Prof. Ch Satyananda Reddy

https://doi.org/10.64823/ijter.2503018

In the era of global e-commerce, understanding customer sentiment across diverse languages is vital for enhancing user experience and business intelligence. This project, titled "Multilingual Sentiment Analysis in E-commerce Platform", focuses on predicting customer sentiment—positive, negative, or neutral—based on product reviews submitted in multiple languages. The core objective is to bridge the language gap in online feedback interpretation using advanced machine learning and natural language processing techniques. To achieve this, a hybrid approach leveraging both deep learning and traditional models is implemented—specifically, BERT (Bidirectional Encoder Representations from Transformers) for robust text embeddings and contextual understanding, and Random Forest for efficient classification.

https://doi.org/10.64823/ijter.2503009

In today's competitive job market, understanding hiring trends and job posting patterns is crucial for job seekers, recruiters, and analysts alike. Traditional job portals often lack insights into evolving skill demands and recruitment behavior [2]. This paper introduces a comprehensive analysis system for LinkedIn Job Postings and Hiring Trends, leveraging data visualization and business intelligence techniques [4]. Using a cleaned dataset of scraped LinkedIn job postings, the system identifies top in demand roles, frequently required skills, salary patterns, experience levels, and regional hiring dynamics [1]. The solution utilizes Power BI for interactive dashboards, enriched with DAX measures to uncover hidden patterns and relationships ([3]). Additionally, Python and Excel were used for data preprocessing, ensuring data quality and consistency. The resulting dashboards support multi-angle exploration—industry-wise hiring, job level analysis, function vs. experience mapping, and skill gaps— providing actionable insights for career planning and workforce development. This data-driven approach empowers stakeholders with a deeper understanding of the professional landscape and future hiring trajectories.