producing or shaping significant bits of knowledge from the crude content information. 

What is NLP? 

Common Language handling is a program which manages the human dialects and performs significant bits of knowledge. In straightforward words NLP is one of the significant segments which plays out the semantic examination which may assist the machine with reading the content. The Text Analytics NLP will utilize different strategy to release the unstructured or uncertainty content from the source record. 

At first NLTK library (Natural language toolbox) should be introduced as an initial step for executing text analytics using python which is anything but difficult to interface. 

Different procedure in NLP 

Tokenization: 

It is the initial phase in NLP and the procedure includes breaking strings into tokens. It includes with three significant advances, 
  • Concentrate the mind boggling sentence into words 
  • Understanding significance and significance of every word 
  • At long last delivering important depiction from a crude info. 

Alright we will examine in detail as beneath, 

Step 1: Split the content or words into string (for example "," are called tokens). 

Step 2: Find out recurrence or rehashed term from the crude content 

Step 3: Stemming - > It alludes to normalizing words into base structure 

Doorman Stemming: Removing Morphological and Grammatical blemishes 

Lancaster Stemming: Aggressive stemming calculation 

Step 4: Lemmatization - > Lemmatization attempts to change over a word into its base structure. 

Step 5: Removing or filtrering the Stop words 

Step 6: POS process which is finished by utilizing most prestigious devices, for example, NLTK, Spacy, TextBob, and so forth., 

Step 7: Named Entity acknowledgment - > Process of recognizing the area Name, Person Name, Company Name, and so forth., 

Step 8: Chunking - > It includes picking single snippets of data into greater pieces. 

This is the way Text analytics python for a business activities which in turns delivers more ROI. Expectation you appreciated the above systems and tips which helps during the time spent Text Analytics solutions. What's more, it would be ideal if you let me know your criticism and recommendations through the remark segment.

Thanks and Regards,
Charles