Submission of an in-class NLP sentiment analysis competition held at Microsoft AI Singapore group. This submission entry explores the performance of both lexicon & machine-learning based models

In this project we have built a model which takes a dataset as an input andas an output gives the percentage of posive ,negative and neutral tweets in the given dataset. It is done using natural language processing library using scikit learn machine learning libraries such as textblob.

Goodness of Pronunciation Pipelines for OOV Removal

Simple lexicon-based persian sentiment analysis

Final Year Project: Sentiment Analysis Approach for Reputation Evaluation

Source code of paper "Incorporating prior knowledge into word embedding for Chinese word similarity measurement", accepted by ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP).

Topic: Opinion Mining and Summarization of Hotel Reviews

Generate synthetic labeled data for extremely low-resource languages using bilingual lexicons.

Twitter Sentiment Analysis Using Python

Investment Decision Support System

The purpose of creating this application is to help the government, especially the Directorate General of Taxes (DJP) in improving and fixing the problems that exist in the M - Tax application. This application is built using Flask as its framework and uses the Long Short - Term Memory (LSTM) and Lexicon Based algorithms in conducting sentiment …

A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor

Lexicon based segmentation of cursive handwritten images, and recognizing the characters using deep learning model.

Group project analyzing news sentiment of articles from The New York Times archives from 2015 to 2017. The app provides visualizations to understand sentiment trends over time, space and categories using Natural Language Processing, and allows user interactivity (@iCode13 worked on the geovizzes and contributed to frontend development). UT Austi…

🐦 Sentiment analysis on tweets

I performed sentiment analysis aimed at determining the sentiment of 50000 imDB movie reviews, whether they are positive, negative, or neutral. I employed various NLP approaches including lexicon based approaches, machine learning models, PLM models, and hybrid models, and assessed the performance on each type of model.

sentiment analysis with Lexicon, Machine Learning and Deep learning methods

A complete platform for gathering, pre-processing, and labeling tweets.