Kedibone Thantsha If you're looking for a ready-to-use software specifically tailored for analyzing text data to determine sentiment (positive/negative), uncertainty, forward-looking, and sustainability.

I would recommend you to use Python library NLTK (Natural Language Toolkit). NLTK is a popular for natural language processing library.

While it doesn't provide out-of-the-box functions for forward-looking or sustainability word analysis, it offers extensive functionality for text processing, sentiment analysis, and part-of-speech tagging. You can customize and extend it according to your specific needs.

https://www.nltk.org/

Sasibhushan Rao Chanthati , Yes am looking for a ready-to-use software. I sincerely appreciate. Have you used the software and if so kindly share with me the methodology.

Thank you

Kedibone

Kedibone Thantsha That is open source. You can try and see,

GNN would be more helpful in case of algorithms

Soumya Sri Perepu thank you. I sincerely appreciate. What does GNN stands for?

Kedibone Thantsha , For the sentiment analysis, can recommend for the prospect of the most recent advances is to go with zero-shot application of Large Language Models, and here is the software and the way you can use it: https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework?tab=readme-ov-file#zero-shot

I have a setup for a certain models that a part of the repository above as well as the part of the related studies: Preprint Large Language Models in Targeted Sentiment Analysis for Russian

In particular, it was shown that the straightaway application would results in behavior similar to fine-tuned version of the BERT model.

PS: You can also setup a chain of questions "Chain-of-Thought" that involve model responses in order to get the answer supported by intermediate responeses from model. At the moment, I am working on the related project adaptation here:

https://github.com/nicolay-r/quick_cot

Original studies: Preprint Reasoning Implicit Sentiment with Chain-of-Thought Prompting

Thanks!

Sincerely,

Nicolay

Nicolay Rusnachenko : thank you so much. I sincerely appreciate.

May God bless you

See https://stats4edu.com/services/writing-new-and-efficient-r-code-pyhton-code-sas-code-and-stata-code/

Popular sentiment analysis software includes:

These tools offer various features for analyzing and interpreting sentiment in text data.

Chuck A Arize thank you. I eventually managed to get Google Colab and i have analysed the texts and in the process of analysing data.