Language Independent Sentence-Level Subjectivity Analysis with Feature Selection

Identifying and extracting subjective informa-tion from News, Blogs and other user gen-erated content has lot of applications. Most of the earlier work concentrated on English data. But, recently subjectivity related re-search at sentence-level in other languages has increased. In this paper, we achieve sentence-level subjectivity classification us-ing language independent feature weighing and selection methods which are consistent across languages. Experiments performed on 5 different languages including English and South Asian language Hindi show that En-tropy based category coverage difference cri-terion (ECCD) feature selection method with language independent feature weighing meth-ods outperforms other approaches for subjec-tive classification.

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail