Category Archives: AI

Sentiment Analysis with SentiWordNet- Part II

#AI, #ArtificialIntelligence #NLP This article is an application of sentiwordnet lexical database. In last article I provided with the code to access the sentiment, here are some more details on sentiwordnet with examples. Sentiwordnet is a nice database for looking up to sentiment of a word given its POS tag. So you can look upContinue reading “Sentiment Analysis with SentiWordNet- Part II”

Sentiment Analysis with Python- SentiWordNet

Here is a code for sentiment analysis explained with sentiwordnet. The code is here for determining the sentiment analysis of a sentence. I have used this on the climate dataset with me. And found the most positive sentiment-oriented documents and most negatively oriented documents, also the most lengthy documents too. Here is a simple codeContinue reading “Sentiment Analysis with Python- SentiWordNet”

Word2vec and the distance between words based on it

This is a short article to show how the distance between words can be computed using word2vec. I often follow deep theoretical and large practical articles with small articles covering basics. Even basics are important and hence here today is a small article on how to compute the distance between words with help of word2vec.Continue reading “Word2vec and the distance between words based on it”

Predicting Preferred Working Hours with RNN GRU

In this article, we propose to predict the preferred working hours by individuals based on the present inputs provided. Why this is required? As the work is preset and work goals are made based on resource predictions. Employees are resources of an organization and if we accumulate current phenomena with time we can predict wellContinue reading “Predicting Preferred Working Hours with RNN GRU”

Fuzzy Rough Set based Evaluations of Summaries

Note: This same article appears in my median.com account as well #AI-EXERCISE #RESEARCH-Excercise This short article lays emphasis on how to evaluate summaries produced from Text Summarization. The toolkit used here is Fuzzy Rough sets. The reference summary and the system summary are evaluated and compared for similarity using Fuzzy Rough Set based lower similarityContinue reading “Fuzzy Rough Set based Evaluations of Summaries”

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.”