International Journal of Advanced Computer Technology

https://ijact.org/index.php/ijact/issue/feed International Journal of Advanced Computer Technology 2025-01-04T23:28:29+00:00 Dr. Amrendra Singh Yadav editor@ijact.org Open Journal Systems <center> <h3 style="font-family: georgia,serif; font-size: 20px; color: #0066ff;">A Peer reviewed, Open access, Bimonthly, International Journal since 2012</h3> </center> <section class="additional_content"> ISSN : 2319-7900 | DOI : 10.18535/ijact | <a href="https://ijact.org/revista/index.php/ijact/gateway/plugin/WebFeedGatewayPlugin/rss">RSS</a> <a href="https://ijact.org/revista/index.php/ijact/about/submissions">Submission</a>  Call For Paper Volume 8 issue 2 December 2019 Submission Deadlines Volume / Issue / Month : Volume 08 Issue 2, 25 December 2019 Read More  Submit Article at : <a href="mailto:editor@ijact.org">submitpaper@ijact.org</a> International Journal of Advanced Computer Technology (IJACT) ISSN - 2319-7900 is a leading international online journal for publication of new ideas. It's a peer-reviewed, open access scholarly journal that publishes research works and review articles in all research domains (Engineering, Technology). This is an open access journal which means that all content is freely available to the Researcher /users of any institution with vision spreading and sharing knowledge. These published articles/papers are accessible to the research scholars through a wide indexing policy adopted by this online international journal. Hence, they can freely be accessed and used by everyone for the improvement of current trends in science and technology. This Journal favors and promotes online publication of papers to truly present itself as an online journal. </section> <center></center> IJACT follows publication ethics during phases of online publication inline with the guidelines and standards developed by the Committee on Publication Ethics (COPE) to avoid any malpractices by all concerned including authors, reviewers, editors and publishers. This is in accordance with the BOAI definition of open access. We are committed and promise to take this journal to greater heights. We invites authors for the guest editorship, reviewers, program for improving the journal quality and also sharing the innovative ideas. Frequency : Six issues/year Submit Article at submitpaper@ijact.org https://ijact.org/index.php/ijact/article/view/152 A Comprehensive Study on Sentiment Analysis of Twitter Data: Techniques, Challenges, and Future Prospects 2025-01-04T23:28:29+00:00 Khwaish Tiwari e21cseu0799@bennett.edu.in Shreya * e21cseu0598@bennett.edu.in

The increasing prevalence of social media platforms, especially Twitter, has led to an abundance of user-generated content that offers valuable insights into public opinion and sentiment. This paper presents a comprehensive study on sentiment analysis of Twitter data, emphasizing the significance of analyzing sentiments expressed in short, often informal, textual data. The study reviews and compares existing methods, including machine learning and dictionary-based approaches, for extracting and classifying sentiments. Advanced techniques such as Naive Bayes, Support Vector Machines, logistic regression, and deep learning models like Recurrent Neural Networks (RNN) are evaluated for their performance and applicability to Twitter sentiment analysis. The challenges of analyzing Twitter data, such as handling informal language, abbreviations, sarcasm, and context dependency, are explored. Preprocessing steps, including tokenization, removal of noise, and feature extraction techniques like N-grams and part-of-speech tagging, are implemented to enhance data quality. The study also integrates hybrid approaches to leverage the strengths of various models for improved accuracy and scalability. Experimental results demonstrate the effectiveness of combining traditional machine learning techniques with deep learning models to achieve higher sentiment classification accuracy. The findings highlight the potential of advanced natural language processing methods and multimodal content analysis in overcoming challenges unique to Twitter. This study contributes to the field by addressing critical research gaps and providing practical insights for real-time sentiment analysis applications. The implications span multiple domains, including business, politics, and public health, enabling organizations to make informed decisions based on dynamic user sentiments.