A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques

Sentiment analysis of COP9-related tweets: a comparative study of pre-trained models and traditional techniques

Introduction

Sentiment analysis has become a crucial area of research in natural language processing in recent years. The study aims to compare the performance of various sentiment analysis techniques, including lexicon-based, machine learning, Bi-LSTM, BERT, and GPT-3 approaches, using two commonly used datasets, IMDB reviews and Sentiment140. The objective is to identify the best-performing technique for an exemplar dataset, tweets associated with the WHO Framework Convention on Tobacco Control Ninth Conference of the Parties in 2021 (COP9).

Methods

A two-stage evaluation was conducted. In the first stage, various techniques were compared on standard sentiment analysis datasets using standard evaluation metrics such as accuracy, F1-score, and precision. In the second stage, the best-performing techniques from the first stage were applied to partially annotated COP9 conference-related tweets.

Results

In the first stage, BERT achieved the highest F1-scores (0.9380 for IMDB and 0.8114 for Sentiment 140), followed by GPT-3 (0.9119 and 0.7913) and Bi-LSTM (0.8971 and 0.7778). In the second stage, GPT-3 performed the best for sentiment analysis on partially annotated COP9 conference-related tweets, with an F1-score of 0.8812.

Discussion

The study demonstrates the effectiveness of pre-trained models like BERT and GPT-3 for sentiment analysis tasks, outperforming traditional techniques on standard datasets. Moreover, the better performance of GPT-3 on the partially annotated COP9 tweets highlights its ability to generalize well to domain-specific data with limited annotations. This provides researchers and practitioners with a viable option of using pre-trained models for sentiment analysis in scenarios with limited or no annotated data across different domains.

Proteins Need Extra Attention: Improving the Predictive Power of Protein Language Models on Mutational Datasets with Hint Tokens

In this computational study, we introduce "hint token learning," a novel machine learning approach designed to enhance protein language modeling. This method effectively addresses the unique challenges of protein mutational datasets, characterized by highly similar inputs that may differ by only a single token. Our research highlights the superiority of hint token learning over traditional fine-tuning methods through three distinct case studies. We first developed a highly accurate free energy of folding model using the largest protein stability dataset to date. Then, we applied hint token learning to predict a biophysical attribute, the brightness of green fluorescent protein mutants. In our third case, hint token learning was utilized to assess the impact of mutations on RecA bioactivity. These diverse applications collectively demonstrate the potential of hint token learning for improving protein language modeling across general and specific mutational datasets. To facilitate broader use, we have integrated our protein language models into the HuggingFace ecosystem for downstream, mutational fine-tuning tasks.