在這多元化的社會，生活中的大小事務充斥每個人的每一天，要在這一堆資訊當中找尋欲尋找的相關議題，如何可以精確搜尋呢？本研究透過人工智慧(Artificial Intelligence)的方法去分類多種社會議題，並檢測出情緒，使得可以快速檢測大量的資料屬於何種議題及情緒。情感分析在這幾年越來越受重視，應用的範圍廣泛，可用於商業或教育等。本研究也是假新聞的前期研究，透過探究文本處理及分類，讓檢測假新聞的研究可以更迅速地著手進行。
本研究使用的資料集來源為International Workshop on Semantic Evaluation，議題與情感為同一文本資料，議題分為Atheism, Climate Change is a Real Concern,Donald Trump, Feminist Movement, Hillary Clinton 以及Legalization of Abortion，這些議題都是與世界上受大眾關注的，情感的部分分為正向、中立以及負向。本研究使用深度學習演算法(Deep Learning Algorithm)中的具有序列性的長短期記憶(Long Short Term Memory, LSTM)方法，訓練前將所有的資料集做自然語言處理(Natural Language Processing, NLP)，把不必要訓練的元素全部移除，像是：空白、標點符號、無意義的字詞等，利用Part of speech 進行詞形還原(Lemmatization)，將文本資料斷詞(Tokenize)，並分配給每個詞一個獨自的號碼，讓詞具有獨特性，以利訓練。訓練使用LSTM 演算法，序列性的架構可以使得前面的訊息得以傳遞到後面，不會產生梯度消失或梯度爆炸的情況，訓練好的模型輸出六個議題模型及一個情感模型。每個模型皆經由八個可能選擇模型中選出效能最好的。
本研究在訓練結果輸出時，會透過變換參數，同時間訓練出多個模型，最終自動選擇最優的作為輸出模型，如此可以省下人工變換參數的時間成本。本研究訓練出六個議題模型及一個情感模型，這些模型可以有助在尋找相關議題時快速的檢測，一個文本資料可以同時擁有多個議題在本研究中也可達到如此的檢測成果，同時可以獲得文本的情感檢測。本研究於應用面上可使用在企業的產品分析、顧客服務或顧客體驗等多面向領域。

In this diversified society, the daily affairs of life are full of everyone's every day. In this pile of information, we need to find related issues to be searched. How can we search precisely? This study uses the method of Artificial Intelligence to classify various social issues and detect sentiments, so that it can quickly detect which issues and sentiments a large amount of data belongs to. Sentiment analysis has gained more and more attention in recent years, and its application is widely used in business and education. This research is also a preliminary study of fake news. By exploring text processing and classification, research on detecting false news can be carried out more quickly.
The source of the dataset used in this study was International Workshop on Semantic Evaluation. The issues and sentiments were the same text, and the issues were Atheism, Climate Change is a Real Concern, Donald Trump, Feminist Movement, Hillary Clinton, and Legalization of Abortion. These issues are related to the public's attention, and the sentiment part is divided into positive, neutral and negative. This study uses the Long Short Term Memory (LSTM) method in the Deep Learning Algorithm. All data sets are processed in Natural Language Processing (NLP) before training, remove all unnecessary training elements, such as: blank, punctuation, meaningless words, etc., using Part of speech for Lemmatization. The text data is tokenized and assigned to each word a unique number, giving the word uniqueness for training. Training uses the LSTM algorithm. The sequential architecture allows the previous message to be passed to the back without gradient disappearance or gradient explosion. The trained model outputs six issue models and a sentiment model. Each model is selected to be the most effective of the eight possible selection models.
In the study, when the training results are output, the parameters are transformed, and multiple models are trained at the same time. Finally, the optimal output model is automatically selected, which can save the time cost of manual transformation parameters. This study trains six issue models and one sentiment model. These models can help to quickly detect related issues. A text material can have multiple topics at the same time. In this study, such test results can also be achieved, and sentiment detection of text can be obtained. This research can be used in many areas such as product analysis, customer service, and customer experience on the application side.

[1] Keyes, Ralph. The post-truth era: Dishonesty and deception in contemporary life. Macmillan, 2004.
[2] iSideWith(https://www.isidewith.com/).
[3] Tractica(https://www.tractica.com/).
[4] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436.
[5] Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9.8 (1997): 1735-1780.
[6] Wikipedia contributors. "Text mining." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 26 Jun. 2019. Web. 13 Jul. 2019.
[7] Manning, Christopher D., Christopher D. Manning, and Hinrich Schütze. Foundations of statistical natural language processing. MIT press, 1999.
[8] Feldman, Ronen, and James Sanger. The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge university press, 2007.
[9] Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited, 2016.
[10] Sebastiani, Fabrizio. "Machine learning in automated text categorization." ACM computing surveys (CSUR) 34.1 (2002): 1-47.
[11] Couldry, Nick. Media, society, world: Social theory and digital media practice. Polity, 2012.
[12] statista: Percentage of U.S. population with a social media profile from 2008 to 2019. (https://www.statista.com/).
[13] DIGITAL INFORMATION WORLD: The Most Popular Social Media Platforms of 2019. (https://www.digitalinformationworld.com/).
[14] Semeval-2016 Task 6: Detecting Stance in Tweets. Saif M. Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. Proceedings of the International Workshop on Semantic Evaluation (SemEval ’16). June 2016. San Diego, California.
[15] Tansley, Stewart, and Kristin M. Tolle. The fourth paradigm: data-intensive scientific discovery. Ed. Anthony JG Hey. Vol. 1. Redmond, WA: Microsoft research, 2009.
[16] Sicular, Svetlana. "Gartner's big data definition consists of three parts, not to be confused with three" v" s." Gartner, Inc 27 (2013).
[17] Wikipedia contributors. "Big data." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 12 Jul. 2019. Web. 14 Jul. 2019.
[18] Bird, Steven, Ewan Klein, and Edward Loper. Natural language processing with Python: analyzing text with the natural language toolkit. " O'Reilly Media, Inc.", 2009.
[19] NLTK 3.4.4 documentation. (https://www.nltk.org/)
[20] Alias-i. 2008. LingPipe 4.1.0. http://alias-i.com/lingpipe (accessed October 1, 2008)
[21] C. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. Bethard, & D. McClosky. "The Stanford CoreNLP natural language processing toolkit." Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations. 2014.
[22] Stanford CoreNLP. (https://stanfordnlp.github.io/CoreNLP/)
[23] Rabiner, Lawrence R. "A tutorial on hidden Markov models and selected applications in speech recognition." Proceedings of the IEEE 77.2 (1989): 257-286.
[24] Michael I. Jordan. “Attractor dynamics and parallelism in a connectionist sequential machine.” Proceedings of the Cogitive Science Conference, pp 531-546, 1986.
[25] Saeed Hassanpour, Naofumi Tomita, Timothy DeLise, Benjamin Crosier & Lisa A. Marsch. “Identifying substance use risk based on deep neural networks and Instagram social media data.” Proceedings of the Neuropsychopharmacology, 44 (2019), pp. 487-494.
[26] Nguyen, Duc T., and Jason J. Jung. "Real-time event detection on social data stream." Mobile Networks and Applications 20.4 (2015): 475-486.
[27] I. Augenstein, T. Rocktäschel, A. Vlachos, & K. Bontcheva. "Stance detection with bidirectional conditional encoding." arXiv preprint arXiv:1606.05464 (2016).
[28] J. Peng, S. Detchon, K. K. R. Choo, & H. Ashman. "Astroturfing detection in social media: a binary n‐gram–based approach." Concurrency and Computation: Practice and Experience 29.17 (2017): e4013.
[29] H. Zhong, H. Li, A. C. Squicciarini, S. M. Rajtmajer, C. Griffin, D. J. Miller, & C. Caragea. "Content-Driven Detection of Cyberbullying on the Instagram Social Network." IJCAI. 2016.
[30] Severyn, Aliaksei, and Alessandro Moschitti. "Unitn: Training deep convolutional neural network for twitter sentiment classification." Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015). 2015.
[31] Glorot, Xavier, Antoine Bordes, and Yoshua Bengio. "Domain adaptation for large-scale sentiment classification: A deep learning approach." Proceedings of the 28th international conference on machine learning (ICML-11), 2011.
[32] Dovdon, Enkhzol, and José Saias. "ej-sa-2017 at semeval-2017 task 4: Experiments for target oriented sentiment analysis in twitter." ACL, 2017.
[33] Morency, Louis-Philippe, Rada Mihalcea, and Payal Doshi. "Towards multimodal sentiment analysis: Harvesting opinions from the web." Proceedings of the 13th international conference on multimodal interfaces. ACM, 2011.
[34] Brill, Eric. "A simple rule-based part of speech tagger." Proceedings of the third conference on Applied natural language processing. Association for Computational Linguistics, 1992.
[35] M. Hagen, M. Potthast, M. Büchner, & B. Stein. "Webis: An ensemble for twitter sentiment detection." Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), 2015.