Emotional analysis refers to the use of natural language processing technology system detection、Extraction、Analysis of the attitude in the text、Position、perspective and view，It is a semantic analysis、Artificial Intelligence、Cognitive science and other fields that have attracted much attention from research directions。Entering the 21st century，Social media booming，Public opinion on various social media platforms、Exchange，A large amount of data rich in emotional information promotes the birth and development of emotional analysis。Emotional analysis is usually equivalent to emotional classification，That is to classify the text based on the language characteristics of the text。Specifically，Emotional classification is the emotional pole of the text (positive、negative) and intensity evaluation，You can also perform a multi -dimensional analysis of various specific emotions，Ru anger、Happy、Sadness, etc.。Valley analysis tasks in a broad sense，Including subjective classification、Emotional Classification、Anthology with entity extraction、Summary、Junk review detection, etc.。Emotional analysis is mainly performed at three levels: document level、Sentence -level and aspects。Document -level emotional analysis aims to determine whether the entire document expresses is positive or negative emotion。Sentence -level emotional analysis is more detailed，Classification of emotions expressed in each sentence in the document。Based on emotional goals rather than language units (document、Paragraph、sentences, etc.) Emotional analysis，Called emotional analysis based on aspect -based or characteristic，Emotional goals can be a characteristic of entity or certain aspects、Event、Topics, etc.。

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bet365 best casino games The traditional methods of emotional analysis are mainly two types: machine learning method and emotional dictionary method。Machine learning method is divided into supervision and learning method、No Supervision Learning Law、Semi -Supervance Learning Law。The difference between them is，Is it based on a large number of labeled data training models。In the Supervision Learning Law，The learning process is based on the marked training data set，Try to map the input to the output，Learning input and output relationship function，The emotional classification used to infer the new data set。where，Common emotional classifiers are simple Bayesia、Support vector machine、Maximum entropy, etc.。but，These classifiers can only be trained by labeled data sets，And this data set usually requires experienced markers to perform artificial marks，Not easy to get，Not easy to get。Unsupervised learning method can solve this problem，It uses inspiration information such as seed words，Finding a potential structure in an unsocolored data set through a cluster，No manual participation。Commonly used clustering methods are layered cluster、Divide clustering, etc.。other，You can also adopt a semi -supervision learning method，Use a small amount of data with labeled data and a large number of unbex data training classifiers。

Analysis of Emotional Dictionary，Also known as Emotional Dictionary，refers to the process of extracting the non -structural features of the text based on the emotional dictionary。Emotional Dictionary is a watch containing emotional words and phrases，These words are encoded as positive、negative or neutral，and the corresponding strength level。The basic principle of this method is，First of all, the target text is divided、Words，Then match the words in the dictionary，Then calculate the emotional score of the text based on the number and weight of the emotional words in the text。Emotional Dictionary can be manually marked、Construction based on dictionary and corpus Bet365 app download -based methods。Establish an emotional vocabulary through manual labeling，Usually timely effort。Current，This method is mainly used to check the accuracy of the automatic labeling method。When building an emotional dictionary based on dictionary，It will first use the existing dictionary resources，such as WordNet，Extract a set of emotional words，Retrieve their synonyms and antonyms in the dictionary，Add to this set of words，Then iterates，until there is no more new emotional words appear，After artificial inspection，This set of words can be extended into an emotional dictionary。When building an emotional dictionary based on the corpus，It mainly uses a set of emotional words marked to identify the new emotional words in the corpus，According to the principle of common presentation，Build an emotional word。Current，Many emotional dictionaries have been widely used，such as SentiwordNet、MPQA Subjectivity Lexicon、ntusd, etc.。Some dictionaries are universal，while some are targeted at specific fields。In specific studies，Select the right emotional dictionary according to the research field，Improper use will cause text features that cannot be recognized in certain specific fields，Reduce the accuracy of the analysis results。

In recent years，Sentences based on deep learning、Document representation、Knowledge portrait and other technologies，attracted attention in the field of emotional analysis。For example，Word embedded technology based on neural network，Can be able to characterize the vocabulary in the vector space，Codes for semantic and syntax special signs at the same time，so as to effectively make up for traditional methods (such as words bags、TFIDF and other word frequency signs) deficiency。Another example，Migration learning technology can use models that have pre -training trains existing fields，Fine -tuning the parameters related to the target task，Extend it to a new dataset and a new field，so as to save a lot of manual labeling time and energy，is one bet365 Play online games of the effective methods for cross -domain emotional classification。

　Specific application 

For the past 20 years，The popularity of social media has greatly promoted the development of participation culture。Groups and public emotions learned from it，It is an important reference basis for various social decisions。Consumers before purchasing products and services，I hope to know the views of other consumers on products and services; while companies also hope to understand the opinions of consumers or the public on products and services。Public views on government policies and measures in social media，Policy decision makers at all levels can determine the public's opinion，Society to deal with fast changes、Economic and political situation。Public opinion has become more and more core issues in the field of humanities and social sciences，Researchers in the era of big data also need to break through traditional，Applying natural language processing methods to effectively promote discipline progress。Current，Emotional analysis has begun to see in many areas of humanities and social sciences。

In the field of economic and financial，The text used for emotional analysis mainly comes from the company's annual report、Company press conference、News report、In -depth comment、Analysis Report、Social media posts, etc.。The emotional analysis system can use these different sources of information，Find the data information of related listed companies，Make emotional analysis and summarize ingredients，to predict the company's stock trend。other，Emotional analysis can also be used to predict the company's future performance。Already found out，The increase in risk emotions in the annual report is significantly related to the decrease in future income，The abnormal positive tone in the release of the company's revenue press release is also not good in future income。

In the field of management，The text used for emotional analysis mainly comes from the user's online comment。A large number of research on the relationship bet365 best casino games between consumers' online comments and product sales，It is recommended that enterprises use effective network data monitoring and analysis technology detection of emotion in online evaluation，Especially negative emotions，to avoid affecting product sales。Emotional analysis as a big data analysis technology，It is widely used in products and service management in many industries。In the tourism industry，The online comment of the hotel plays a key role in hotel accommodation decisions of potential customers，The same is true in the field of catering and aviation。In a medical institution，Emotional analysis is commonly used to study patients against diseases、Medical Service、Drugs and other opinions and feelings。In the entertainment industry，Emotional analysis mainly pays attention to the evaluation of movies，Including actors、Director、Music, etc.，Understanding the overall trend of comments can effectively predict the box office performance of the movie。

In the political field，Data used for emotional analysis include social media posts、Interviews and lectures of politicians、News reports, etc.。Emotional analysis is widely used to understand the public's view of a political issue or political figures，to predict the direction of real world political events，Forecast candidates' popularity in elections，Then predict election results。More importantly，As social media become a popular channel for ordinary people to express opinions，Monitor social media，Timely discover the emotions and concerns of the public，It can be the basis for the government's insight and policies to formulate policies。

　Future Outlook 

In the past 20 years，Emotional analysis in the field of humanities and social sciences has continued to increase，Except the above main application areas，Its figure also appeared in literary works for appreciation、Analysis of social relations、Academic writing and more in more and more studies。The fusion of the two has broad development space，Is a issue worth Bet365 app download systematic exploring，The efforts in the following two aspects are important。

First，Natural language processing technology needs to be continuously innovated and developed，Provides strong support for big data text analysis。The accuracy of existing emotional classification methods is not high enough，The algorithm cannot fully handle emotional words and complex language phenomena other than simply analyzed it，If the finger of the finger and the common finger dissolution、Semantic disadvantage and other problems。Emotional problems are often complicated and diverse，Because people seem to be able to express positive and negative emotions in infinitely many ways。For example，Iron is a common daily expression，and its complexity and vagueness make the irony recognition extremely challenging。Another example，The expression of factuality may also contain emotion，The current emotional analysis method is usually targeted at subjective statements，So I ignore this objective statement。other，At present, most emotional analysis technology development is mainly for English data。Different from different languages，Technology that can verify English data does not necessarily apply to other language data。Therefore，Building a multi -language library for emotional analysis is important。

2，Emotional research is a cross -disciplinary problem，In the future, cooperation between researchers in multiple fields (especially computer science and technology and humanities and social sciences) can be carried out.。This can not only promote the innovation and development of emotional analysis technology，It will also make significant contributions to the research in different fields and even the entire society。On the one hand，Researchers in the field of humanities and social sciences currently，Especially linguistic researchers，I have realized the potential of social media analysis and big data emotional analysis。Emotions are an important aspect of natural language and semantics，Develop bet365 best casino games semantic theory from the perspective of natural language treatment，Can effectively supplement and promote traditional linguistics research。On the other hand，The development of natural language processing technology also requires the perspective of humanities and social sciences (such as psychologists、The interpretation of the sociologist on the concept of emotion，Linguist's theory of language structure)，Provides necessary supplements for algorithm -based emotional and semantic analysis。

　　(This article is the key project of the National Social Science Fund "Research on the Research on Chinese Political Discourse International Discipline based on text" (18AY006) phased achievement) 

(Author Unit: Foreign Linguistics and Application Linguistics Research Center of Xi'an University of Foreign Languages)