Smart Decisions with Opinion Mining

Abstract: The runaway growth of web technology has resulted in an unprecedented volume of data being produced and published on the web each day. Social networking sites such as Twitter and Facebook have turned into indispensable zones for individuals to share thoughts, experiences, and opinions around the world. Sentiment analysis which involves the extraction and analysis of opinion from text, is central to gauging public feeling, monitoring trends, business strategy, and customer satisfaction with regards to unstructured and heterogeneous nature of Twitter data, most research has been conducted on how to use sentiment analysis methods to classify opinion as positive, negative, or neutral. In this paper, sentiment analysis of social media data is investigated based on a Twitter dataset, utilizing machine learning methods such as Long Short-Term Memory (LSTM) networks for precise sentiment classification.

Web technology, social networking sites, Twitter, Facebook, Sentiment analysis, Opinion extraction, public sentiment, trend monitoring, business strategy, customer satisfaction, machine learning, long short-term memory (LSTM) networks

1. Asafuzzaman, et al.(2020). Sentiment Analysis of Twitter Data. [Google Scholar] [Crossref]

2. Zhang, et al. (2018). Deep Sentiment Analysis Learning. [Google Scholar] [Crossref]

3. Hossain, et al. (2019). Comparative study of Sentiment Analysis Methodologies. [Google Scholar] [Crossref]

4. Kumar, et al. (2021). Sentiment Classification Using Machine Learning. [Google Scholar] [Crossref]

5. Gupta, et al. (2020). Exploring Sentiment Analysis for Social Media. [Google Scholar] [Crossref]

6. Ramesh, et al. (2022). Novel Deep Learning Approaches for Sentiment Analysis. [Google Scholar] [Crossref]

7. Nair, et al. (_2021). Real-Time Sentiment Analysis on Twitter. [Google Scholar] [Crossref]

8. Chen, et al. (2019). Hybrid Approaches in Sentiment Analysis. [Google Scholar] [Crossref]

9. Singh, et al. (2020). Impact of Preprocessing on Sentiment Analysis. [Google Scholar] [Crossref]

10. Patel, et al. (2023). Challenges and Opportunities in Social Media Sentiment Analysis. [Google Scholar] [Crossref]

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