## 7 Conclusions and Future Work  
This study aimed to enhance the emotion analysis capabilities of the ML-Ask system by expanding its affect lexicon through the integration of multiple emotion dictionaries and implementing an automatic extraction mechanism for emotive expressions. Our approach involved the manual addition of emotive expressions from two external dictionaries and the automatic extraction of candidate expressions from a large-scale blog corpus.  
According to the results of the evaluation experiments, it can be concluded that the two available dictionaries of emotive expressions and the dictionary used in the ML-Ask database have a significant difference in the understanding of emotive expressions, and this study succeeded in improving the performance of the system, although by a small amount (2 percentage points).  
On the other hand, we extracted emotive sentences from a large-scale blog corpus and automatically extracted candidate emotive expressions from them to expand the affect lexicon of a lexicon-based affect analysis system.  
Specifically, to expand the affect lexicon, we first created a dataset with information about user emotions. We then compared the dataset for three types of segmentation and two types of weighting coefficients. The results showed that chunk processing was the most effective segmentation method, and OkapiBM25 was the most effective weighting coefficient.  
Although the automatic expansion of the affect lexicon was possible, the fully automatic method included expressions other than emotive expressions, which caused an increase in the incorrect extraction rate of emotions. Therefore, we further manually eliminated non-emotive expressions from the automatically expanded affect lexicon, thereby solving the problem of the incorrect extraction rate.  
Future research will explore the expansion of the affect lexicon by converting Kanji emotive expressions into Kana expressions and evaluating their usage in the Japanese YACIS corpus. We also plan to develop a fully automated lexicon expansion method using large language models, aiming to enhance scalability and accuracy. Moreover, integrating transformer-based models with the ML-Ask system will be investigated to leverage advanced natural language processing capabilities for more effective emotion analysis.  
By addressing these avenues, we aim to further advance the field of affect analysis, offering robust tools for sentiment analysis and improving human-computer interactions across diverse applications.  
Conceptualization, L.W., S.I. and M.P.; methodology, L.W. and S.I.; software, M.P., P.D., R.R. and F.M.; validation, L.W., S.I., M.P. and F.M.; formal analysis, L.W. and S.I.; investigation, L.W. and S.I.; resources, L.W., S.I., M.P., P.D., Y.U. and R.R.; data curation, L.W., S.I., M.P., P.D., Y.U. and R.R.; writing--original draft preparation, L.W. and S.I.; writing--review and editing, L.W. and M.P.; visualization, L.W. and S.I.; supervision, M.P. and F.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The emotion dataset for the Japanese language used to support the findings of this study is available upon reasonable request to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ CVS & Contextual Valence Shifters \\ MeCab & Yet Another Part-of-Speech and Morphological Analyzer \\ ML-Ask & eMotive eLement and Expression Analysis system \\ NLP & Natural Language Processing \\ OkapiBM25 & Term weighting for Okapi information retrieval system with Best Matching 25 \\ OOV & Out-of-Vocabulary \\ POS & Part-of-Speech \\ SNS & Social Networking Services \\ TF*IDF & Term weighting using Term Frequency with Inverse Document Frequency \\ TF-IDF & See TF*IDF \\ YACIS & Yet Another Corpus of Internet Sentences \\ \end{tabular}