What type of study is this?

This is a Quantitative Study study.

October 16, 2025

Sentiment Analysis Using Deep Learning Techniques

Key Points

Deep learning techniques enhance sentiment analysis by automating data categorization in social media.
Automated learning models can effectively handle unstructured data from platforms, improving sentiment classification accuracy.
Challenges in sentiment analysis include the lack of labeled data in NLP, which deep learning methods aim to address.
Cross-lingual sentiment analysis using deep neural networks can broaden the understanding of global user sentiments.

Abstract

Massive troves of information, including users' perspectives, feelings, ideas, and disputes regarding various social events, goods, brands, and politics, are produced by various online platforms, such as social networks, forums, review sites, and blogs. Readers, product sellers, and legislators all pay close attention to the opinions offered by internet users. Sentiment analysis has garnered a lot of interest because of its ability to evaluate and organize the unstructured type of data seen in social media. The term "sentiment analysis" is a method of text organization used to categorize the conveyed mindset or emotions as either "negative," "positive," "neutral," "positive," "negative," "thumbs up," "thumbs down," etc. The difficulty with sentiment analysis is that there is not enough labeled data in the NLP sector. Since deep learning models are useful because of their automated learning potential, this problem has been solved by combining sentiment analysis with deep learning. The current research on using deep learning models like deep neural networks to address sentiment analyzing challenges such as sentiment categorization, cross-lingual issues, textual/visuals analysis, product reviews analysis, etc., are summarized.

Mark Helpful

Bookmark

Relay