Built using Python, Streamlit, and NLTK, the Hate Speech Detection App employs a Decision Tree Classifier for identifying hate speech in text. It features real-time speech input, NLP preprocessing, and a user-friendly Streamlit interface, offering both visual and text-to-speech result presentation.

The document classification solution should significantly reduce the manual human effort in the HRM. It should achieve a higher level of accuracy and automation with minimal human intervention.

Simple sentiment analysis of IMDB movie reviews dataset using count vectorizer, Tfidfvectorizer and nltk library.

In this project, we aim to analyze hotel reviews to determine the underlying sentiment expressed by customers. Our goal is to differentiate between positive and negative reviews using Natural Language Processing (NLP) techniques and machine learning algorithms.

The scope of this project is to classify fake and true news. After performing an analysis on the dataset using two different vectorizers and two machine learning algorithms, the results are conveyed in the form of accuracy score and confusion matrices.

Building a KNN model. The dataset for this project collects part of the knowledge from the API TMDB, which contains only 5000 movies out of the total number. Model the data using a KNN, analyze the results and optimize the model.

Analyze Google Play store dataset and model the data using Naive Bayes to create a review classifier.

Movie Review Classification with TF-IDF Vectorize & SVC given a set of text movie reviews that have been labeled negative or positive

AI-powered classifier mobile app using NLP to spot fake job ads and protect users from online scams. Our system analyzes language patterns and leverages algorithms to create a safe and trustworthy job search experience.

Women's' E-commerce product review dataset downloaded from Kaggle will undergo in this code a sentiment analysis process

A Machine Learning Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like MultinomialNB, LogisticRegression, SVC, DecisionTreeClassifier, RandomForestClassifier, KNeighborsClassifier, AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier, GradientBoostingClassifier, XGBClassifier to compare accuracy an

This project employs emotion detection in textual data, specifically trained on Twitter data comprising tweets labeled with corresponding emotions. It seamlessly takes text inputs and provides the most fitting emotion assigned to it. This app has more than 400 visitors!

Job scams are doubling, putting job seekers at risk. This project fights back with an AI-powered classifier using NLP to spot fake job ads and protect users from online scams. Our system analyzes language patterns and leverages algorithms to create a safe and trustworthy job search experience. Stop scams, empower job seekers, and build trust online

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Data Science - Text Mining Work

Sentiment analysis of movie reviews based on NLP and Machine Learning!

It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization

This project is to classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews using NLP.

The movie recommendation system is implemented using content based filtering

Twitter Sentiment Analysis Using InSet (Indonesia Sentiment Lexicon) and Random Forest Classifier