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Author: Mochamad Nizar Palefi Ma'ady
Mochamad - Nizar Palefi Ma'ady
Thesis Title: 時間探索二維可視化情感的的Twitter信息
Temporal Exploration in 2D Visualization of Emotions on Twitter Stream
Advisor: 楊傳凱
Chuan-Kai Yang
Committee: 賴源正
Yuan-Cheng Lai
Nai-Wei Lo
Degree: 碩士
Department: 管理學院 - 資訊管理系
Department of Information Management
Thesis Publication Year: 2016
Graduation Academic Year: 104
Language: 英文
Pages: 50
Keywords (in Chinese): 可視化微博表情時間數據
Keywords (in other languages): Temporal Data
Reference times: Clicks: 383Downloads: 24
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任何由所述需求驅動的,並希望客戶組織已經明白從任何媒體客戶行為。 Twitter正在成為世界上最流行的社交媒體之一。如今,微博的很多第三方應用已經讓客戶自由地隨時隨地分享他們的意見。然而,這是很難理解客戶行為的變化,因為鳴叫被張貼在不穩定的可能性。因此,可視化客戶的情感行為的時間模式的Twitter可以發揮在決策至關重要的作用。現有的數據可視化工具,如D3.js,激勵我們去開發,並探討在二維的可視化的Twitter數據的時間維度。



Any organization that is driven by the needs and wants of customers has to understand customer behavior from any media. Twitter is becoming one of the most popular social media in the world. Nowadays, many third-party applications of Twitter have been making customers freely to share their opinions anywhere and anytime. However, it is difficult to understand the change of customer behavior, because the tweet is posted in erratic likelihood. Therefore, visualizing the time pattern of customer emotion behavior on Twitter can play a crucial role in decision-making. Available data visualizing tools, such as D3.js, motivate us to develop and to explore time dimension of Twitter data in 2D visualization.

Our proposed system is composed of three main parts: text analysis, visualization presentation, and interaction. The input data was collected from Twitter in real-time and stored directly in our tweet database. We separated acquiring data into two steps. First, we retrieved all tweets from several certain countries by using geographical bounding-box and stored in our tweet database, then we set up a keyword filter, “iPhone”, to get the dataset that is ready to be processed. Twitter data used in this research was collected from the entire U.S.A., Japan, Indonesia, and Taiwan, since knowing different location may result different customer behavior. Prior to present visualization, data preprocessing was performed in order to determine attributes, which words are considered as positive or negative opinion. Naïve Bayes text classifier was then developed using a set of the chosen words containing opinion features.

Finally, our proposed system used the result of sentiment analysis to be visualized in day / hour and week / day heat map, interactive stream graph, and context focus via brushing visualization. The results show that our proposed system can explore and analyze the temporal pattern of customer emotions behavior.

ABSTRACT ACKNOWLEDGEMENT LIST OF FIGURES LIST OF TABLES Chapter 1. Introduction Motivation Contribution 1.3. Thesis Organization Chapter 2. Related Work 2.1. Text Visual Basic Framework 2.2. Knowledge Representation 2.3. Micro-Blog Visualization Chapter 3. Proposed System 3.1. System Overview 3.2. The Architecture of the Proposed System 3.3. Text Analysis 3.3.1. Data Acquisition 3.3.2. Data Preprocessing 3.3.3. Sentiment Analysis 3.4. Visualization Presentation 3.4.1. Two-Dimensional Heat Map 3.4.2. Context Focus via Brushing 3.4.3. Interactive Stacked Area Chart 3.5. Interaction Chapter 4. Experimental Result 4.1. Experiments 4.2. Results 4.3. Limitation and Discussion Chapter 5. Conclusion and Future Work 5.1. Conclusion 5.2. Future Work References

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