What are the challenges of performing sentiment analysis on short text data (e.g., tweets)?

Jul 7, 2016 ... Sentiment analysis can also provide analytical perspectives for financial investors who want to discover and respond to market opinions [3, 4]. Jun 16, 2015 ... In this paper, we aim to tackle the problem of sentiment polarity categorization, which is one of the fundamental problems of sentiment analysis. Sep 15, 2018 ... The traditional approaches to Twitter Sentiment Analysis (TSA) include dictionary-based and use supervised machine learning tools for sentiment ... Nov 27, 2023 ... Conventional sentiment analysis methods often fail to capture this heterogeneity, potentially leading to misjudged economic consequences for ... Specifically, the self-attention mechanism enables AMIC to generate context-aware word embeddings, assigning document-specific sentiment scores to words based ... Sentiment analysis provides an effective way to evaluate written or spoken language to determine if the expression is favorable, unfavorable, or neutral. The sentiment analysis algorithm determines if a chunk of text is positive, negative or neutral. It uses natural language processing (NLP) techniques such as ... Mar 7, 2023 ... In this blog, we'll demonstrate how to perform sentiment analysis with the space features in BigQuery ML by training and inferencing machine learning models ... Oct 25, 2022 ... Sentiment analysis classifies the sentiment of the reviews into positive or negative sentiment. You use the Sentiment workflow to train a BERT- ... ... used for fine-tuning the LLM (Step 4). State-of-the-art pre-trained LLMs can perform sentiment analysis without any fine-tuning using zero-shot or few-shot ...