A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications

TL;DR

This survey comprehensively reviews sentiment analysis, covering tasks, approaches, applications, challenges, and opportunities across multiple data formats, emphasizing its importance in understanding customer opinions and market trends.

Contribution

It provides an extensive overview of recent developments in sentiment analysis across various domains and data types, highlighting challenges and future opportunities.

Findings

Sentiment analysis is crucial for understanding customer feedback.

Deep learning approaches have advanced sentiment classification accuracy.

Challenges include handling unstructured data and multilingual content.

Abstract

Sentiment analysis (SA) is an emerging field in text mining. It is the process of computationally identifying and categorizing opinions expressed in a piece of text over different social media platforms. Social media plays an essential role in knowing the customer mindset towards a product, services, and the latest market trends. Most organizations depend on the customer's response and feedback to upgrade their offered products and services. SA or opinion mining seems to be a promising research area for various domains. It plays a vital role in analyzing big data generated daily in structured and unstructured formats over the internet. This survey paper defines sentiment and its recent research and development in different domains, including voice, images, videos, and text. The challenges and opportunities of sentiment analysis are also discussed in the paper. \keywords{Sentiment Analysis, Machine Learning, Lexicon-based approach, Deep Learning, Natural Language Processing}