# Empowering Large Language Models to Leverage Domain-Specific Knowledge in E-Learning
## Appendix A Appendix A
#### a.2 Case 2 Textbook Sentiment Analysis  
* Question: Please describe the sentiment analysis and sentiment dictionary and its application examples.
* Ground Answer: Sentiment Analysis, also known as Opinion Mining, refers to the subjective and sentiment assessment of the extraction source through the application of natural language processing technology and text analysis. Sentiment analysis-related thesaurus. Sentiment thesaurus is the most critical resource for sentiment analysis, almost all participation needs to rely on a thesaurus of many different emotion words for analysis, common sentiment thesaurus includes AFINN and Bing Liu opinion vocabulary, which also means that sentiment thesaurus plays an indispensable role in sentiment analysis. The following will be explained in detail for the abovementioned thesaurus.  
1. AFINN Dictionary of Emotions It is a rating ranging from \(-5\) (Very negative) to \(5\) (Very positive) of a list of English words by [PERSON] completed by manual marking between 2009 and 2011 ([PERSON], 2011), attached to the polarity (frontal/negative). This thesaurus is ANEW (Affective Norms for English Words) lexicon-based, but AFINN is more focused on microgrids. The language used on online platforms such as Zhi is suitable for analyzing the sentiment of online comments because its word list contains slang, obscene words, acronyms, and Internet buzzwords, which are commonly used after the development of the Internet. AFINN is currently available in two versions, the first being AFINN-96 with a total of 1468 unique words and phrases, and the second edition of AFINN-111 is the latest version with a total of 2477 words and phrases.
2. Bing Liu Thesaurus Proposed by [PERSON] and [PERSON] in 2004, it mainly distinguishes between positive and negative sentiments of single words, and the positive list includes agile (Agile), Enjoy (Enjoy), Lift (Improving). The negative list has anger (Anger), Reject (Refusing), Incapacitated (Unable). There are 2006 positive and 4783 negative words, tailored specifically for social media, as it takes into account changes in specific areas such as misspellings and slang.  
Application of sentiment analysis.  
Sentiment analysis can be applied to comments generated on blogs, micro-blogs, forums, chats, and social media to track sentiment related to political elections, finance, and consumer issues, which also reflects the validity and diversity of sentiment analysis research. For example, in the 2014 dispute between [PERSON] and [PERSON], through big data, the high correlation between \"youth\" and \"hot dance\" was excavated from popular and trending keywords on the Internet. Therefore, arrange a hot dance event, and then use the physical event to find out these groups from the Internet and combine them with the election, and become \"Go! Youth! Be Yourself\" series of activities. In this way, it is to find out the young people who are apathetic to politics and do not know about [PERSON] Use popular keywords from web searches as the subject for developing marketing campaigns and postings.  
Recent studies have shown that people express their emotions through online platforms, blogs, social media, etc., which also shows that sentiment analysis has a certain benefit for the development of enterprises. At present, there is an online tool that can analyze Chinese emotions, as mentioned earlier, called Lexalytics. It is very easy to operate, as shown in Figures 2-5, its online URL is [[https://www.lexalytics.com/demo](https://www.lexalytics.com/demo)]([https://www.lexalytics.com/demo](https://www.lexalytics.com/demo)), and interested people can try it out.