Sentiment Analysis
Sentiment analysis aims to automatically determine the emotional tone expressed in text, aiming to understand opinions and attitudes. Current research heavily focuses on leveraging large language models (LLMs) like BERT and its variants, along with other architectures such as graph neural networks, to improve accuracy and efficiency, particularly in multimodal settings and low-resource languages. This field is crucial for various applications, including market research, social media monitoring, and understanding public opinion, driving advancements in natural language processing and impacting decision-making across numerous sectors.
Papers
Contrasting the efficiency of stock price prediction models using various types of LSTM models aided with sentiment analysis
Varun Sangwan, Vishesh Kumar Singh, Bibin Christopher
CIDER: Context sensitive sentiment analysis for short-form text
James C. Young, Rudy Arthur, Hywel T. P. Williams
Opinion mining using Double Channel CNN for Recommender System
Minoo Sayyadpour, Ali Nazarizadeh
Political Sentiment Analysis of Persian Tweets Using CNN-LSTM Model
Mohammad Dehghani, Zahra Yazdanparast
Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations
Xinyu Liu, Yan Ding, Kaikai An, Chunyang Xiao, Pranava Madhyastha, Tong Xiao, Jingbo Zhu
L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models
Aabha Pingle, Aditya Vyawahare, Isha Joshi, Rahul Tangsali, Raviraj Joshi