Research Article Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions Manisha Bhende , 1 Anuradha Thakare , 2 Bhasker Pant , 3 Piyush Singhal , 4 Swati Shinde , 5 and Betty Nokobi Dugbakie 6 1 Marathwada Mitra Mandal’s Institute of Technology, Pune, India 2 Pimpri Chinchwad College of Engineering, Pune, India 3 Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India 4 Department of Mechanical Engineering, GLA University, Mathura, UP, India 5 Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India 6 Department of Chemical Engineering, Kwame Nkrumah University of Science and Technology, KNUST, Ghana Correspondence should be addressed to Betty Nokobi Dugbakie; bdnokobi@st.knust.edu.gh Received 21 March 2022; Accepted 21 April 2022; Published 30 July 2022 Academic Editor: Vijay Kumar Copyright © 2022 Manisha Bhende et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users’ emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks. Given this situation, a multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short- term memory network) is proposed to represent the microblog text word vectorization and the combination of MAML and BiLSTM is constructed. e model of BiLSTM realizes the classification of negative emotions on Weibo and updates the pa- rameters through machine gradient descent; the metalearner in MAML calculates the sum of the losses of multiple pieces of training, performs a second gradient descent, and updates the metalearner parameters. e updated metalearner can quickly iterate when faced with a new Weibo negative sentiment classification task. e experimental results show that compared with the prepopular model, on the Weibo negative sentiment dataset, the precision rate, recall rate, and F1 value are increased by 1.68 percentage points, 2.86 percentage points, and 2.27 percentage points, respectively. 1. Introduction Artificial intelligence is a subclass of data science that aims to develop smart computers capable of doing a wide range of tasks, which would ordinarily normally require human comprehension. ese intelligent devices can reflect on their prior experiences and information, evaluate their environ- ment, and take necessary measures. Human intelligence is defined as the ability to learn from previous experiences, adapt to new situations, handle abstract thinking, and transform one’s own surroundings using the knowledge gained. Human intelligence differs from artificial intelligence in that AI aims to create technologies that can mimic human behaviour and perform humane activities, whereas human intelligence aims to adapt to new situations by combining various cognitive states. Humans rely on their brains’ memories, computing capability, and ability to think, but AI-powered computers rely on facts and directions input into the system. e ability to learn and comprehend from numerous situations and past experiences is the foundation of human intellect. However, because AI cannot think, it lags in this field. e goal of the study is to perform sentiment analysis using MAML and BiLSTM for negative sentiment investigation among a smaller number of samples when Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 5075277, 8 pages https://doi.org/10.1155/2022/5075277