Film and Television Website Scores Authenticity Verification Based on the Emotional Analysis

Sentiment analysis is a method to identify and understand the emotion in the text through NLP and text analysis. In the era of information technology, there is often a certain error between the comments on the movie website and the actual score of the movie, and sentiment analysis technology provides a new way to solve this problem. In this paper, Python is used to obtain the movie review data from the Douban platform, and the model is constructed and trained by using naive Bayes and Bi-LSTM. According to the index, a better Bi-LSTM model is selected to classify the emotion of users’ movie reviews, and the classification results are scored according to the classification results, and compared with the real ratings on the website. According to the error of the final comparison results, the feasibility of this technology in the scoring direction of film reviews is being verified. By applying this technology, the phenomenon of film rating distortion in the information age can be prevented and the rights and interests of film and television works can be safeguarded.

Twitter Data Analysis Using Hadoop and‘R’and Emotional Analysis Using Optimized SVNN

Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints.A parallel computational environment pro-vided by Apache Hadoop can distribute and process the data over different desti-nation systems.In this paper,the Hadoop cluster with four nodes integrated with RHadoop,Flume,and Hive is created to analyze the tweets gathered from the Twitter stream.Twitter stream data is collected relevant to an event/topic like IPL-2015,cricket,Royal Challengers Bangalore,Kohli,Modi,from May 24 to 30,2016 using Flume.Hive is used as a data warehouse to store the streamed tweets.Twitter analytics like maximum number of tweets by users,the average number of followers,and maximum number of friends are obtained using Hive.The network graph is constructed with the user’s unique screen name and men-tions using‘R’.A timeline graph of individual users is generated using‘R’.Also,the proposed solution analyses the emotions of cricket fans by classifying their Twitter messages into appropriate emotional categories using the optimized sup-port vector neural network(OSVNN)classification model.To attain better classi-fication accuracy,the performance of SVNN is enhanced using a chimp optimization algorithm(ChOA).Extracting the users’emotions toward an event is beneficial for prediction,but when coupled with visualizations,it becomes more powerful.Bar-chart and wordcloud are generated to visualize the emotional analysis results.

Evolution and spatiotemporal analysis of earthquake public opinion based on social media data

As critical conduits for the dissemination of online public opinion,social media platforms offer a timely and effective means for managing emergencies during major disasters,such as earthquakes.This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province.By collecting,cleaning,and organizing post-earthquake Sina Weibo(short for Weibo)data,we employed the Latent Dirichlet Allocation(LDA)model to extract information pertinent to public opinion on these earthquakes.This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events.An emotion analysis,utilizing an emotion dictionary,categorized the emotional content of post-earthquake Weibo posts,facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes.The findings were visualized using Geographic Information System(GIS)techniques.The analysis revealed certain commonalities in online public opinion following both earthquakes.Notably,the peak of online engagement occurred within the first 24 hours post-earthquake,with a rapid decline observed between 24 to 48 hours thereafter.The variation in popularity of online public opinion was linked to aftershock occurrences.Adjusted for population factors,online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high.Initially dominated by feelings of“fear”and“surprise”,the public sentiment shifted towards a more positive outlook with the onset of rescue operations.However,distinctions in the online public response to each earthquake were also noted.Following the Yangbi earthquake,Yunnan Province reported the highest number of Weibo posts nationwide;in contrast,Qinghai Province ranked third post-Maduo earthquake,attributable to its smaller population size and extensive damage to communication infrastructure.This research offers a methodological approach for the analysis of online public opinion related to earthquakes,providing insights for the enhancement of post-disaster emergency management and public mental health support.

Construction of Psychological Adjustment Function Model of Music Education Based on Emotional Tendency Analysis

In the face offierce competition in the social environment,mental health problems gradually get the attention of the public,in order to achieve accurate mental health data analysis,the construction of music education is based on emotional tendency analysis of psychological adjustment function model.Design emotional tendency analysis of music education psychological adjustment function architecture,music teaching goal as psychological adjust-ment function architecture building orientation,music teaching content as a foundation for psychological adjust-ment function architecture and music teaching process as a psychological adjustment function architecture building,music teaching evaluation as the key of building key regulating function architecture,Establish a core literacy oriented evaluation system.Different evaluation methods were used to obtain the evaluation results.Four levels of psychological adjustment function model of music education are designed,and the psychological adjust-ment function of music education is put forward,thus completing the construction of psychological adjustment function model of music education.The experimental results show that the absolute value of the data acquisition error of the designed model is minimum,which is not more than 0.2.It is less affected by a bad coefficient and has good performance.It can quickly converge to the best state in the actual prediction process and has a strong con-vergence ability.

Multimodal Sentiment Analysis Based on a Cross-Modal Multihead Attention Mechanism

Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse data.Concate-nating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion method.This fusion method does not utilize the correlation information between modalities.To solve this problem,this paper proposes amodel based on amulti-head attention mechanism.First,after preprocessing the original data.Then,the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input sequence.Next,the input coding sequence is fed into the transformer model for further processing and learning.At the transformer layer,a cross-modal attention consisting of a pair of multi-head attention modules is employed to reflect the correlation between modalities.Finally,the processed results are input into the feedforward neural network to obtain the emotional output through the classification layer.Through the above processing flow,the model can capture semantic information and contextual relationships and achieve good results in various natural language processing tasks.Our model was tested on the CMU Multimodal Opinion Sentiment and Emotion Intensity(CMU-MOSEI)and Multimodal EmotionLines Dataset(MELD),achieving an accuracy of 82.04% and F1 parameters reached 80.59% on the former dataset.

A PERT-BiLSTM-Att Model for Online Public Opinion Text Sentiment Analysis

As an essential category of public event management and control,sentiment analysis of online public opinion text plays a vital role in public opinion early warning,network rumor management,and netizens’person-ality portraits under massive public opinion data.The traditional sentiment analysis model is not sensitive to the location information of words,it is difficult to solve the problem of polysemy,and the learning representation ability of long and short sentences is very different,which leads to the low accuracy of sentiment classification.This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text based on the pre-training model of the disordered language model,bidirectional long-term and short-term memory network and attention mechanism.The model first uses the PERT model pre-trained from the lexical location information of a large amount of corpus to process the text data and obtain the dynamic feature representation of the text.Then the semantic features are input into BiLSTM to learn context sequence information and enhance the model’s ability to represent long sequences.Finally,the attention mechanism is used to focus on the words that contribute more to the overall emotional tendency to make up for the lack of short text representation ability of the traditional model,and then the classification results are output through the fully connected network.The experimental results show that the classification accuracy of the model on NLPCC14 and weibo_senti_100k public data sets reach 88.56%and 97.05%,respectively,and the accuracy reaches 95.95%on the data set MDC22 composed of Meituan,Dianping and Ctrip comment.It proves that the model has a good effect on sentiment analysis of online public opinion texts on different platforms.The experimental results on different datasets verify the model’s effectiveness in applying sentiment analysis of texts.At the same time,the model has a strong generalization ability and can achieve good results for sentiment analysis of datasets in different fields.

A Study on the Acceptance of the English Translation of Romance of the Three Kingdoms by Overseas Readers Based on Python Data Analysis Technology

Paperless reading has become a prevalent trend among global readers,leading to the accumulation of vast amounts of reading data on numerous book websites.This offers new perspectives for studying translated works.This paper utilizes Python-based data processing technology to collect and analyze reader reviews of Romance of the Three Kingdoms on Amazon and Goodreads,presenting trends in review volume,word cloud maps,and readers’emotional attitudes in a quantitative manner.The findings indicate that overseas readers generally exhibit a positive emotional tendency towards Romance of the Three Kingdoms and recognize its cultural value.However,negative opinions do exist,focusing on aspects of the book’s quality,such as printing quality and proofreading.These results provide valuable insights for the foreign translation of canonical texts.

An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks

As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.

Analysis of the trend of global power sources based on comment emotion mining

In recent years,renewable energy technologies have been developed vigorously,and related supporting policies have been issued.The developmental trend of different energy sources directly affects the future developmental pattern of the energy and power industry.Energy trend research can be quantified through data statistics and model calculations;however,parameter settings and optimization are difficult,and the analysis results sometimes do not reflect objective reality.This paper proposes an energy and power information analysis method based on emotion mining.This method collects energy commentary news and literature reports from many authoritative media around the world and builds a convolutional neural network model and a text analysis model for topic classification and positive/negative emotion evaluation,which helps obtain text evaluation matrixes for all collected texts.Finally,a long-short-term memory model algorithm is employed to predict the future development prospects and market trends for various types of energy based on the analyzed emotions in different time spans.Experimental results indicate that energy trend analysis based on this method is consistent with the real scenario,has good applicability,and can provide a useful reference for the development of energy and power resources and of other industry areas as well.

A survey on deep learning for textual emotion analysis in social networks

Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.

Automatic Satisfaction Analysis in Call Centers Considering Global Features of Emotion and Duration

Analysis of customers＇ satisfaction provides a guarantee to improve the service quality in call centers.In this paper,a novel satisfaction recognition framework is introduced to analyze the customers＇ satisfaction.In natural conversations,the interaction between a customer and its agent take place more than once.One of the difficulties insatisfaction analysis at call centers is that not all conversation turns exhibit customer satisfaction or dissatisfaction. To solve this problem,an intelligent system is proposed that utilizes acoustic features to recognize customers＇ emotion and utilizes the global features of emotion and duration to analyze the satisfaction. Experiments on real-call data show that the proposed system offers a significantly higher accuracy in analyzing the satisfaction than the baseline system. The average F value is improved to 0. 701 from 0. 664.

Public Sentiment Analysis of Social Security Emergencies Based on Feature Fusion Model of BERT and TextLevelGCN

At present, the emotion classification method of Weibo public opinions based on graph neural network cannot solve the polysemy problem well, and the scale of global graph with fixed weight is too large. This paper proposes a feature fusion network model Bert-TextLevelGCN based on BERT pre-training and improved TextGCN. On the one hand, Bert is introduced to obtain the initial vector input of graph neural network containing rich semantic features. On the other hand, the global graph connection window of traditional TextGCN is reduced to the text level, and the message propagation mechanism of global sharing is applied. Finally, the output vector of BERT and TextLevelGCN is fused by interpolation update method, and a more robust mapping of positive and negative sentiment classification of public opinion text of “Tangshan Barbecue Restaurant beating people” is obtained. In the context of the national anti-gang campaign, it is of great significance to accurately and efficiently analyze the emotional characteristics of public opinion in sudden social violence events with bad social impact, which is of great significance to improve the government’s public opinion warning and response ability to public opinion in sudden social security events. .

Analysis of Emotions Using Multimodal Data: A Case Study

In this case study, we hypothesized that sympathetic nerve activity would be higher during conversation with PALRO robot, and that conversation would result in an increase in cerebral blood flow near the Broca’s area. The facial expressions of a human subject were recorded, and cerebral blood flow and heart rate variability were measured during interactions with the humanoid robot. These multimodal data were time-synchronized to quantitatively verify the change from the resting baseline by testing facial expression analysis, cerebral blood flow, and heart rate variability. In conclusion, this subject indicated that sympathetic nervous activity was dominant, suggesting that the subject may have enjoyed and been excited while talking to the robot (normalized High Frequency < normalized Low Frequency: 0.22 ± 0.16 < 0.78 ± 0.16). Cerebral blood flow values were higher during conversation and in the resting state after the experiment than in the resting state before the experiment. Talking increased cerebral blood flow in the frontal region. As the subject was left-handed, it was confirmed that the right side of the brain, where the Broca’s area is located, was particularly activated (Left < right: 0.15 ± 0.21 < 1.25 ± 0.17). In the sections where a “happy” facial emotion was recognized, the examiner-judged “happy” faces and the MTCNN “happy” results were also generally consistent.

Influence of Cultural Alienation on Happiness of Overseas Students: Mediating Role of Stress Relief and Regulating Role of Cultural Intelligence

When the global outbreak of new coronary pneumonia broke out in 2020,online public opinion events triggered by cultural differences among overseas students had come into the public view.To further explore the relationship between the cultural alienation of overseas students and their own happiness,this study takes visualization and analysis of positive,negative sentiment analysis of Weibo netizens’comment data in the“Xu Kexin Incident”as the starting point,on the basis of introducing cultural alienation,stress relief methods,and cultural intelligence,combining gender and social ability,social relations and other individual attributes,designed a questionnaire to investigate 502 overseas students,through the construction and analysis of the adjusted Cox risk ratio intermedi-ary model,comprehensive single factor interference and multi-factor cross-over comprehensive analysis.The results show that the cultural alienation of overseas students has a significant effect on their own well-being.The study concluded as follows:(1)Netizens hold polarized views on the three dimensions of overseas students’mask,safety,and culture;(2)Stress relief methods play an intermediary role between cultural alienation and the happiness of overseas students,among which Negative stress relief methods play a greater role;(3)The level of cultural intelligence regulates the intermediary process of stress relief methods.The higher the level of cultural intelligence,the stronger the regulatory effect.

EmotionMap:Visual Analysis of Video Emotional Content on a Map

Emotion plays a crucial role in gratifying users’needs during their experience of movies and TV series,and may be underutilized as a framework for exploring video content and analysis.In this paper,we present EmotionMap,a novel way of presenting emotion for daily users in 2D geography,fusing spatio-temporal information with emotional data.The interface is composed of novel visualization elements interconnected to facilitate video content exploration,understanding,and searching.EmotionMap allows understanding of the overall emotion at a glance while also giving a rapid understanding of the details.Firstly,we develop EmotionDisc which is an effective tool for collecting audiences’emotion based on emotion representation models.We collect audience and character emotional data,and then integrate the metaphor of a map to visualize video content and emotion in a hierarchical structure.EmotionMap combines sketch interaction,providing a natural approach for users’active exploration.The novelty and the effectiveness of EmotionMap have been demonstrated by the user study and experts’feedback.

Brief Overview of Intelligent Education

Intelligent Education uses Al technology as a means in the education ecology to promote the automation and intelligence of education and teaching.It reshapes the education ecology,adding Al things to the traditional education ecology that dominated by teachers and students.Although IE technology is widely used,there is little discussion about a comprehensive overview of IE.The goal and connotation of IE is discussed.Meanwhile,the emotional,ethical,Al technology as well as supervision and management perspectives in IE are discussed too.The core goal of IE is putted forward that is human-oriented and individualized development of students is.Finally,the education ecology with dual-teacher collaborative in intelligence education was proposed.

Document-Level Sentiment Analysis of Course Review Based on BG-Caps

With the development of the Internet in various fields,the combination of education and the Internet is close.Many users choose courses they are interested in to study on the MOOC platform and leave text reviews with emotional colors.However,the traditional word vector representation method extracts text information in a static way,which ignores text location information.The convolutional neural network cannot fully utilize the semantic features and correlation information,so the results of text sentiment analysis are inaccurate.To solve the above problems,this paper proposes a sentiment analysis method based on BGCaps MOOC text review.The ALBERT pretraining model was used to obtain the dynamic feature of the text.Combined with the BiGRU and capsule network model,the features were trained to obtain deep semantic features.We evaluated our mode on theMOOCreviewdataset.The results showthat the proposed method achieved effective improvement in accuracy.

Expression Analysis Based on Face Regions in Real-world Conditions

Facial emotion recognition is an essential and important aspect of the field of human-machine interaction.Past research on facial emotion recognition focuses on the laboratory environment.However,it faces many challenges in real-world conditions,i.e.,illumination changes,large pose variations and partial or full occlusions.Those challenges lead to different face areas with different degrees of sharpness and completeness.Inspired by this fact,we focus on the authenticity of predictions generated by different<emotion,region>pairs.For example,if only the mouth areas are available and the emotion classifier predicts happiness,then there is a question of how to judge the authenticity of predictions.This problem can be converted into the contribution of different face areas to different emotions.In this paper,we divide the whole face into six areas:nose areas,mouth areas,eyes areas,nose to mouth areas,nose to eyes areas and mouth to eyes areas.To obtain more convincing results,our experiments are conducted on three different databases:facial expression recognition+(FER+),real-world affective faces database(RAF-DB)and expression in-the-wild(ExpW)dataset.Through analysis of the classification accuracy,the confusion matrix and the class activation map(CAM),we can establish convincing results.To sum up,the contributions of this paper lie in two areas:1)We visualize concerned areas of human faces in emotion recognition;2)We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis.Our findings can be combined with findings in psychology to promote the understanding of emotional expressions.

Emotion-Aware Music Driven Movie Montage

In this paper, we present Emotion-Aware Music Driven Movie Montage, a novel paradigm for the challenging task of generating movie montages. Specifically, given a movie and a piece of music as the guidance, our method aims to generate a montage out of the movie that is emotionally consistent with the music. Unlike previous work such as video summarization, this task requires not only video content understanding, but also emotion analysis of both the input movie and music. To this end, we propose a two-stage framework, including a learning-based module for the prediction of emotion similarity and an optimization-based module for the selection and composition of candidate movie shots. The core of our method is to align and estimate emotional similarity between music clips and movie shots in a multi-modal latent space via contrastive learning. Subsequently, the montage generation is modeled as a joint optimization of emotion similarity and additional constraints such as scene-level story completeness and shot-level rhythm synchronization. We conduct both qualitative and quantitative evaluations to demonstrate that our method can generate emotionally consistent montages and outperforms alternative baselines.