REAL-TIME SENTIMENT ANALYSIS ON TWITTER USING LSTM FOR ENHANCED SOCIAL MEDIA MONITORING AND USER INTERACTION INSIGHTS
DOI:
https://doi.org/10.29121/granthaalayah.v12.i5.2024.6096Keywords:
Real-Time Sentiment Analysis, Lstm, Social Media, User Interaction, Online Reputation, Digital AgeAbstract [English]
In today's digital age, an online reputation is a crucial asset for any business. Poorly managed responses to negative reviews on social media can lead to significant costs and damage. Sentiment analysis provides an effective way to monitor and analyze online opinions, particularly in real-time, allowing businesses to track public sentiment regarding their products and services. This project leverages sentiment analysis on Twitter to harness the power of real-time data, enabling businesses to assess and respond to customer feedback promptly. By using Long Short-Term Memory (LSTM) models, this approach offers advanced capabilities in analyzing tweet sentiments, providing a deeper understanding of consumer sentiment and enhancing social media monitoring.
One key improvement of this project over existing tools is the focused collection of data exclusively from Twitter, reducing noise and minimizing the risk of false results caused by irrelevant data sources. By analyzing user interactions on social media, beyond basic metrics like likes, shares, and comments, sentiment analysis seeks to uncover the underlying emotions and motivations of consumers, providing valuable insights for brands, public figures, NGOs, governments, and educational institutions.
Existing sentiment analysis tools typically require a background in data science and advanced technical knowledge. However, this project introduces a user-friendly interface, allowing non-experts to easily access and interpret sentiment analysis results. The interface will display product reviews along with their corresponding sentiments, providing a seamless experience for the user. Additionally, the project incorporates a phrase-level sentiment analysis feature, which analyzes user-input phrases and predicts the sentiment behind them, offering a more granular and precise understanding of social media content.
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Copyright (c) 2024 Jatin Gupta, Vipanshu Sharma, Yash Kumar, Shefali Madan
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