隨著Web2.0時代來臨及有關技術、應用不斷發展，為人類表達自身想法來全新空間。研究者們透過網路這個傳播媒介，探究人類在不同網路社群互動時的語言使用。人類的表達涵蓋了各種語言現象，且當中具有細微差別。這些文本資訊，為採用特徵學習的電腦系統獲取訊息中的涵義帶來挑戰。儘管有些發言的意思可以從字面上直接理解，但更為有趣的是，是這些文字背後有時候藏有其他意涵。尤其人們在網路上的互動還必須考慮個人與社會層次。在本篇研究中，蒐集了人們在網路社群上一系列的互動，採取不同過去特徵學習使用的方法論，擷取出人們在互動中真正要呈現的意思。再者，本研究還將展示各種資料蒐集法、特徵學習設計及模型建構，這些將有助完整人機互動的價值，且成功反映出人們在網路世界的行為，例如話語背後的諷刺意味，又或者是發言者的身心健康情況等。

The advent of the Web 2.0 and further developments it brought opened a new space for humans to express their thoughts. This communication medium has provided a way for researchers to investigate the use of language on social networks across different groups and applications. Human expressions are conveyed with all sorts of linguistic phenomena and nuances that present various challenges for computational systems that aim to gain insights from textual information through various feature representations. Moreover these utterances can convey value in and explicit way but more interestingly, they can also carry implicit value. The nature of these human interactions on social networks is also embedded with considerations on both an individual and a societal level. In this work, we study different collections of human expressions on social media, present a thorough evaluation of the possible methodological alternatives to process them, and introduce a variety of applications showcasing the embedded insight which can be extracted from this data. Specifically, we introduce a variety of methods for data collection, feature design and model construction that can integrate value from human computer interactions and can successfully reflect human behaviors on domains ranging from sarcasm to mental health and others.

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