Deep Learning Based Enhancement of Social Media Sentiment Analysis

Abstract

Users’daily uploads accessibly on Twitter, Facebook, and Instagram where their opinions or feelings are expressed in text, videos, or pictures. Sentiment interpretation within these platforms proves helpful for companies, analysts, or even government leaders. However, the often-imprecise techniques used in sentiment analysis, especially the older, traditional ones, fail to meet expectations—particularly in cases where slangor sarcasm heavily influences the discourse, or as it is known: people’s ways of communicating evolve all the time.Ourcurrentstudyisfocusedonthepowerfulimpactthatdeeplearningcanbringtowardsocial media sentiment analysis. More sophisticated algorithms like LSTMs and CNNs alongside BERT and other transformer models train on colossal data sets and masters to uncover the actual intended meaning of phrases. That shifts the goal of sentiment analysis from simply identifyingthesentimentaspositiveornegativetoachievinganuancedunderstandingcapable of reasoning to different classifications of sentiments.First,wetrytoobtaindatastraightoutofsocialnetworks.Thisisfollowedbyaseriesofclean up actions aimed at data cleaning and treatment. These data undergo cleansing or thorough cleaning.Then, the data undergo model training through supervised or unsupervised machine learning on a multi-class sentiment system identifying sentiments on seeing displaying words as favorable, unfavorable, neutral or opposing. Results indicate quite an improvement where this approach has led to the prediction of sentiments beyond detection madepossible by automated machine learning alongside manual model building and lexicon-based models, surpassing the previously captured logic-based approaches mark.Deeplearningsubstantiallyadvancesthemannerinwhichpublicopinionisunderstoodorseen. Ultimately helping corporations calibrate their perception of customers’ feelings on their offerings or assist government leaders

Introduction

Social media is a vast and dynamic space where individuals express opinions on topics ranging from politics to brands. Understanding these emotions—positive, negative, or neutral—is essential for businesses, researchers, and decision-makers. Traditional sentiment analysis methods often fail to capture the complexities of social media language, such as slang, sarcasm, and evolving trends.

This research explores the use of deep learning models—including LSTM, CNN, and Transformers (like BERT)—to improve emotion detection on social media. These models are more effective at handling large-scale, noisy text data and extracting deeper contextual meaning.

Methodology Overview:

• Data Collection: Real-time data from Twitter, Facebook, and YouTube using web scraping and APIs.

• Preprocessing: Cleaning text (removing URLs, emojis, hashtags), tokenizing, lemmatizing, and addressing slang/satire.

• Feature Extraction: Using Word2Vec, GloVe, and TF-IDF to convert text into numerical formats.

• Model Training: Comparing deep learning models (LSTM, CNN, BERT) trained on labeled data.

• Evaluation: Assessing model performance using accuracy, precision, and recall metrics.

Contribution:

The research enhances sentiment analysis accuracy, enabling businesses to better understand customer feedback, analysts to monitor public opinion, and researchers to detect social trends more effectively. It contributes to advancements in Natural Language Processing (NLP) and AI-based text analysis.

Conclusion

Inthisresearch,wefocusedonimprovingtheaccuracyandeffectivenessofemotionalanalysis ofsocialmediausingdeeplearningtechniques.Socialmediaplatformsgeneratelargeamounts of users\' opinions every second, but traditional spirit analysis methods often struggle with informal and complex nature of social media. By using advanced deep learning models such as LSTM, CNN and Burt, we could better understand references, emotions and patterns in the user -borne text. These models provided more accurate classification than older machine learning methods. Ourexperimentshaveshownthatdeeplearningtoalargeextentimprovestheperformanceof emotional analysis, making it more reliable for real -world applications such as brand monitoring, customer warning, tracking of opinion and more. Overall, this research contributes to the Natural Language Processing (NLP) field, and shows how intensive learning can be used to understand human feelings on social media, provides valuable insights into companies, researchers and decision makers.

References

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Copyright

Copyright © 2025 Ayushi Singh, Deepshikha Prajapati, Divyanshi Dyori, Kajal Kumari, Dr. Sureshwati , Dr. Saumya Chaturvedi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.