Sentiment Analysis of IMDB movie reviews using SVM with an accuracy of 87%.
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Updated
Sep 3, 2024 - Jupyter Notebook
Sentiment Analysis of IMDB movie reviews using SVM with an accuracy of 87%.
PHP StopWords removal library with support for multiple languages.
This project aims to detect hate speech on Twitter using advanced NLP and machine learning techniques, exploring feature extraction methods like TF-IDF and sentiment analysis, and evaluating models such as Logistic Regression and SVM.
Bidirectional RNNs are used to analyze the sentiment (positive, negative, neutral) of movie reviews. .
A module for creating stopword lists for any language, based on a set of documents.
Making Word Cloud in English and Chinese context.
Laboratory 3 - Retrieval Information
Laboratory 2 - Retrieval Information
Data Pre-processing Application/UI is a simple UI which can automate repitive tasks, while ensuring consistency and efficiency in NLP data preprocessing.
Extract text content from an HTML page, process it, and extract unique words from the processed text. This notebook utilizes various text processing techniques including cleaning, normalization, tokenization, lemmatization or stemming, and stop words removal.
This project implements NLP and Classification models for Spam SMS detection
This was a hackathon project that I worked on for BestBuy around classifying the call transcripts using ML & NLP techniques
Python package that makes it easy to use stop words lists in Python projects.
This application serves as a powerful tool for categorizing restaurant reviews as either negative or positive. Its primary purpose is to provide restaurateurs and managers with an efficient means of evaluating customer feedback. By distinguishing between negative and positive comments, this app aims to enhance the quality of service.
"KZM COVID Informatics: A repository for data analysis and insight extraction from the CORD-19 dataset, focused on advancing our understanding of the COVID-19 pandemic."
Long english text passages are given, a genuine topic is needed to be assigned to the particular text passage. After cleaning the dataset, features were learnt using thidf approach, Linear SVC is used to get the final prediction
Device Recommendation System using Cosine Similarity. It will recommend Electronic Gadgets based on Similar Configuration.
NLP methods for distinguishing positive and negative reviews written about movies.
Basic text preprocessing pipeline, which includes tokenization, stemming, and stopword removal
Visualizing word embeddings generated by GloVe and Word2Vec models using the t-SNE algorithm
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