A Comprehensive Survey of Natural Language Processing Techniques for Sentiment Analysis

Abstract

Sentiment analysis, a subfield of Natural Language Processing (NLP), plays a crucial role in understanding and extracting human emotions, opinions, and attitudes from textual data. This paper presents a comprehensive survey of the various techniques and approaches employed in sentiment analysis. It aims to provide a detailed overview of the state-of-the-art methods, their applications, and the challenges that researchers and practitioners face in this domain.

The survey begins by introducing the fundamental concepts of sentiment analysis and its significance in the era of big data and social media. It explores the primary types of sentiment analysis, namely, document-level, sentence-level, and aspect-level sentiment analysis, shedding light on their unique characteristics and applications.

Subsequently, this paper delves into the pre-processing steps crucial for sentiment analysis, such as text cleaning, tokenization, and feature extraction. It discusses the importance of sentiment lexicons and their role in quantifying sentiment in text.

The survey comprehensively covers supervised and unsupervised machine learning techniques employed for sentiment analysis, including support vector machines, recurrent neural networks, and the emerging field of transformer-based models. It also touches upon the integration of sentiment analysis with other NLP tasks like named entity recognition and topic modeling.

Furthermore, this paper addresses the challenges in sentiment analysis, including sarcasm detection, context awareness, and handling imbalanced datasets. It also explores emerging trends such as the incorporation of multimodal data and cross-lingual sentiment analysis.