Deep Learning-Based Text Emotion Analysis for Legal Anomie

Abstract

Text emotion analysis is an effective way for analyzing the emotion of the subjects’ anomie behaviors. This paper proposes a text emotion analysis framework (called BCDF) based on word embedding and splicing. Bi-direction Convolutional Word Embedding Classification Framework (BCDF) can express the word vector in the text and embed the part of speech tagging information as a feature of sentence representation. In addition, an emotional parallel learning mechanism is proposed, which uses the temporal information of the parallel structure calculated by Bi-LSTM to update the storage information through the gating mechanism. The convolutional layer can better extract certain components of sentences (such as adjectives, adverbs, nouns, etc.), which play a more significant role in the expression of emotion. To take advantage of convolution, a Convolutional Long Short-Term Memory (ConvLSTM) network is designed to further improve the classification results. Experimental results show that compared with traditional LSTM model, the proposed text emotion analysis model has increased 3.3 and 10.9% F1 score on psychological and news text datasets, respectively. The proposed CBDM model based on Bi-LSTM and ConvLSTM has great value in practical applications of anomie behavior analysis.

Acknowledgment of patient in sense behaviors using bidirectional ConvLSTM

“…This table compared the result based on the existing ConvLSTM14 and proposed BiConvLSTM on two reputed datasets and one real-life Covid-19 patient dataset. ConvLSTM14,23 method gives 88.06% in the UCI dataset, 85.64% in the HMDB51 dataset, and 89.09% in the Covid-19 patient database as comparing our proposed method on all datasets give more accurate and precise result, 91.23% on UCI dataset, 92.34% on HMDB51 dataset and 92.54% on Covid-19 patient dataset. Overall proposed method performance has 3.17% on the UCI dataset and 26.70%…”

“…Our architecture expands the architecture described by interpreting image data in both directions using a Convolutional LSTM (ConvLSTM). 20 We hypothesize that accessing both previous and future information from an existing state enables the BiConvLSTM [21][22][23][24][25][26] to comprehend the meaning of the existing input, resulting in improved categorizing on non-homogenous and voluminous datasets. It demonstrates our networks' effectiveness in conducting experimentation on three standard datasets.…”

Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things

“…The key to LSTM lies in its internal memory units, which include forget gates, input gates, and output gates, enabling it to effectively capture long-term dependencies in sequential data (She, 2022;Liu et al, 2022;Khan et al, 2022). However, LSTM is a unidirectional network, meaning it cannot infer the current time-step information based on context.…”

Legal anomie as a factor in reducing effectiveness of law-making activities

“…Гофман, аномия -это «состояние ценностно-нормативного вакуума, характерного для переходных и кризисных периодов и состояний в развитии обществ, когда старые нормы и ценности перестают действовать, а новые еще не установились (Hoffman, 2008:205). Как указывает She B., аномическое поведение относится к неупорядоченному социальному явлению, вызванному аномическим состоянием действующего законодательства в процессе трансформации новой и старой системы страны (She, 2022). Его исследование позволяет понять нарушения закона и психосоциальное несоответствие его требованиям (Zanatta et al, 2019).…”