期刊名称：International Journal of Advances in Engineering and Management
电子版ISSN：2395-5252
出版年度：2021
卷号：3
期号：5
页码：885-889
DOI：10.35629/5252-0305752762
语种：English
出版社：IJAEM JOURNAL
摘要：Textual emotion detection has a high impact on business, society, politics or education with applications such as, detecting depression or personality traits, suicide prevention or identifying cases of cyber-bulling. Given this context, the objective of our research is to contribute to the improvement of emotion recognition task through an automatic technique focused on reducing both the time and cost needed to develop emotion corpora. Our proposal is to exploit a bootstrapping approach based on intentional learning for automatic annotations with two main steps: An initial similarity-based categorization where a set of seed sentences is created and extended by distributional semantic similarity (word vectors or word embedding). Train a supervised classifier on the initially categorized set. The technique proposed allows us an efficient annotation of a large amount of emotion data with standards of reliability according to the evaluation results. The social networking sites dispense their data conveniently and freely on the web. This availability of data entices the interest of young researchers to plunge them in the field of sentiment analysis. People express their emotions and perspectives on the social media discussion forums. The business organizations employ researchers to investigate the unrevealed facts about their products and services. Spontaneous and automatic determination of sentiments from reviews is the main concern of multinational organizations the machine learning techniques have improved accuracy of sentiment analysis and expedite automatic evaluation of data these days. This work attempted to utilize four machine learning techniques for the task of sentiment analysis.
