{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-empirical-evaluation-of-word-embedding","title":"An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks","arxiv_id":null,"date":"2021-04-06","proceeding":"IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) 2021 4","authors":["Ritika Nandi","Geetha Maiya","Priya Kamath","Shashank Shekhar"],"abstract":"It is a clearly established fact that good categorization results are heavily dependent on representation techniques. Text representation is a necessity that must be fulfilled before working on any text analysis task since it creates a baseline which even advanced machine learning models fail to compensate. This paper aims to comprehensively analyze and quantitatively evaluate the various models to represent text in order to perform Subjectivity Analysis. We implement a diverse array of models on the Cornell Subjectivity Dataset. It is worth noting that the\r\nBERT Language Model gives much better results than any other model but is significantly computationally expensive than the\r\nother approaches. We obtained state-of-the-art results on the subjectivity task by fine-tuning the BERT Language Model. This\r\ncan open up a lot of new avenues and potentially lead to a specialized model inspired by BERT dedicated to subjectivity analysis.","url_abs":"https://ieeexplore.ieee.org/document/9392437","url_pdf":"https://ieeexplore.ieee.org/document/9392437","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-empirical-evaluation-of-word-embedding","repo_url":"https://github.com/Ritika2001/Word-Embedding-Models-for-Subjectivity-Analysis","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"subjectivity-analysis","task_name":"Subjectivity Analysis"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/subjectivity-analysis-on-subj","task":"Subjectivity Analysis","dataset":"SUBJ","model":"BERT-Base + CLR + LSTM","rank_in_archive_order":2,"of":19,"metrics":{"Accuracy":"97.30"},"uses_additional_data":false},{"leaderboard":"/sota/subjectivity-analysis-on-subj","task":"Subjectivity Analysis","dataset":"SUBJ","model":"BERT-Base + LSTM","rank_in_archive_order":4,"of":19,"metrics":{"Accuracy":"96.60"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}