Sher Muhammad Daudpota

TL;DR: Deep long short-term memory models used for estimating the sentiment polarity and emotions from extracted tweets have been trained to achieve state-of-the-art accuracy on the sentiment140 dataset and the use of emoticons showed a unique and novel way of validating the supervised deep learning models on tweets extracted from Twitter.

TL;DR: This study proposes a supervised aspect based opinion mining system based on two-layered LSTM model for performing aspect based sentiment analysis on students’ feedback for evaluating faculty teaching performance.

TL;DR: This work has performed sentiment analysis on tweets related to Apple products, which are extracted from StockTwits (a social networking site) from 2010 to 2017 and shows that there is positive relation between people opinion and market data.

TL;DR: Assessing text sequence generation algorithms coupled with grammatical validation on domain-specific highly imbalanced datasets for text classification finds that the performance of same deep neural network models improve up to 17% when datasets are balanced using generated text.

TL;DR: In this paper, a large-scale dataset consisting of manually labeled students' comments collected from the Coursera online platform was used to evaluate various machine learning models for aspect-based opinion mining to address this challenge effectively.