Python Code for AI Exercise: Assigning Sentiment Assignments for unknown words with Large Language Models

#AI Excercise Github Link: Nidhika1121/LLM_Sentiments: LLM_Sentiments (github.com) Now, with this, exercise, sentiment can be assigned to the slangs in languages, even to unknown words, new words occurring in these times of app specific short words, added in our vocabulary. In last article, we saw the AI exercise what 5 class sentiment alignment means. However, theContinue reading “Python Code for AI Exercise: Assigning Sentiment Assignments for unknown words with Large Language Models”

Sentiments with Large Language Models as multi-class classifications problem

  Abstract: Sentiment analysis has been a topic of many researches and interest. In this article, computational steps are shown and worked out to show, how we can have multiple centers in the sentiment classification task. This does not stop the existing works but provides more avenues to open to research applications more. Conceptualise aContinue reading “Sentiments with Large Language Models as multi-class classifications problem”

Sentiment Analysis, a Quick Review of Essentials

This is first in series of evaluations of sentiment scores and applying these in useful applications. In coming articles you can see some applications of the topics introduced. Todays topic is of introduction of Sentiment Analysis. This is not a basic’s session but a review session. Sentiment Analysis can be defined in several ways. TheContinue reading “Sentiment Analysis, a Quick Review of Essentials”

Soft Rough Sets for Textual Data Analytics and Language Processing

Here, are some areas we have been working. You shall get glimpses of its workings. For any future work, help, suggestions, you can write to us. This article covers topics on Soft Rough Sets and it’s applications in Text Analytics and Language Processing. The coding shall use Python as much as it can.

Decision Tree using Python With Random Data

Import all required libraries — — — — — — — — — — — — — import pandas as pd from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn import tree import matplotlib.pyplot as plt import random import os — — — — — — — — — — — — — —Continue reading “Decision Tree using Python With Random Data”

Decision Trees and Futuristic Applications

​​​​​Nidhika Yadav Abstract: Decision Trees (DT) are a familiar and important tool in AI applications. One must understand, that the use of DT extends much beyond its current scope. Hence futuristic applications are proposed in article. That is to say, the AI toolkit can be used in a variety of applications. The current article explainsContinue reading “Decision Trees and Futuristic Applications”

NLP Futuristic Applications, Use Cases, Research Areas. Lecture 1 – Focus on futuristic Keywords Selection algorithms and it’s possible applications.

Here are my lectures 1. Introduction, 2. Brief Illustration, on topic “NLP Futuristic Applications, Use Cases, Reseach Areas. Lecture 1 – Focus on futuristic Keywords Selection algorithms and it’s possible applications.” Here NLP applications are understood in preview of futuristic view points, future possibilities and future applications. These are here illustrated to understand the directionsContinue reading “NLP Futuristic Applications, Use Cases, Research Areas. Lecture 1 – Focus on futuristic Keywords Selection algorithms and it’s possible applications.”

Keyword Selection – Supervised versus Unsupervised – Futuristic view

Keyword selection is about selection of important keywords from a text, a collection of texts or even in books, collection of catalogues. Here I present futuristic view of keyword selection, it applications and proposed uses. Not the past uses we all are familiar with – the future uses of keywords selection. -Actually, keyword extraction reducesContinue reading “Keyword Selection – Supervised versus Unsupervised – Futuristic view”