OTMISC-Topic-Modeling-Tool

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Topic modeling for NYT articles.

We present our concept of a new type of Active-Learning for Deep Learning with NLP text classification and experimentally prove its performance against Random Sampling as well as its runtime performance on the Security Threat dataset from CySecAlert. These new Active Learning algorithms are based on Sentence-BERT and BERTopic clustering algorithms with allow us to generate fixed length tokens for whole sentences to make them comparable to each other. Further the Tokens are Clustered using K-Means or HDBScan to get diverse clusters to pick the samples out of them.

Natural Language Processing with Twitter data

CTM HackLab 2018

Aspect Based Sentiment Analysis (ABSA) of Customer Reviews

Kmeans and SOM clustering for 20newsgroup

Inpoda est un programme python qui traite et analyse les données d'une base de données stocké au format JSON. L'enjeux principal d'Inpoda est d'extraire les 'Topics' et sentiments des tweets d'une base de données internationale.

Adapted BERTopic pipeline for Topic Modeling the arXiv dataset

In Florida and Pennsylvania, Yelp reviews paint a vivid picture of dining experiences across American, Chinese, and Italian cuisines. Using sentiment analysis and topic modeling, we uncover key themes that shape customer satisfaction. From flavor and service to ambiance and value, one factor stands above all—food quality.