Features level sentiment mining in enterprise systems from informal text corpus using machine learning techniques
Ritanjali Panigrahi, Nishikant Bele, Prabin Kumar Panigrahi, Brij B. Gupta
Abstract
This study explores feature-level sentiment analysis of Hindi blog reviews in enterprise systems, a significant area in the Indian context yet understudied. By applying machine learning techniques like SVM across unigram, bigram, trigram, and n-gram models, and combining Lexicon-based methods with machine learning algorithms, we aim to enhance sentiment classification for better customer relationship management and product development. Contrasting with document-level approaches, our method focusing on bigrams achieves a test accuracy of 75%, offering a scalable model for enterprises to extract detailed customer insights from informal text, thereby aiding informed decision-making in a multicultural environment.