Sentiment Analysis

1277 papers with code • 43 benchmarks • 92 datasets

Sentiment Analysis is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment.

Sentiment Analysis techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.

More recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.

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Latest papers with no code

A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning

no code yet • 25 Mar 2024

The approach focuses on generating weakly-supervised annotations by exploiting the strengths of both large language models (LLM) and traditional syntactic dependencies.

Large language models for crowd decision making based on prompt design strategies using ChatGPT: models, analysis and challenges

no code yet • 22 Mar 2024

This paper analyzes the use of ChatGPT based on prompt design strategies to assist in CDM processes to extract opinions and make decisions.

Extracting Emotion Phrases from Tweets using BART

no code yet • 21 Mar 2024

Sentiment analysis is a natural language processing task that aims to identify and extract the emotional aspects of a text.

On Prompt Sensitivity of ChatGPT in Affective Computing

no code yet • 20 Mar 2024

Recent studies have demonstrated the emerging capabilities of foundation models like ChatGPT in several fields, including affective computing.

AraPoemBERT: A Pretrained Language Model for Arabic Poetry Analysis

no code yet • 19 Mar 2024

Moreover, the proposed model significantly outperformed previous work and other comparative models in the tasks of poems' sentiment analysis, achieving an accuracy of 78. 95\%, and poetry meter classification (99. 03\% accuracy), while significantly expanding the scope of these two problems.

FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications

no code yet • 18 Mar 2024

This is achieved by fine-tuning the Llama2 7B model on a small portion of supervised financial sentiment analysis data, so as to jointly handle the complexities of financial lexicon and context, and further equipping it with a neural network based decision mechanism.

Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding

no code yet • 17 Mar 2024

To address them, we propose Mixture-of-Prompt-Experts with Block-Aware Prompt Fusion (MoPE-BAF), a novel multi-modal soft prompt framework based on the unified vision-language model (VLM).

Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language Models

no code yet • 17 Mar 2024

This paper presents a comprehensive examination of the impact of tokenization strategies and vocabulary sizes on the performance of Arabic language models in downstream natural language processing tasks.

Deep Learning-based Sentiment Analysis in Persian Language

no code yet • 17 Mar 2024

Recently, there has been a growing interest in the use of deep learning techniques for tasks in natural language processing (NLP), with sentiment analysis being one of the most challenging areas, particularly in the Persian language.

Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis

no code yet • 15 Mar 2024

To address the issue of multiple aspect categories and sentiment entanglement, we propose a hierarchical disentanglement module to extract distinct categories and sentiment features.