TL;DR
This article will mainly introduce the application scenarios, advantages and disadvantages, as well as the development prospects of various methods in deep learning.
The study concluded that advances in deep learning models have significantly improved the accuracy of sentiment analysis, and recommends developing models capable of more effectively handling linguistic complexities, such as sarcasm, irony, regional dialects, and multiple dialects.
Nowadays, Natural Language Processing, or NLP, is a key component of many programs that analyze and comprehend human language. The sentiment analysis of mobile product reviews collected from the Kaggle repository—more especially, the 20,710-review Amazon Mobile evaluations dataset—is the main emphasis of this research....
This review paper looks at the main data mining methods used for sentiment analysis in Indian regional languages, including machine learning, lexicon-based, rule-based, deep learning, and transformer-based approaches and highlights what they do well and where they struggle.
This paper presents a lightweight sentiment classification model based on Long Short-Term Memory networks, developed as a foundational text-analysis component for future multimodal emotion recognition systems, and provides a reproducible and computationally efficient baseline suitable for integration into broader multi...
Advances in mobile technologies have established social media as a major platform for expressing emotions and opinions. Analyzing such publicly shared sentiments enables companies and political organizations to make informed and data-driven decisions. As a result, sentiment analysis has become an essential tool for r...
Sentiment analysis is an important method used to understand people’s opinions, emotions, and views from text data. Because social media sites like Twitter are so widely used, a lot of content is produced in a variety of languages, which presents difficulties for utilizing Natural Language Processing (NLP). Traditional...
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