Sentimental Analysis of Movie Tweet Reviews Using Machine Learning Algorithms
| dc.contributor.author | Kari, Hemanth Kumar | |
| dc.date.accessioned | 2023-12-26T18:46:09Z | |
| dc.date.available | 2023-12-26T18:46:09Z | |
| dc.date.issued | 2024-01-03 | |
| dc.identifier.doi | 10.24251/HICSS.2024.627 | |
| dc.identifier.isbn | 978-0-9981331-7-1 | |
| dc.identifier.other | 3d23985e-8142-4088-b090-939a7ff254e4 | |
| dc.identifier.uri | https://hdl.handle.net/10125/107012 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Proceedings of the 57th Hawaii International Conference on System Sciences | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Design and Architectures of Data-Centric and Knowledge Based Systems | |
| dc.subject | experimental results. | |
| dc.subject | machine learning | |
| dc.subject | micro blogging | |
| dc.subject | sentiment analysis | |
| dc.subject | system architecture | |
| dc.title | Sentimental Analysis of Movie Tweet Reviews Using Machine Learning Algorithms | |
| dc.type | Conference Paper | |
| dc.type.dcmi | Text | |
| dcterms.abstract | Sentiment analysis stands as a prominent tool within microblogging platforms, gaining substantial traction as a means to discern public opinion and sentiment across various topics, including movie tweet reviews. In response to this demand, the study introduces a robust system architecture that incorporates an array of algorithms, ranging from Multinomial Naive Bayes and Support Vector Machine (SVM) to K-Nearest Neighbors (KNN), Bernoulli’s Naive Bayes, and Random Forest. This architecture is meticulously trained using annotated Twitter data, methodically excluding non-opinionated content while precisely identifying sentiment. Thorough experimentation underscores the effectiveness of our methodology. To accomplish this, we curate an extensive data set of movie-related tweets, each carefully labeled with sentiments spanning positive, negative, or neutral tones. The methodological framework involves intricate text preprocessing steps, encompassing tokenization, stemming, and the removal of extraneous stop words. This facilitates the extraction of essential features and the conversion of raw text into numerical representations suitable for machine learning. Our sentiment classification modeling employs a diverse ensemble of machine learning algorithms, including Naive Bayes, Support Vector Machines, and Recurrent Neural Networks. The assessment involves a range of metrics such as accuracy, precision, recall, and F1-score, supported by rigorous techniques like cross-validation to enhance the dependability and robustness of results. Our unique contribution lies in the strategic deployment of algorithms and a resilient system architecture adept at surmounting the challenges inherent to microblogs. We emphasize the utmost importance of preprocessing in augmenting the precision of sentiment classification. This research substantiates the system’s aptitude in extracting valuable insights for informed decision-making through the scrutiny of microblog sentiments. | |
| dcterms.extent | 12 pages | |
| prism.startingpage | 5225 |
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