Topic extraction for technology related text

Discover the main subject of the text along with attributes for that subject.

3D box

Context aware Topics

Our context aware semantic nlp will give you semantically better results than other commercial and open-source topic extraction solution in the market today.

We go deeper into the semantics and create topic modelling to understand the intent of the text better than others to give you the best topic recognition.

Classified & Sorted

The results are classified into a hierarchy of topics and their attributes. The attributes for the topics are further sorted in descending order of relevance. This lets you identify the core of the text without drowning in an ocean of keywords.

Infer - beyond keywords

ThatNeedle is not just picking up the keywords in the text. It has the capability to deduce topics and make inference based on content, even when those words are not present in the original text.

No expertise required

You don't need to be an expert in topic modelling, NLU(Natural language understanding), Natural language processing (NLP) , machine learning(ML), deep learning, data science, data modelling, Aritificial intelligence(AI), LDA(Latent Dirichlet Allocation) etc. No need to even get acquianted with all the shiny new technologies.
Our tool is ready to use so you can focus on your business.


Setting up the microservice on GNU/Linux flavours

1. After you have downloaded the binary you can run it as :-

thatneedle_topic_for_tech.l <port_number>

2. You will be prompted for a license key
Please type it and press enter.
Enter license key here: your-licence-key

Remember that you must be connected to the internet for this.

3. Once the is accepted and validated, you are ready to use the service on your server !

You can access the service at
http://:6060/?q=your technology text here

Calling the microservice from a python program

Topic extraction in python made easy with our microservice 

import requests
import json
import urllib

query = "Setting up Tensorflow on linux" # from the user/ UI
query = urllib.parse.quote(query)
results = requests.get(F"http://localhost:9090/?q={query}")
results_dictionary = json.loads(results.text)

About the deliverables:

1. Deliverable is a binary for 1 machine ( single server license )

2. Please note that source-code is not delivered with this transaction.

3. You can run this as a microservice on your server.

4. You can use it in accordance with EULA(End user license agreement)

5. Please ask for an online trial before you purchase. There are no refunds.