Search

Now, the application of intelligent technologies such as machine learning and deep learning in natural language processing has achieved good results. This article studies the integration of emotion analysis in English module teaching of natural language processing. Vocabulary is a very important part in English teaching. Learning vocabulary well can improve students' reading ability. However, in the process of students' learning, vocabulary is the most basic and difficult to learn. Poor vocabulary learning and insufficient accumulation will restrict students' reading ability. Improving vocabulary teaching mode and learning methods can stimulate students' interest in learning and effectively improve their reading ability. In the third part of the article, the neural network language model and statistical model are used to analyze the key technologies of natural language processing, and then the Naive Bayes algorithm and support vector machine model algorithm are used to optimize the data. Finally, two classes are selected for comparative experiment, then, by integrating emotional teaching into students' classroom and analyzing students' interest, the conclusion is that integrating emotional teaching in teaching can effectively improve students' academic achievements, and at the same time, integrating emotional teaching in teaching can also stimulate students' enthusiasm for learning English and effectively change students' learning attitude. [ABSTRACT FROM AUTHOR]

Detecting emotions is very useful in many fields, from health-care to human-computer interaction. In this paper, we propose an iterative user-centered methodology for supporting the development of an emotion detection system based on low-cost sensors. Artificial Intelligence techniques have been adopted for emotion classification. Different kind of Machine Learning classifiers have been experimentally trained on the users' biometrics data, such as hearth rate, movement and audio. The system has been developed in two iterations and, at the end of each of them, the performance of classifiers (MLP, CNN, LSTM, Bidirectional-LSTM and Decision Tree) has been compared. After the experiment, the SAM questionnaire is proposed to evaluate the user's affective state when using the system. In the first experiment we gathered data from 47 participants, in the second one an improved version of the system has been trained and validated by 107 people. The emotional analysis conducted at the end of each iteration suggests that reducing the device invasiveness may affect the user perceptions and also improve the classification performance. [ABSTRACT FROM AUTHOR]

Objective: To explore the negative emotional characteristics of Weibo "Tree Hole" messages in order to provide ideas and data support for mental health promotion,suicide warning and intervention. Method:Using a "Tree Hole" artificial intelligence(AI) robot,351 825 messages were reviewed and words were divided by using a Jieba tool. The 500 most frequently used key words were calculated using a TF-IDF algorithm.Keywords co-occurrence network was constructed for social network analysis with important keywords by using Gephi software. Boson affective dictionary was used to judge for emotion characteristics of high frequency keywords. The amount of messages per hour was collected and the content characteristics of negative emotionswere analyzed. Results: Most messages were posted between 22:00 and 02:00, accounting for 36.3% of the total number of messages reviewed. The time period for posting the fewest number of messages was 05:00 to 07:00, accounting for 4.2% of the total number of messages reviewed. Among the 346 075 valid messages after data processing, 165 890 messages were positive emotional expression,among which 137 132 messages had an emotional score greater than 0, accounting for 39.7% of the total messages, 28 578 messages had an emotional score equal to 0(8.2%),and 180 185 messages were negative emotional expression,accounting for 52% of the total messages.Negative emotions were more expressed than positive emotions in each time period.The contents of the message included emotional expression, interpersonal relationship and social support, sleep and death.Among the people who express suicidal ideation, jumping off the building, sleeping pills, cutting wrists and burning charcoal were the most mentioned words related to suicidal ways.Conclusion: Negative emotions and related negative life events commonly are expressed by users of Weibo "tree hole". More attention should be paid to this phenomena, especially for users who express suicidal ideation,early identification and interventions are important. [ABSTRACT FROM AUTHOR]