Volume-12 , Issue-5 , Oct 2024, ISSN 2320-7639 (Online)

• Open Access   Article

  Aspect-Based Sentiment Analysis for Hotel Reviews Using a Data Augmentation Approach

  Yojitha Chilukuri, Ulligaddala Srinivasarao

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.5 , pp.1-9, Oct-2024

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  Abstract
  The amount of user-generated textual material created by social networks, blogs, forums, and e-commerce websites is increasing at an astronomical pace. When it comes to determining the success of a product or service, the opinions of customers are critical. Due to this, interest in FE exams and assessment mining has surged. Angle`s put-together feeling examination depends on extracting item qualities from client assessments utilizing subject demonstrating and Latent Dirichlet Allocation (LDA). Because of information sparsity and the non-appearance of co-event designs in short texts, LDA won`t be quickly applied to client audits and other short texts. Various methods have been distributed for adapting the latest models like LDA for short. A Pachinko Allocation Model (PAM) is proposed in this paper as a one-of-a-kind methodology for opinion examination because of perspectives. The Pachinko Allocation Model is a new PAM adaptation that extracts product aspects. Data augmentation increases the text data set size for the text classification task. After that, features are extracted using TF-IDF-IC-SDF and TF-IGM methods, and the fine sentiment is extracted utilizing the opinion lexicon. According to the findings of the experiments, PAM is a competitive method for extracting aspects. The outcomes of the trial show that the novel sentiment classification approach is competitive in terms of product extraction. A statistical test has also been conducted.

  Key-Words / Index Term
  Feature Extraction, PAM, Aspect-based Sentiment Analysis, Topic Modelling

  References
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  Citation
  Yojitha Chilukuri, Ulligaddala Srinivasarao, "Aspect-Based Sentiment Analysis for Hotel Reviews Using a Data Augmentation Approach," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.5, pp.1-9, 2024

• Open Access   Article

  Predicting the Effect of Mobile Phone on Student Academic Performance Using Machine Learning

  Shuaibu Yau, Abdulsalam Abdulganiyu Ishola, Abdu Ibrahim Adamu

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.5 , pp.10-17, Oct-2024

  Abstract

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  Abstract
  The study aimed to predict the effect of mobile phones on students’ academic performance using Machine Learning, focusing on the Faculty of Science at the Federal University Birnin Kebbi (FUBK). With the increase in the prevalence of mobile phone usage among students, there is concern about its effect on academic performance. A total of 227 students partakes in the research, with a gender balance of 57.3% male and 42.7% female. Online Questionnaire was distributed for data collection among students from 200Level to 400Level, and responses were cleaned and preprocessed to ensure consistency and accuracy. Analysis was conducted using Python, employing two models: Random Forest (RF) and Decision Tree (DT). However, the results indicated that DT achieved an R²_score of 57%, whereas RF achieved an R²_score of 63% in predicting students` grade point averages based on their mobile phone usage. Other models, like Linear Regression, K-Nearest Neighbor (KNN), and Neural Network Regression, could also be explored for prediction purposes. This study recommends that universities consider implementing predictive models to inform students about their expected grade point averages, aiding them in focusing on their studies. Additionally, universities should encourage students to engage in meaningful tasks, providing additional data points for predictive models and improving their accuracy. Lastly, it is advised that universities be mindful of the ethical implications associated with the use of predictive models.

  Key-Words / Index Term
  Academic performance, Semester grade point, Machine learning, Regression analysis, Mobile phone, FUBK

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  Citation
  Shuaibu Yau, Abdulsalam Abdulganiyu Ishola, Abdu Ibrahim Adamu, "Predicting the Effect of Mobile Phone on Student Academic Performance Using Machine Learning," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.5, pp.10-17, 2024